interactive 3d modelling in outdoor augmented reality worlds
Research Thesis for the Degree of Doctor of Philosophy
By Wayne Piekarski
Bachelor of Engineering in Computer Systems Engineering (Hons), University of South Australia
Dr. Bruce Thomas
Adelaide, South Australia
Wearable Computer Lab
School of Computer and Information Science
Division of Information Technology, Engineering, and the Environment
The University of South Australia
Table of Contents
Chapter 1 - Introduction............................................................................................................ 1
1.1 Problem statement............................................................................................................ 4
1.2 Thesis statement............................................................................................................... 6
1.3 Contributions................................................................................................................... 7
1.4 Dissertation structure....................................................................................................... 8
Chapter 2 - Background........................................................................................................... 10
2.1 Definition of augmented reality..................................................................................... 10
2.2 Applications................................................................................................................... 12
2.3 See through display technology..................................................................................... 20
2.4 3D tracking technology.................................................................................................. 25
2.5 Desktop direct manipulation techniques........................................................................ 38
2.6 Virtual reality interaction techniques............................................................................. 39
2.7 Physical world capture techniques................................................................................. 51
2.8 CAD modelling.............................................................................................................. 55
2.9 Outdoor augmented reality wearable computers........................................................... 60
2.10 Summary...................................................................................................................... 64
Chapter 3 - Augmented reality working planes....................................................................... 65
3.1 Distance estimation cues................................................................................................ 66
3.2 AR working planes definition........................................................................................ 70
3.3 Coordinate systems........................................................................................................ 72
3.4 Plane creation................................................................................................................. 76
3.5 Object manipulation....................................................................................................... 80
3.6 Vertex placement........................................................................................................... 82
3.7 Accurate alignment with objects.................................................................................... 82
3.8 Alignment accuracy using HMDs.................................................................................. 83
3.9 Summary........................................................................................................................ 89
Chapter 4 - Construction at a distance..................................................................................... 90
4.1 Technique features......................................................................................................... 90
4.2 Direct object placement techniques............................................................................... 95
4.3 Body-relative plane techniques...................................................................................... 97
4.4 AR working planes techniques.................................................................................... 103
4.5 Integrated example...................................................................................................... 110
4.6 Operational performance.............................................................................................. 111
4.7 Summary...................................................................................................................... 112
Chapter 5 - User interface...................................................................................................... 114
5.1 Design rationale........................................................................................................... 114
5.2 Cursor operations......................................................................................................... 117
5.3 Command entry........................................................................................................... 123
5.4 Display interface.......................................................................................................... 127
5.5 Tinmith-Metro modelling application.......................................................................... 132
5.6 Informal user evaluations............................................................................................. 138
5.7 Future work.................................................................................................................. 142
5.8 Summary...................................................................................................................... 143
Chapter 6 - Software architecture.......................................................................................... 144
6.1 Design overview.......................................................................................................... 145
6.2 Previous work.............................................................................................................. 147
6.3 Object design............................................................................................................... 152
6.4 Object storage.............................................................................................................. 156
6.5 Implementation internals.............................................................................................. 161
6.6 Sensors and events....................................................................................................... 168
6.7 Rendering..................................................................................................................... 172
6.8 Demonstrations............................................................................................................ 174
6.9 Summary...................................................................................................................... 181
Chapter 7 - Hardware............................................................................................................ 182
7.1 Hardware inventory..................................................................................................... 182
7.2 Tinmith-Endeavour backpack...................................................................................... 184
7.3 Glove input device....................................................................................................... 193
7.4 Summary...................................................................................................................... 200
Chapter 8 - Conclusion.......................................................................................................... 201
8.1 Augmented reality working planes.............................................................................. 201
8.2 Construction at a distance............................................................................................ 202
8.3 User interfaces............................................................................................................. 203
8.4 Vision-based hand tracking......................................................................................... 203
8.5 Modelling applications................................................................................................. 204
8.6 Software architecture................................................................................................... 204
8.7 Mobile hardware.......................................................................................................... 204
8.8 Future work.................................................................................................................. 205
8.9 Final remarks................................................................................................................ 207
Appendix A - Evolutions....................................................................................................... 208
A.1 Map-in-the-Hat (1998)................................................................................................ 208
A.2 Tinmith-2 prototype (1998)......................................................................................... 210
A.3 Tinmith-3 prototype (1999)......................................................................................... 213
A.4 Tinmith-4 and ARQuake prototype (1999)................................................................ 217
A.5 Tinmith-evo5 prototype one (2001)............................................................................ 218
A.6 Tinmith-VR prototype (2001)..................................................................................... 221
A.7 Tinmith-evo5 prototype two with Tinmith-Endeavour (2002)................................... 222
A.8 ARQuake prototype two with Tinmith-Endeavour (2002)........................................ 224
A.9 Summary..................................................................................................................... 226
Appendix B - Attachments.................................................................................................... 227
B.1 CD-ROM.................................................................................................................... 227
B.2 Internet........................................................................................................................ 228
List of Figures
Figure 1‑1.... Example Sony Glasstron HMD with video camera and head tracker.................. 2
Figure 1‑2.... Example of outdoor augmented reality with computer-generated furniture........ 3
Figure 1‑3.... Schematic of augmented reality implementation using a HMD........................... 3
Figure 2‑1.... Example of Milgram and Kishinoís reality-virtuality continuum....................... 11
Figure 2‑2.... The first head mounted display, developed by Ivan Sutherland in 1968........... 13
Figure 2‑3.... External and AR immersive views of a laser printer maintenance application... 13
Figure 2‑4.... Virtual information windows overlaid onto the physical world......................... 14
Figure 2‑5.... Worker using an AR system to assist with wire looming in aircraft assembly... 15
Figure 2‑6.... AR with overlaid ultrasound data guiding doctors during needle biopsies....... 15
Figure 2‑7.... Studierstube AR environment, with hand-held tablets and widgets................. 16
Figure 2‑8.... Marker held in the hand provides a tangible interface for viewing 3D objects.. 16
Figure 2‑9.... Actors captured as 3D models from multiple cameras overlaid onto a marker.. 17
Figure 2‑10.. Touring Machine system overlays AR information in outdoor environments.... 18
Figure 2‑11.. BARS system used to reduce the detail of AR overlays presented to the user. 18
Figure 2‑12.. Context Compass provides navigational instructions via AR overlays.............. 19
Figure 2‑13.. Schematic of optical overlay-based augmented reality...................................... 22
Figure 2‑14.. Optically combined AR captured with a camera from inside the HMD............ 23
Figure 2‑15.. Schematic of video overlay-based augmented reality........................................ 23
Figure 2‑16.. Example video overlay AR image, captured directly from software................. 25
Figure 2‑17.. Precision Navigation TCM2 and InterSense InertiaCube2 tracking devices..... 37
Figure 2‑18.. CDS system with pull down menus and creation of vertices to extrude solids. 43
Figure 2‑19.. CHIMP system with hand-held widgets, object selection, and manipulation... 43
Figure 2‑20.. Immersive and external views of the SmartScene 3D modelling environment.. 44
Figure 2‑21.. Partial UniSA campus model captured using manual measuring techniques..... 53
Figure 2‑22.. Screen capture of Autodeskís AutoCAD editing a sample 3D model............... 56
Figure 2‑23.. Venn diagrams demonstrating Boolean set operations on 2D areas A and B.... 58
Figure 2‑24.. CSG operations expressed as Boolean sets of 3D objects.................................. 58
Figure 2‑25.. Plane equation divides the universe into two half spaces, inside and outside... 59
Figure 2‑26.. Finite cylinder defined by intersecting an infinite cylinder with two planes..... 59
Figure 2‑27.. Box defined using six plane equations and CSG intersection operator............. 60
Figure 2‑28.. Wearable input devices suitable for use in outdoor environments..................... 62
Figure 3‑1.... 3D objects are projected onto a plane near the eye to form a 2D image............ 66
Figure 3‑2.... Normalised effectiveness of various depth perception cues over distance........ 69
Figure 3‑3.... Graph of the size in pixels of a 1m object on a HMD plane 1m from the eye... 70
Figure 3‑4.... Coordinate systems used for the placement of objects at or near a human........ 72
Figure 3‑5.... World-relative AR working planes remain fixed during user movement........... 73
Figure 3‑6.... Location-relative AR working planes remain at the same bearing from the user and maintain a constant distance from the user......................................................................................... 74
Figure 3‑7.... Body-relative AR working planes remain at a fixed orientation and distance to the hips and are not modified by motion of the head......................................................................... 75
Figure 3‑8.... Head-relative AR working planes remain attached to the head during all movement, maintaining the same orientation and distance to the head.......................................................... 76
Figure 3‑9.... AR working plane created along the head viewing direction of the user.......... 77
Figure 3‑10.. AR working plane created at a fixed offset and with surface normal matching the view direction of the user...................................................................................................................... 77
Figure 3‑11.. AR working plane created at intersection of cursor with object, and normal matching the userís view direction.............................................................................................................. 78
Figure 3‑12.. AR working plane created relative to an objectís surface.................................. 78
Figure 3‑13.. AR working plane created at a nominated object based on the surface normal of another reference object.................................................................................................................. 79
Figure 3‑14.. Manipulation of an object along an AR working plane surface......................... 79
Figure 3‑15.. Depth translation from the user moving a head-relative AR working plane...... 79
Figure 3‑16.. AR working plane attached to the head can move objects with user motion.... 79
Figure 3‑17.. Scaling of an object along an AR working plane with origin and two points.... 80
Figure 3‑18.. Rotation of an object along AR working plane with origin and two points...... 80
Figure 3‑19.. Vertices are created by projecting the 2D cursor against an AR working plane 81
Figure 3‑20.. AR working plane attached to the head can create vertices near the user......... 81
Figure 3‑21.. Example fishing spot marked using various shore-based landmarks.................. 83
Figure 3‑22.. Example of range lights in use to indicate location-relative to a transit bearing 83
Figure 3‑23.. Sony Glasstron HMD measured parameters and size of individual pixels......... 84
Figure 3‑24.. Distant landmarks must be a minimum size to be visible on a HMD................. 85
Figure 3‑25.. Dotted lines indicate the angle required to separate the two markerís outlines. 85
Figure 3‑26.. Similar triangles used to calculate final positioning error function..................... 86
Figure 3‑27.. Rearrangement and simplification of final positioning error equation............... 87
Figure 3‑28.. Derivation of alignment equation when marker B is at an infinite distance....... 87
Figure 3‑29.. 3D surface plot with marker distances achieving alignment accuracy of 2 cm.. 88
Figure 3‑30.. 3D surface plot with marker distances achieving alignment accuracy of 50 cm 89
Figure 4‑1.... AR view of virtual table placed in alignment with physical world table........... 95
Figure 4‑2.... VR view of bread crumbs markers defining a flat concave perimeter............... 96
Figure 4‑3.... AR view showing registration of perimeter to a physical world grassy patch... 96
Figure 4‑4.... Example bread crumbs model extruded to form an unbounded solid shape..... 97
Figure 4‑5.... Infinite carving planes used to create a convex shape from an infinite solid..... 98
Figure 4‑6.... Orientation invariant planes generated using multiple marker positions.......... 100
Figure 4‑7.... Relationship between GPS accuracy and required distance to achieve better than 1 degree of orientation error for two different GPS types.................................................. 100
Figure 4‑8.... Orientation invariant planes formed using first specified angle and markers... 101
Figure 4‑9.... Box objects can be moved into a building surface to carve out windows....... 102
Figure 4‑10.. Convex trapezoid and concave T, L, and O-shaped objects............................ 103
Figure 4‑11.. Concave object created using CSG difference of two convex boxes.............. 103
Figure 4‑12.. AR working planes are used to specify vertices and are projected along the surface normal for carving the objectís roof................................................................................................ 104
Figure 4‑13.. AR view of infinite planes building created with sloped roof......................... 105
Figure 4‑14.. AR view of infinite planes building being interactively carved with a roof.... 105
Figure 4‑15.. VR view of building with sloped roof, showing overall geometry.................. 105
Figure 4‑16.. Frames of automobile carving, with markers placed at each corner................. 106
Figure 4‑17.. Final resulting automobile shown overlaid in AR view, and in a VR view..... 106
Figure 4‑18.. Schematic illustrating the painting of a window onto a wall surface............... 107
Figure 4‑19.. Examples showing surface of revolution points for tree and cylinder objects. 107
Figure 4‑20.. AR view of surface of revolution tree with markers on AR working plane..... 108
Figure 4‑21.. VR view of final surface of revolution tree as a solid shape............................ 108
Figure 4‑22.. Outdoor stack of pallets approximating a box, before modelling.................... 109
Figure 4‑23.. VR view of final model with captured geometry and mapped textures.......... 109
Figure 4‑24.. AR view of final abstract model, including street furniture items................... 111
Figure 4‑25.. VR view of final abstract model, including street furniture items................... 111
Figure 5‑1.... Each finger maps to a displayed menu option, the user selects one by pressing the appropriate finger against the thumb.............................................................................................. 117
Figure 5‑2.... Immersive AR view, showing gloves and fiducial markers, with overlaid modelling cursor for selection, manipulation, and creation................................................................................ 118
Figure 5‑3.... Translation operation applied to a virtual tree with the userís hands............... 120
Figure 5‑4.... Scale operation applied to a virtual tree with the userís hands......................... 121
Figure 5‑5.... Rotate operation applied to a virtual tree with the userís hands...................... 122
Figure 5‑6.... Original WordStar application, showing menu toolbar at bottom of screen.... 124
Figure 5‑7.... Immersive AR overlay display components explained..................................... 128
Figure 5‑8.... Top down aerial view of VR environment in heading up and north up mode. 129
Figure 5‑9.... Orbital view centred on the user with a VR style display................................ 129
Figure 5‑10.. User, view plane, 3D world objects, and distant projection texture map......... 131
Figure 5‑11.. Immersive view of Tinmith-Metro with 3D cursor objects appearing to be floating over the incoming video image....................................................................................................... 132
Figure 5‑12.. External view of Tinmith-Metro with userís body and 3D environment......... 132
Figure 5‑13.. Options available from the top-level of Tinmith-Metroís command menu...... 133
Figure 5‑14.. Menu hierarchy of available options for the Tinmith-Metro application.......... 134
Figure 5‑15.. Original horizontal menu design in immersive AR view.................................. 139
Figure 5‑16.. View of the user interface being tested in a VR immersive environment........ 140
Figure 6‑1.... Overall architecture showing sensors being processed using libraries and application components, and then rendered to the userís HMD..................................................................... 146
Figure 6‑2.... Layers of libraries forming categories of objects available to process data...... 153
Figure 6‑3.... Data values flow into a node for processing, producing output values............ 153
Figure 6‑4.... Expanded view of data flow model showing stages of processing................. 154
Figure 6‑5.... Network distribution is implemented transparently using automatically generated serialisation callbacks and a network transmission interface............................................................... 156
Figure 6‑6.... Examples demonstrating usage of the hierarchical object store....................... 158
Figure 6‑7.... Simplified layout of composite Position class, showing nested objects........... 160
Figure 6‑8.... Edited extract from the is-300.h orientation tracker C++ definition file......... 162
Figure 6‑9.... Complete XML serialisation of the IS-300 orientation tracker object............. 163
Figure 6‑10.. C++ code demonstrating setup and execution of callbacks............................. 164
Figure 6‑11.. Mathematical operations possible between absolute and relative objects........ 169
Figure 6‑12.. Distorted view of Tinmith-Metro showing improperly placed avatar objects when the resolution of OpenGLís internal values is exceeded............................................................. 170
Figure 6‑13.. User is represented in the 3D world with a hierarchical avatar model............. 176
Figure 6‑14.. Indoor tracking system with backpack, head and shoulder mounted video cameras, GPS antenna, and fiducial markers on the hands, walls and ceiling.............................................. 177
Figure 6‑15.. Partial layout of manipulation menu, with internal commands and next path. 179
Figure 7‑1.... Data bus interconnect diagram of components used for mobile outdoor AR.. 184
Figure 7‑2.... Rear view of previous backpack design, showing tangled mess of cabling..... 185
Figure 7‑3.... Front and rear views of the Tinmith-Endeavour backpack in use outdoors..... 185
Figure 7‑4.... Design of polycarbonate housing with hinged laptop holder and internals..... 187
Figure 7‑5.... Backpack shown in desktop configuration, permitting normal use outside..... 187
Figure 7‑6.... Interior of backpack housing, showing internal components and cabling........ 188
Figure 7‑7.... Power supply breakout box, with +5V, +9V, and +12V at each connector.... 189
Figure 7‑8.... Power bus interconnect diagram of components used for mobile outdoor AR 190
Figure 7‑9.... Two USB ports, glove connector, and cables mounted onto shoulder straps.. 190
Figure 7‑10.. Design of brackets to attach Sony Glasstron and Firefly camera to a helmet.. 191
Figure 7‑11.. Designs of various versions of the glove and attached fiducial markers.......... 194
Figure 7‑12.. Circuit schematic for the low power glove controller....................................... 195
Figure 7‑13.. Example use of the fiducial marker tracking used for a 3D cursor.................. 196
Figure 7‑14.. ARToolKit default camera_para.dat file, with error x=2.5, y=48.0................. 198
Figure 7‑15.. Graphical depictions showing original and new orthogonal camera model..... 199
Figure A‑1... Phoenix-II wearable computer with batteries, cables, and belt mounting........ 209
Figure A‑2... Map-in-the-Hat prototype inside ruck sack, with antenna, cables, and HMD. 209
Figure A‑3... Screen shots of Map-in-the-Hat indicating a waypoint on the display............ 209
Figure A‑4... Tinmith-2 hiking frame with some equipment attached................................... 211
Figure A‑5... 2D top down map overlaid on physical world (using offline AR overlay)...... 212
Figure A‑6... 2D top down map overlay with current location relative to nearby buildings. 212
Figure A‑7... 3D wireframe overlay of building, with small extension made to the left....... 213
Figure A‑8... Tinmith-3 backpack with HMD, head tracker, and forearm keyboard............ 214
Figure A‑9... Software interconnect diagram for Tinmith-2 to Tinmith-4 prototypes........... 215
Figure A‑10. View of ModSAF tool with simulated entities and a wearable user................ 215
Figure A‑11. Wearable user in outdoor environment generates DIS packets........................ 216
Figure A‑12. MetaVR view of avatar for wearable user and helicopter for ModSAF entity 216
Figure A‑13. DIS entities overlaid in yellow on HMD with a top down view..................... 216
Figure A‑14. Visualising artificial CAD building extensions overlaid on physical world..... 217
Figure A‑15. ARQuake implemented using optical AR with virtual monsters shown.......... 218
Figure A‑16. Mock up demonstrating how a modelling system could be used outdoors...... 219
Figure A‑17. Side view of original Tinmith-evo5 backpack, with cabling problems............. 220
Figure A‑18. Close up view of messy cable bundles and miscellaneous input devices......... 220
Figure A‑19. Screen shots of the first Tinmith-Metro release in use outdoors...................... 221
Figure A‑20 VR immersive system used to control the Tinmith-Metro user interface.......... 222
Figure A‑21. Side and front views of the Tinmith-Endeavour backpack in use outdoors.... 223
Figure A‑22. Screen capture of the latest Tinmith-Metro release in use outdoors................. 224
Figure A‑23. USB mouse embedded into a childrenís bubble blowing toy.......................... 225
Figure A‑24. Monsters overlaid on the physical world with video overlay ARQuake.......... 225
List of Tables
Abbreviations and Definitions
1394†††††††††††† IEEE Standard 1394, also referred to as Firewire or i.Link [IEEE95]
2D††††††††††††††† Two Dimensional in XY
3D††††††††††††††† Three Dimensional in XYZ
AAAD†††††††† Action at a distance, first defined by Mine [MINE95a]
ACRC††††††††† Advanced Computing Research Centre at UniSA
AGD66††††††† Australian Geodetic Datum 1966 [ICSM00]
AGD84††††††† Australian Geodetic Datum 1984 [ICSM00]
AR††††††††††††††† Augmented Reality
CIS†††††††††††††† School of Computer and Information Science at UniSA
COTS†††††††††† Commercial Off The Shelf
CRT††††††††††††† Cathode Ray Tube (technology used in television and monitor displays)
CSG†††††††††††† Constructive Solid Geometry
DGPS†††††††††† Differential GPS
DIS†††††††††††††† IEEE Standard 1278, the Distributed Interactive Simulation protocol [IEEE93]
DOF†††††††††††† Degrees of Freedom ([X, Y, Z] for position, [q, f, j] for orientation)
3DOF†††††††††† Three degrees of freedom, only three measurements, such as only orientation or position tracker information
6DOF†††††††††† Six degrees of freedom, information about orientation and position, a complete tracking solution
DSTO†††††††††† Defence Science Technology Organisation, Adelaide, South Australia
ECEF†††††††††† Earth-Centred Earth-Fixed Cartesian coordinates, in metres [ICSM00]
Evo†††††††††††††† Evolution or version number
FOV†††††††††††† Field of View, the angle of the userís view that a head mounted display or camera can cover
GLONASS† Russian Federation, Global Navigation Satellite System (Global'naya Navigatsionnaya Sputnikovaya Sistema in Russian)
GPS††††††††††††† US Department of Defence, Global Positioning System
HMD††††††††††† Head Mounted Display
HUD††††††††††† Heads Up Display
ITD†††††††††††††† Information Technology Division (located at DSTO Salisbury, Adelaide)
IPC†††††††††††††† Inter-Process Communication
LCD†††††††††††† Liquid Crystal Display
LOD†††††††††††† Land Operations Division (located at DSTO Salisbury, Adelaide)
LLH†††††††††††† Latitude Longitude Height spherical polar coordinates [ICSM00]
LSAP†††††††††† Land Situational Awareness Picture System
MR†††††††††††††† Mixed Reality
NFS††††††††††††† Sun Microsystemsí Network File System [SAND85]
OEM††††††††††† Original Equipment Manufacturer
RPC††††††††††††† Sun Microsystemsí Remote Procedure Calls
RTK†††††††††††† Real-Time Kinematic (centimetre grade GPS technology)
SERF††††††††††† Synthetic Environment Research Facility (located at DSTO Salisbury, Adelaide)
SES††††††††††††† Scientific and Engineering Services (located at DSTO Salisbury, Adelaide)
STL††††††††††††† Standard Template Library (for the C++ language)
SQL††††††††††††† Server Query Language
Tinmith†††††††† This Is Not Map In The Hat (named for historical purposes)
UniSA††††††††† University of South Australia
USB†††††††††††† Universal Serial Bus
UTM†††††††††††† Universal Transverse Mercator grid coordinates, in metres [ICSM00]
VE††††††††††††††† Virtual Environment
VR††††††††††††††† Virtual Reality
WCL††††††††††† Wearable Computer Lab at the University of South Australia
WIM†††††††††††† Worlds in Miniature [STOA95]
WIMP††††††††† Windows, Icons, Menus, and Pointer
WGS84††††††† World Geodetic System 1984 [ICSM00]
X††††††††††††††††† The X Window System
XML††††††††††† Extensible Mark-up Language
This dissertation presents interaction techniques for 3D modelling of large structures in outdoor augmented reality environments. Augmented reality is the process of registering projected computer-generated images over a userís view of the physical world. With the use of a mobile computer, augmented reality can also be experienced in an outdoor environment. Working in a mobile outdoor environment introduces new challenges not previously encountered indoors, requiring the development of new user interfaces to interact with the computer. Current AR systems only support limited interactions and so the complexity of applications that can be developed is also limited.
This dissertation describes a number of novel contributions that improve the state of the art in augmented reality technology. Firstly, the augmented reality working planes technique gives the user the ability to create and edit objects at large distances using line of sight and projection techniques. This technique overcomes limitations in a humanís ability to perceive depth, and requires simple input devices that are available on mobile computers. A number of techniques that leverage AR working planes are developed, collectively termed construction at a distance: street furniture, bread crumbs, infinite planes, projection carving, projection colouring, surface of revolution, and texture map capture. These techniques can be used to create and capture the geometry of outdoor shapes using a mobile AR system with real-time verification and iterative refinement. To provide an interface for these techniques, a novel AR user interface with cursors and menus was developed. This user interface is based around a pair of pinch gloves for command input, and the use of a custom developed vision tracking system for use in a mobile environment. To develop applications implementing these contributions, a new software architecture was designed to provide a suitable abstraction to make development easier. This architecture is based on an object-oriented data flow approach, uses a special file system notation object repository, and supports distributed objects. The software requires a platform to execute on, and so a custom wearable hardware platform was developed. The hardware is based around a backpack that contains all the equipment required, and uses a novel flexible design that supports simple reconfiguration.
Based on these contributions, a number of modelling applications were developed to demonstrate the usefulness of these techniques. These modelling applications allow users to walk around freely outside, and use proprioception and interactions with the hands to control the task. Construction at a distance allows the user to model objects such as buildings, trees, automobiles, and ground features with minimal effort in real-time, and at any scale and distance beyond the userís reach. These applications have been demonstrated in the field to verify that the techniques can perform as claimed in the dissertation.
I declare that this thesis does not incorporate without acknowledgment any material previously submitted for a degree or diploma in any university and that to the best of knowledge it does not contain any materials previously published or written by another person except where due reference is made in the text.
Adelaide, February 2004
Dr Bruce Thomas Ė Thesis Supervisor
Adelaide, February 2004
A dissertation does not just appear out of nowhere, and although it is supposed to be a contribution by one person for a PhD, there are still a lot of people who have helped me out over the years. I have been fortunate enough to have had the support of so many people and without it this would not have been possible. While most people did not help directly on the project, every one of them contributed in some way towards helping me to get where I am today, even things like just being a friend and going out and having fun. Others were responsible for giving me a push in the right direction in life, and for everyone listed here I am eternally grateful for their help.
Firstly there is the Wearable Computer Lab crew. Although I initially started in the lab alone, over the years we have grown to being a major lab at the university, and I have worked with a number of people - Ben Close, Hannah Slay, Aaron Toney, Ben Avery, Ross Smith, Peter Hutterer, Matthias Bauer, Pierre Malbezin, Barrie Mulley, Matthew Schultz, Scott Sheridan, Leonard Teo, John Squires, John Donoghue, Phil DeBondi, and Dan Makovec. Many of us have spent many countless late nights working on projects to meet deadlines and the spirit of our team is truly awesome.
In the CIS department I also have a number of friends apart from those in the WCL who I go to lunch with almost every day, and I also enjoy spending time with them outside of work hours - Grant Wigley, Greg Warner, Malcolm Bowes, Stewart Itzstein, and Toby Richer. I would especially like to thank Grant for his friendship during the PhD program, as we both started at the same time and have helped each other out considerably. On behalf of the lunch crew I would also like to express gratitude to the Brahma Lodge Hotel, for serving up copious amounts of the finest cheesy potatoes on the planet to fuel our lunch time cravings.
In the CIS department I have worked with three heads of school over the years and each have supported me in my endeavours - Andy Koronios, Brenton Dansie, and David Kearney. With their financial and leadership contributions I have been given the resources I need to complete this PhD and also help to develop the WCL into what it is today.
Staff members in the CIS department have also been extremely helpful. The numerous general staff performed the many tasks that are required to keep the department running each day, and were always very happy to help out when required. They helped to organise my teaching trips, the ordering of equipment, and dealing with finances. Greg Warner and Malcolm Bowes ran the department servers and allowed us to perform unorthodox computer networking in the lab. Frank Fursenko and Tony Sobey also discussed with me C++ and graphics programming on a number of occasions. Millist Vincent assisted by proofreading parts of the thesis and provided technical comments.
The DSTO Land Operations Division with Victor Demczuk and Franco Principe were initially responsible for donating various wearable computers and resources to the department. This was used to start the initial projects in the WCL and would not have existed without them. The Information Technology Division at DSTO has also been instrumental in further research work we have done, giving us a number of large grants for equipment as well as the design of our new backpack. I would especially like to thank Rudi Vernik for his vision in granting this funding to the WCL and it has helped to make our research first class work. The SES group with John Wilson, Paul Zalkauskas, Chris Weckert, and Barry Crook, led by Peter Evdokiou, have to be the finest and most professional group of engineers I have ever met. Together they manufactured the Tinmith-Endeavour backpack design which dazzles people at conferences all over the world.
When I was still growing up in 1993, I had the fortune of using an Internet dial up service run by Mark Newton. He introduced me to the one true operating system Unix, and all its amazing features. I used his machine to learn how to use Unix and the fledgling Internet, and through this I met a number of other people in the area. The late Chris Wood took the time to teach me how to write Makefiles, use Emacs (the one true editor), and how to correct bugs in my first X11 application. These contributions helped to steer my professional development toward Unix development which would come into much use at university. The Linux community has also supported me by inviting me to speak at most of their conferences, allowing me to teach audiences about my research work and to learn from others. The developers who donated their time to the development of drivers for the hardware I use have been most helpful, without these this project would not have been possible.
When I was just starting at university I was fortunate enough to meet Steve Baxter. He had just started up an Internet company called SE Net along with his wife Emily Baxter and friend Chris Foote, and asked me to work as the first employee of the company helping with sales and support. As the company grew I was given the role of Manager of R&D, and given the task of developing the systems that controlled the entire company. Steve trusted in my abilities the future of his entire company. This enabled me to gain a wealth of experience in leadership and design that would never be given to most 18 year olds, and for this I am very grateful. Together we built a company that led the field in a number of areas. As part of the team at SE Net, I have many friends - Matt Altus, David Kuzmak, Richard and Genni Kaye, Robert Gulley, Andrew Xenides, Rebecca Razzano, Lindsay Whitbread, Megan Hehir, Mark Mills, and Scott Smith. Unfortunately SE Net has now been absorbed by the new owners of the company, and so the fine traditions and spirit from SE Net are no longer around, but exist in our memories forever.
During my travels overseas I have met a number of great people who have been friendly and helpful. I would like to especially thank the Computer Science department at the University of North Carolina at Chapel Hill for allowing me to spend three months performing research there. Sally Stearns and Ray Thomas were kind enough to let me stay at their house for the first few days while I found a place to stay. At UNC I made many friends such as Mark Harris, Scott Cooper, Ken Hoff, Benjamin Lok, Samir Nayak, David Marshburn, Andrew Nashel, and Stefan Sain, as well as the Pi Lambda Phi fraternity and Drink Club crew.
There are also a number of other people who do not work with me but have been friends for many years and I would also like to thank them for their support - David Pridgeon, Trent Greenland, Ghassan Abi Mosleh, Rebecca Brereton, Donna Harvey, Krishianthi Karunarathna, Josie Brenko, Tam Nguyen, Sarah Bolderoff, and Derek Munneke.
The most instrumental person for this dissertation was my supervisor Dr Bruce Thomas. I have worked with Bruce for the last five years first as a final year undergraduate project student, and then as a PhD student. Bruce had the insight to move into wearable computers and augmented reality in the very early days and formed the Wearable Computer Lab we have today. Bruce has helped me to gain an international profile in the AR and wearables community, by generously giving me the funding to travel to numerous places all over the world and to meet other researchers (and obtain a frequent flyer gold card). This international development has strengthened my PhD with experience from a wide range of people and further motivated my research with fresh ideas. I would also like to thank Bruce for the countless hours I have spent with him discussing research, proof reading papers and this dissertation, talking about life in general, and having a beer as friends when travelling.
To achieve a PhD at the University of South Australia, the dissertation must be reviewed by two external experts in the area of research. I was fortunate enough to have Professor Steven Feiner from Columbia University and Associate Professor Mark Billinghurst from HIT Lab New Zealand as reviewers, who are both outstanding researchers in the international community. Each of them carefully read through the hundreds of pages of this dissertation and gave me excellent feedback which has been integrated into this final version. I would like to thank both of them for their time and dedication to reviewing my work and helping to improve it.
Most importantly of all, I would like to thank my mum Kris, dad Spishek, brother Arron, and my grandparents for supporting me for the last 25 years. My family also helped me build some of the early backpack prototypes in the garage, making an important contribution to the project. It is through their encouragement and care that I have made it through all the steps to reach this point in life, and I couldnít have done it without them. When my dad bought me a Commodore 64 when I was a little boy, who would have thought I would have ended up here today? My family has always taken care of me and I love them all very much.
In summary, I would like to thank everyone for putting up with me for the last couple of years. I believe that this dissertation has made a real contribution to the field of computer science and I hope that everyone that reads this dissertation finds it useful in their work. It has been a fun journey so far, and I look forward to catching up with everyone and having lots of fun and good times, because that is the most important thing of all.
Now it is time to catch up on some sleep and have a holiday! (Well, not really - there is still plenty of other work to do now)
Adelaide, February 2004
ďAnother noteworthy characteristic of this manual is that it doesn't always tell the truth ... The author feels that this technique of deliberate lying will actually make it easier for you to learn the ideas. Once you understand the simple but false rule, it will not be hard to supplement that rule with its exceptions.Ē
Donald Knuth, from the preface to The TeXbook
ďIf you do choose to use a computer, beware the temptation it offers to let manuscript preparation displace composition. They are two separate activities, best done separately. Hyphenation and exposition are at war with one another. Pagination vies with content. The mind busy fretting over point size has no time left over to consider clarity. If you need a break from the ardors of composition, try the time-honored ones like poking the fire or baking bread. They smell good, and they don't give you any illusion that your paper is making progress while you indulge in them.Ē
Mary-Claire van Leunen
"We all agree that your theory is crazy, but is it crazy enough?"
Niels Bohr (1885-1962)
Augmented reality (AR) is the registration of projected computer-generated images over a userís view of the physical world. With this extra information presented to the user, the physical world can be enhanced or augmented beyond the userís normal experience. The addition of information that is spatially located relative to the user can help to improve their understanding of it. In 1965, Sutherland described his vision for the Ultimate Display [SUTH65], with the goal of developing systems that can generate artificial stimulus and give a human the impression that the experience is actually real. Sutherland designed and built the first optical head mounted display (HMD) that was used to project computer-generated imagery over the physical world. This was the first example of an augmented reality display [SUTH68]. Virtual reality (VR) was developed later using opaque display technology to immerse the user into a fully synthetic environment. One of the first integrated environments was by Fisher et al., combining tracking of the head for VR with the use of tracked gloves as an input device [FISH86].
Augmented reality and virtual reality share common features in that they present computer-generated images for a user to experience, with information anchored to 3D locations relative to the userís display, their body, or the world [FEIN93b]. The physical world seen when using AR can be thought of as a fourth kind of information that the user can experience, similar to world-relative display but not artificially generated. A typical example of a head mounted display is shown in Figure 1‑1, and an example AR scene with both physical and virtual worlds is depicted in Figure 1‑2. The schematic diagram in Figure 1‑3 depicts how a see-through HMD (used to produce AR images for the user) can be conceptualised, and the next chapter discusses in depth their implementation. While other forms of sensory stimulation such as haptics and audio are also available to convey information to the user, these will not be discussed since the focus of this dissertation is HMD-based AR.
Figure 1‑1††††††† Example Sony Glasstron HMD with video camera and head tracker
Although the first HMD was implemented in 1968 and performed augmented reality, the first main area of research with these devices was for virtual reality. VR has a number of similar research problems with AR, but does not rely on the physical world to provide images and can also be viewed on display monitors or projectors. Since the user is normally tethered to the VR system, they are not able to walk large distances and virtual movement techniques such as flying are required to move beyond these limits. These same problems restricted initial AR research because, by its very nature, the user would like to walk around and explore the physical world overlaid with AR information without being tethered to a fixed point. While there are some important uses for AR in fixed locations, such as assisted surgery with overlaid medical imagery [STAT96] or assistance with assembly tasks [CURT98], the ability to move around freely is important. When discussing the challenges of working outdoors Azuma states that the ultimate goal of AR research is to develop systems that ďcan operate anywhere, in any environmentĒ [AZUM97b].
A pioneering piece of work in mobile augmented reality was the Touring Machine [FEIN97], the first example of a mobile outdoor AR system. Using technology that was small and light enough to be worn, a whole new area of mobile AR research (both indoor and outdoor) was created. While many research problems are similar to indoor VR, there are unsolved domain specific problems that prevent mainstream AR usage. Older survey papers (such as by Azuma [AZUM97a]) cover many technological problems such as tracking and registration. As this technology has improved, newer research is focusing on higher-level problems such as user interfaces, as discussed by Azuma et al. [AZUM01].
Figure 1‑2††††††† Example of outdoor augmented reality with computer-generated furniture
Figure 1‑3††††††† Schematic of augmented reality implementation using a HMD
Many outdoor AR systems produced to date rely only on the position and orientation of the userís head in the (sometimes limited) physical world as the user interface, with user interfaces that can indirectly adjust the virtual environment. Without a rich user interface capable of interacting with the virtual environment directly, AR systems are limited to changing only simple attributes, and rely on another computer (usually an indoor desktop machine) to actually create and edit 3D models. To date, no one has produced an outdoor AR system with an interface that allows the user to leave behind all their fixed equipment and independently perform 3D modelling in real-time. This dissertation explores user interface issues for AR, and makes a number of contributions toward making AR systems able to operate independently in the future, particularly for use in outdoor environments.
The development of user interfaces for mobile outdoor AR systems is currently an area with many unsolved problems. When technology is available in the future that solves existing registration and tracking problems, having powerful applications that can take advantage of this technology will be important. Azuma et al. state that ďwe need a better understanding of how to display data to a user and how the user should interact with the dataĒ [AZUM01]. In his discussions of virtual reality technology, Brooks stated that input devices and techniques that substitute for real interactions were still an unsolved problem and important for interfacing with users [BROO97]. While VR is a different environment in that the user is fully immersed and usually restricted in motion, the 3D interaction problems Brooks discusses are similar and still relevant to the AR domain. Working in an outdoor environment also imposes more restrictions due to its mobile nature, and increases the number of problems to overcome.
On desktop computers, the ubiquitous WIMP interface (windows, icons, menus, and pointer - as pioneered by systems such as the Xerox Star [JOHN89]) is the de-facto standard user interface that has been refined over many years. Since mobile outdoor AR is a unique operating environment, many existing input methodologies developed for AR/VR and desktop user interfaces are unavailable or unsuitable for use. In an early paper about 3D modelling on a desktop, Liang and Green stated that mouse-based interactions are bottlenecks to designing in 3D because users are forced to decompose 3D tasks into separate 1D or 2D components [LIAN93]. Another problem is that most desktop 2D input devices require surfaces to operate on, and these are unavailable when walking outdoors. Rather than trying to leverage WIMP-based user interfaces, new 3D interfaces should be designed that take full advantage of the environment and devices available to the user. An advantage of AR and VR is that the userís body can be used to control the view point very intuitively, although no de-facto standard has emerged for other controls in these applications. While research has been performed in the VR area to address this, many techniques developed are intended for use in immersive environments (which do not have physical world overlay requirements) and with fixed and limiting infrastructure (preventing portability).
With AR systems today (and with virtual environments in general), a current problem is the supply of 3D models for the computer to render and overlay on the physical world. Brooks mentions that 3D database construction and modelling is one of four technologies that are crucial for VR to become mainstream, and is still an unsolved problem [BROO97]. He mentions that there is promising work in the area of image-based reconstruction, but currently modelling is performed using CAD by-products or hard work. An interesting problem area to explore is what types of models can be created or captured directly while moving around outdoors. By integrating the modelling process and user interface, the user is able to control the modelling process directly and take advantage of their extensive knowledge of the environment.
For this dissertation I have investigated a number of different unsolved problems in the field of AR, and then combined the solutions developed to produce real world applications as demonstrations. I have formulated these different problems into research questions that will be addressed in this dissertation:
∑ How can a user intuitively control and interact with a mobile AR system outdoors, without hampering their mobility or encumbering their hands?
How can a user perform manipulation tasks (such as translate, rotate, and scale) with an outdoor mobile AR system of existing 3D geometry, in many cases out of armís reach and at scales larger than the userís body?
How can a user capture 3D geometry representing objects that exist in the physical world, or create new 3D geometry of objects that the user can preview alongside physical world objects, out of armís reach and at scales larger than the userís body?
What is an appropriate software architecture to develop this system with, to operate using a wide variety of hardware and software components, and to simplify application development?
What hardware must be developed and integrated into a wearable platform so that the user can perform AR in the physical world outdoors?
What application domains can take advantage of the novel ideas presented in this dissertation, and for what real world uses can they be applied to?
The main goal of this dissertation is to answer the questions discussed previously and contribute solutions towards the problems facing augmented reality today, predominantly in the area of user interfaces for mobile outdoor systems. The specific research goals of this dissertation are as follows:
∑ Mobile user interface - The user interface should not unnecessarily restrict the userís mobility, and should use intuitive and natural controls that are simple to learn and use. Requiring the user to carry and manipulate physical props as controls may interfere with the userís ability to perform a required task, such as holding a tool.
Real-time modelling enhanced with proprioception - The user should be able to interactively create and capture the geometry of buildings using the presence of their body. Using solid modelling operations and the current position and orientation of the head and hands will make this process intuitive for the user.
Mobile augmented reality - Outdoor augmented reality requires a mobile computer, a head mounted display, and tracking of the body. These technologies currently suffer from a number of limitations and applications must be designed with this in mind to be realisable. By using current technology, new ideas can be tested immediately rather than waiting for future technology that may not appear in the short term.
Using parts of the body such as the head and hands to perform gestures is a natural way of interacting with 3D environments, as humans are used to performing these actions when explaining operations to others and dealing with the physical world. By using techniques such as pointing and grabbing with objects in positions relative to the body, user interfaces can leverage proprioception, the userís inbuilt knowledge as to what their body is doing. Mine et al. [MINE97a] demonstrated that designing user interfaces to take advantage of a humanís proprioceptive capabilities produced improved results. Using an input device such as a mouse introduces extra levels of abstraction for the direct manipulation metaphor (as discussed by Johnson et al. [JOHN89]), and so using the head and hands allows more intuitive controls for view point specification and object manipulation.
Trying to leverage existing 2D input devices for use in a naturally 3D environment is the wrong approach to this problem, and designing proper 3D user interfaces that directly map the user to the problem (as well as taking advantage of existing interactive 3D research) will yield improved results. The use of 3D input devices has been demonstrated to improve design and modelling performance compared to 2D desktop systems that force the user to break down 3D problems into separate and unnatural 2D operations [CLAR76] [SACH91] [BUTT92] [LIAN93].
In VR environments the user is able to use combinations of physical movement and virtual flying operations to move about. In contrast, in AR environments the user is required to always move with their physical body otherwise registration between the physical and artificial worlds will be broken. Using direct manipulation techniques, interacting with objects that are too large or too far away is not possible. Desktop-based CAD systems rely on 2D inputs but can perform 3D operations through the use of a concept named working planes. By projecting a 2D input device cursor onto a working plane the full 3D coordinates of it can be calculated unambiguously. By extending the concept of working planes to augmented reality, both the creation of geometry and interaction with objects at a distance can be achieved. These working planes can be created using the physical presence of the body or made relative to other objects in the environment. Accurate estimation of the depth of objects at large distances away has been shown to be difficult for humans [CUTT95], and AR working planes provides accurate specification of depth. This functionality is achieved using a slightly increased number of interaction steps and reduction in available degrees of freedom.
By combining AR working planes with various primitive 3D objects (such as planes and cubes) and traditional constructive solid geometry techniques (such as carving and joining objects), powerful modelling operations can be realised, which I have termed construction at a distance. These operations give users the capability to capture 3D models of existing outdoor structures (supplementing existing surveying techniques), create new models for preview that do not currently exist, and perform editing operations to see what effect changes have on the environment. By taking advantage of a fully tracked AR system outdoors, and leveraging the presence of the userís body, interactive modelling can be supported in an intuitive fashion, streamlining the process for many types of real world applications.
This dissertation makes a number of research contributions to the current state of the art in augmented reality and user interfaces. Some of the initial contributions in this dissertation also require a number of supporting hardware and software artefacts to be designed and developed, each with their own separate contributions. The full list of contributions is:
∑ The analysis of current techniques for distance estimation and action at a distance, and the formulation of a technique named augmented reality working planes. This new technique can create objects accurately at large distances through the use of line of sight techniques and the projection of 2D cursors against planes. This technique is usable in any kind of virtual environment and is not limited to augmented reality. [PIEK03c]
The design and implementation of a series of techniques I term construction at a distance, which allow users to capture the 3D geometry of existing outdoor structures, as well as create 3D geometry for non-existent structures. This technique is based on AR and uses the physical presence of the user to control the modelling. Objects can be modelled that are at scales much larger than the user, and out of armís reach. [PIEK03c]
The iterative design and development of an augmented reality user interface for pointing and command entry, allowing a user wearing gloves to navigate through and select menu options using finger presses, without requiring high fidelity tracking that is unavailable outdoors. This user interface can operate without tracking, but when tracking is available it allows interaction at a distance with 3D environments. [PIEK03d]
The development of a vision-based hand tracking system using custom designed pinch gloves and existing fiducial marker tracking software that can work reliably under outdoor wearable conditions. [PIEK02f]
The development of applications that allow users to model buildings with the techniques described in this dissertation, and in some cases being able to model objects previously not possible with or faster than existing surveying techniques. [PIEK01b] [PIEK03c]
The design and implementation of a software architecture that is capable of supporting the research for this dissertation, culminating in the latest design of the Tinmith software. Current software architectures are still immature and do not support all the requirements for this dissertation, and so this architecture was designed to support these requirements and implements many novel solutions to various problems encountered during the design. [PIEK01c] [PIEK02a] [PIEK03f]
The iterative design and development of hardware required to demonstrate the applications running outdoors. Wearable computing is currently in its infancy and so the devices that are required to be used outdoors are not necessarily designed for this, and numerous problems have been encountered for which novel solutions to these are implemented. [PIEK02h]
After this introduction chapter, chapter two contains an overall background discussion introducing the concepts and technology that form a core of this dissertation. This overview provides a general discussion of related research, and each chapter then includes other more specific background information when relevant. Chapter three discusses the problems associated with a user interacting with objects at large distances Ė while most VR systems take advantage of a humanís ability to work well within armís reach, outdoor AR work tends to be performed away from the body where depth perception attenuates very rapidly. The novel idea of using the concept of CAD working planes for AR is introduced, along with various techniques that can be employed to perform interaction at a wide range of scales and distances beyond the reach of the user. Chapter four takes the concepts previously developed in the dissertation as well as constructive solid geometry and develops a new series of techniques named construction at a distance that allow users to perform modelling of outdoor objects using a mobile AR system. These techniques are then demonstrated using a series of examples of outdoor objects to show their usefulness. Chapter five explains how the previously developed techniques are interfaced to the user through pointing with the hands, command entry using the fingers, and the display of data to the HMD. The user interface is required to develop applications that are useable tools - an important part of this dissertation is the ability to test out the techniques and improve them iteratively. Chapter six introduces the software architecture used to facilitate the development of virtual environment applications. The software architecture contains a number of novel features that simplify the programming of these applications, unifying all the components with a consistent design based on object-oriented programming, data flow, and a Unix file system-based object repository. By tightly integrating components normally implemented separately, such as the scene graph, user interface, and internal data handling, capabilities that are normally difficult to program can be handled with relative ease. To complete the research, chapter seven describes the hardware components that are an important part of the overall implementation since the software relies on these to execute. The research and development of the mobile backpack, user input glove, and vision tracking system are explained to show some of the important new innovations that have been developed in these areas for use outdoors. After concluding with a discussion of the numerous contributions made and future work, this dissertation contains an appendix with a history of my previous hardware and software implementations, as well as links to where further information can be found about the project.
"If you want to make an apple pie from scratch, you must first create the universe."
This chapter contains a summary of the state of the art in augmented reality research and related technologies that are relevant to this dissertation. Some of this information has been developed during the period of my research and is included so comparisons can be made. First, an extended (from chapter 1) description and definition of augmented reality is presented, followed by a discussion of how it fits into the spectrum of virtual environments. The chapter then goes on to discuss various indoor and outdoor AR applications that have been developed, demonstrating the current state of the art. Following this is a discussion of the two techniques for performing real-time AR overlay, as well as a summary of the numerous types of tracking technologies employed. After these technologies have been explained, a history of human computer interaction techniques for desktop and virtual reality systems is then covered. Current techniques used for the capture of models in the physical world are then discussed, followed by a section summarising commercially available CAD software and solid modelling techniques. Finally, the problems of working outdoors with wearable computers are described, including how they can be used for mobile augmented reality.
When Sutherland proposed the concept of the Ultimate Display [SUTH65], his goal was to generate artificial stimulus that would give the user the impression that the experience is real. Instead of immersing the user into an artificial reality, a second approach is to augment the userís senses with extra information, letting them experience both artificial and real stimulus simultaneously. In his excellent survey paper of the field, Azuma defines augmented reality systems as those that contain the following three characteristics [AZUM97a]:
∑ Combines real and virtual
Interactive in real-time
Registered in 3D
This definition does not limit augmented reality to the use of head mounted displays (allowing for monitors, projectors, and shutter glasses), but excludes non-interactive media such as movies and television shows. This dissertation focuses on mobile outdoor augmented reality, and therefore this chapter will focus only on research related to head mounted displays.
Figure 2‑1††††††† Example of Milgram and Kishinoís reality-virtuality continuum
(Adapted from [MILG94])
With the availability of real-time computer-generated 3D graphics, computers can render synthetic environments on a display device that can give the user the impression they are immersed within a virtual world. This technology is referred to as virtual reality (VR) and is designed to simulate with a computer the physical world humans normally can see. The opposite of VR is the real physical world typically experienced by a human, although it may be slightly attenuated because it is being viewed via a head mounted display or video camera. Augmented reality is therefore made up of a combination of virtual and real environments, although the exact make up of this may vary significantly. Milgram and Kishino used these properties to define a reality-virtuality continuum [MILG94], and this can be used to perform comparisons between various forms of mixed reality by placement onto a spectrum. At one end of the continuum is the physical world, the other end is fully synthetic virtual environments, and AR is located somewhere in between since it is a combination of the two. Figure 2‑1 is adapted from Milgram and Kishinoís continuum, with example pictures at different locations on the reality-virtuality spectrum but showing the view from the same location. The first image in Figure 2‑1 shows a view of the physical world seen through a head mounted display, with no virtual information at all. The next image is augmented reality, where artificial objects (such as the table) are added to the physical world. The third image is augmented virtuality, where physical world objects (such as a live display of the userís view of the world) are added into a fully immersive virtual environment. The final image depicts a completely synthetic environment, with no information from the physical world being presented. Every type of 3D environment can be placed somewhere along this spectrum and can be used to easily compare and contrast their properties.
To overlay 3D models on to the userís view, a mobile AR system requires a HMD to be combined with a device that can measure the position and orientation of the userís head. As the user moves through the physical world the display is updated by the computer in real-time. The accuracy of the virtual objects registered to the physical world influences the realism of the fusion that the user experiences. A major focus of current AR research has been achieving good registration, as discussed extensively in survey papers by Azuma [AZUM97a] and Azuma et al. [AZUM01]. There are a number of known problems that cause poor registration, such as tracker inaccuracies, HMD misalignment, and delays in the various stages of rendering from the trackers to the display.
While registration is important for producing AR applications that are realistic (giving the user a sense of presence and hence being more immersive and easier to use) it is not the only important issue in AR research. Other questions, such as how do users interface with these systems, and what kind of tasks can systems perform, are also important and make the registration research useable for building real world applications.
During the evolution of technologies such as virtual reality and augmented reality, there have been a number of applications developed that demonstrate the use of this technology. In the field of augmented reality, this research work initially began indoors where hardware is able to be large and consume considerable electrical power without imposing too many restrictions on its use. As hardware has become smaller in size and more powerful, researchers are demonstrating more complex systems and are starting to move outdoors. This section discusses various applications that have been developed for both indoor and outdoor environments, approximately arranged in chronological order where possible.
For indoor augmented reality, there are a number of applications that have been developed in areas as diverse as information display, maintenance, construction, and medicine. These applications are used to provide extra situational awareness information to users to assist with their tasks. By projecting data onto the vision of a user, information is shown in situ in the environment and the user can better understand the relationship the data has with the physical world. The first working AR demonstration was performed using a HMD designed by Sutherland [SUTH68] and is shown in Figure 2‑2. This HMD is transparent, in that the user can see the physical world as well as computer-generated imagery from small CRT displays overlaid using a half silvered mirror. So while the goal of the Ultimate Display concept was to completely immerse the userís senses into a virtual environment, Sutherland actually invented the addition of information (augmented reality) with the development of this display. Sutherlandís demonstration projected a simple wire frame cube with line drawn characters representing compass directions on each wall. Other see through HMDs were developed for use in military applications, with examples such as the Super Cockpit project by Furness [FURN86]. The use of HMDs was designed to improve on existing heads up displays (HUD) in military aircraft, providing information wherever the user is looking instead of just projected onto the front of the glass windshield. Similar technology is used to implement displays for virtual reality, except these are opaque and do not use the physical world to provide extra detail.
Figure 2‑2††††††† The first head mounted display, developed by Ivan Sutherland in 1968
(Reprinted and reproduced with permission by Sun Microsystems, Inc)
Figure 2‑3††††††† External and AR immersive views of a laser printer maintenance application
(Images courtesy of Steven Feiner Ė Columbia University)
The KARMA system was developed by Feiner et al. as a test bed for the development of applications that can assist with 3D maintenance tasks [FEIN93a]. Instead of simply generating registered 3D graphics from a database to display information, KARMA uses automatic knowledge-based generation of output depending on a series of rules and constraints that are defined for the task. Since the output is not generated in advance, the system can customise the output to the current conditions and requirements of the user. One example demonstrated by Feiner et al. was a photocopier repair application (shown in Figure 2‑3) where the user is presented with detailed 3D instructions showing how to replace toner and paper cartridges.
Figure 2‑4††††††† Virtual information windows overlaid onto the physical world
(Image courtesy of Steven Feiner Ė Columbia University)
The Windows on the World work by Feiner et al. demonstrated the overlay of windows with 2D information onto an AR display [FEIN93b]. While traditional AR systems render 3D information, this system is based on 2D information in an X Windows server. Windows of information can be created in the X server and then attached to the display, the userís surround, or the physical world. As the user moves about the 3D environment, the system recalculates the position of the windows on the HMD. Since the system is based on X Windows, any standard X application can be used and information always appears facing the user with no perspective warping. Figure 2‑4 shows an example of 2D information windows attached to different parts of the environment.
One of the first commercially tested applications for augmented reality was developed by the Boeing company to assist with the construction of aircraft [CURT98]. One task performed by workers is the layout of wiring bundles on looms for embedding into the aircraft under construction. These wiring looms are complicated and so workers must constantly refer to paper diagrams to ensure the wires are placed correctly. Curtis et al. describe the testing of a prototype AR system that overlays the diagrams over the wiring board so that workers do not have to take their eyes away from the task. Although it was never fully deployed in the factory, this research is a good demonstration of how AR technology can be used to assist workers with complicated real world tasks.
(Image courtesy of David Mizell Ė Boeing Company)
Figure 2‑6††††††† AR with overlaid ultrasound data guiding doctors during needle biopsies
(Image courtesy of Andrei State Ė University of North Carolina, Chapel Hill)
Using AR to assist doctors with medical imaging is an area that shows much promise in the near future. A current problem with X-ray and ultrasound images is that they are two dimensional and it is difficult to spatially place this information easily within the physical world. By overlaying this information onto the patient using AR, the doctor can immediately see how the imaging data relates to the physical world and use it more effectively. State et al. have been performing research into the overlay of ultrasound images onto the body to assist with breast biopsies [STAT96]. During the biopsy, a needle is injected into areas of the body that the doctor needs to take a sample of and analyse. Normally, the doctor will take many samples and hope that they manage to achieve the correct location, but damaging areas of tissue in the process. Using AR, the ultrasound overlay can be used to see where the biopsy needle is relative to the area of interest, and accurately guide it to the correct location. This results in less damage to the surrounding tissue and a greater chance of sampling the desired area. Figure 2‑6 shows an example of a needle being inserted into a simulated patient with overlaid ultrasound imagery.
Figure 2‑7††††††† Studierstube AR environment, with hand-held tablets and widgets
(Images courtesy of Gerhard Reitmayr Ė Vienna University of Technology)
Schmalstieg et al. [SCHM00] and Reitmayr and Schmalstieg [REIT01a] describe a collaborative augmented reality system named Studierstube, which can perform shared design tasks. In this environment, users can work together to perform tasks such as painting objects and direct manipulation of 3D objects, as shown in Figure 2‑7. To provide users with a wide range of possible operations, the user carries a Personal Interaction Panel (PIP) [SZAL97]. The PIP can be constructed using either a pressure sensitive tablet or a tracked tablet and pen combination, and the AR system then overlays interactive widgets on top of the tablet. Using the pen on the tablet, the user can control the widgets that are linked up to various controls affecting the environment.
Figure 2‑8††††††† Marker held in the hand provides a tangible interface for viewing 3D objects
(Images courtesy of Mark Billinghurst Ė University of Washington)
The ARToolKit was developed by Kato and Billinghurst to perform the overlay of 3D objects on top of paper fiducial markers, using only tracking data derived from captured video images [KATO99]. Using this toolkit, a number of applications have been developed that use tangible interfaces to directly interact with 3D objects using the hands. Billinghurst et al. [BILL99] use this toolkit to perform video conferencing, with the user able to easily adjust the display of the remote user, as shown in Figure 2‑8. Another application that uses this technology is Magic Book by Billinghurst et al. [BILL01]. Each page of the magic book contains markers that are used to overlay 3D objects with AR. By pressing a switch on the display the user can be teleported into the book and experience immersive VR. Magic Book integrates an AR interface (for viewing the book from a top down view with a tangible interface) with a VR interface (for immersively flying around the bookís 3D world).
Figure 2‑9††††††† Actors captured as 3D models from multiple cameras overlaid onto a marker
(Image courtesy of Adrian Cheok Ė National University of Singapore)
The 3D Live system by Prince et al. [PRIN02] captures 3D models of actors in real-time that can then be viewed using augmented reality. By arranging a series of cameras around the actor, Virtual Viewpoint software from Zaxel [ZAX03] captures the 3D geometry using a shape from silhouette algorithm, and then is able to render it from any specified angle. 3D Live renders this output onto ARToolKit markers, and live models of actors can be held in the hands and viewed using easy to use tangible interfaces, as shown in Figure 2‑9. Prince et al. explored a number of displays for the system, such as holding actors in the hands on a card, or placing down life sized actors on the ground with large markers.
While indoor examples are useful, the ultimate goal of AR research is to produce systems that can be used in any environment with no restrictions on the user. Working outdoors expands the range of operation and has a number of unique problems, discussed further in Section 2.9. Mobile outdoor AR pushes the limits of current technology to work towards achieving the goal of unrestricted AR environments.
The first demonstration of AR operating in an outdoor environment is the Touring Machine (see Figure 2‑10) by Feiner et al. from Columbia University [FEIN97]. The Touring Machine is based on a large backpack computer system with all the equipment necessary to support AR attached. The Touring Machine provides users with labels that float over buildings, indicating the location of various buildings and features at the Columbia campus. Interaction with the system is through the use of a GPS and head compass to control the view of the world, and by gazing at objects of interest longer than a set dwell time the system presents further information. Further interaction with the system is provided by a tablet computer with a web-based browser interface to provide extra information. The Touring Machine was then extended by Hollerer et al. for the placement of what they termed Situated Documentaries [HOLL99]. This system is able to show 3D building models overlaying the physical world, giving users the ability to see buildings that no longer exist on the Columbia University campus. Another feature is the ability to view video clips, 360 degree scene representations, and information situated in space at the site of various events that occurred in the past.
Figure 2‑10††††† Touring Machine system overlays AR information in outdoor environments
(Images courtesy of Steven Feiner Ė Columbia University)
Figure 2‑11††††† BARS system used to reduce the detail of AR overlays presented to the user
(Images courtesy of Simon Julier Ė Naval Research Laboratory)
The Naval Research Laboratory is investigating outdoor AR with a system referred to as the Battlefield Augmented Reality System (BARS), a descendent of the previously described Touring Machine. Julier et al. describe the BARS system [JULI00] and how it is planned for use by soldiers in combat environments. In these environments, there are large quantities of information available (such as goals, waypoints, and enemy locations) but presenting all of this to the soldier could become overwhelming and confusing. Through the use of information filters, Julier et al. demonstrate examples (see Figure 2‑11) where only information of relevance to the user at the time is shown. This filtering is performed based on what the userís current goals are, and their current position and orientation in the physical world. The BARS system has also been extended to perform some simple outdoor modelling work [BAIL01]. For the user interface, a gyroscopic mouse is used to manipulate a 2D cursor and interact with standard 2D desktop widgets.
Figure 2‑12††††† Context Compass provides navigational instructions via AR overlays
(Images courtesy of Riku Suomela Ė Nokia Research Lab)
Nokia research has been performing research into building outdoor wearable AR systems, but with 2D overlaid information instead of 3D registered graphics. The Context Compass by Suomela and Lehikoinen [SUOM00] is designed to give users information about their current context and how to navigate in the environment. 2D cues are rendered onto the display (as depicted in Figure 2‑12). Other applications such as a top down perspective map view have also been implemented by Lehikoinen and Suomela [LEHI02]. To interact with the system, a glove-based input technique named N-fingers was developed by Lehikoinen and Roykkee [LEHI01]. The N-fingers technique provides up to four buttons in a diamond layout that can be used to scroll through lists with selection, act like a set of arrow keys, or directly map to a maximum of four commands.
Apart from the previously mentioned systems, there are a small number of other mobile AR systems that have also been developed. Billinghurst et al. performed studies on the use of wearable computers for mobile collaboration tasks [BILL98] [BILL99]. Yang et al. developed an AR tourist assistant with a multimodal interface using speech and gesture inputs [YANG99]. Puwelse et al. developed a miniaturised prototype low power terminal for AR [POUW99]. Behringer et al. developed a mobile AR system using COTS components for navigation and control experiments [BEHR00]. The TOWNWEAR system by Satoh et al. demonstrated high precision AR registration through the use of a fibre optic gyroscope [SATO01]. The DWARF software architecture was designed by Bauer et al. for use in writing mobile outdoor AR applications [BAUE01]. Cheok has developed some outdoor games using AR and the 3D Live system discussed previously [CHEO02a] [CHEO02c]. Cheok has also developed accelerometer-based input devices such as a tilt pad, a wand, and a gesture pad for use with wearable computers [CHEO02b]. Fisher presents an authoring toolkit for mixed reality experiences and developed a prototype outdoor AR system [FISH02]. Ribo et al. developed a hybrid inertial and vision-based tracker for use in real-time 3D visualisation with outdoor AR [RIBO02]. Roberts et al. are developing a prototype for visualisation of subsurface data using hand held, tripod, and backpack mounted outdoor AR systems [ROBE02]. The use of AR for visualisation of archaeological sites was performed by Vlahakis et al. [VLAH02].
As previously mentioned, this dissertation focuses on the use of HMDs to merge computer-generated images with the physical world to perform augmented reality. This section describes the HMDs and other supporting technology necessary to display AR information, implemented using either optical or video combination techniques. These techniques are described and then compared so the applications of each can be better understood.
Rolland et al. [ROLL94], Drascic and Milgram [DRAS96], and Rolland and Fuchs [ROLL00] describe in detail the technological and perceptual issues involved with both optical and video see through displays. These authors identified a number of important factors that need to be considered when selecting which technology to use for an application, and these are as follows:
∑ System latency Ė the amount of time taken from when physical motion occurs to when the final image reflecting this is displayed.
Real-scene resolution and distortion Ė the resolution that the physical world is presented to the user, and what changes are introduced by the optics.
Field of view Ė the angular portion of the userís view that is taken up by the virtual display, and whether peripheral vision is available to the user.
Viewpoint matching Ė the view of the physical world may not match the projection of the 3D overlay, and it is desirable to minimise these differences for the user.
Engineering and cost factors Ė certain designs require complex optics and so tradeoffs must be made between features and the resources required to construct the design.
∑ Perceived depth of overlapping objects Ė when virtual objects are drawn in front of a physical world object, it is desirable that the virtual objects perform correct occlusion.
Perceived depth of non-overlapping objects Ė by using depth cues such as familiar sizes, stereopsis, perspective, texture, and motion parallax, users can gauge the depth to distant objects.
Qualitative aspects Ė the virtual and physical worlds must be both rendered and these images must preserve their shape, colour, brightness, contrast, and level of detail to be useful to the user.
Depth of field Ė When physical and virtual images are passed through optics they will be focused at a particular distance. Keeping the image sharp at the required working distance is important for the user.
∑ User acceptance and safety Ė if the display attenuates the physical world it could be unsafe to use in some environments since the userís vision system is not being supplied with adequate information to navigate.
Adaptation Ė some displays have limitations that can be adjusted to by humans over time, and can be used as an alternative to improving the technology if there are no harmful side effects.
Peripheral field of view Ė the area outside the field of view of the virtual display is not overlaid with information, but is still useful to the user when navigating in the physical world.
The design of an optically combined see through HMD system may be represented by the schematic diagram in Figure 2‑13, although in practice the design is much more complex due to the internal optics required to merge and focus the images. A small internal LCD screen or CRT display in the HMD generates an image, and an optical combiner (such as a half silvered mirror or a prism) reflects part of the light into the userís eyes, and allowing light from the physical world to pass through to the eyes as well.
Figure 2‑13††††† Schematic of optical overlay-based augmented reality
In general, most current AR systems based on optically combined displays share the following properties:
∑ Optical combiners are used to merge physical and virtual world images.
The computer generates an overlay image that uses black whenever it wants the pixels to be see-through, and so the images are simple and can be rendered quickly.
The physical world light is seen by the user directly and has high resolution with an infinite refresh rate and no delay, while the generated image is pixelated and delayed.
The physical world remains at its dynamic focal length, while the overlay image is fixed at a specific focal length.
Accurate registration of the image with the physical world is difficult because the computer cannot monitor the final AR image to correct any misalignments.
Ghosting effects are caused by the optical combiner since both virtual and physical images are visible simultaneously (with reduced luminance), and obscuring the physical world with a generated image cannot typically be performed.
The field of view of the display is limited by the internal optics, and distortions increase at larger values.
The front of the display must be unoccluded so that the physical world can be seen through the HMD.
An example image from an optically combined AR system is shown in Figure 2‑14, with a 3D virtual table overlaying the physical world. Some of the problems with the technology are shown by the ghosted image and reflections, caused by sunlight entering the interface between the HMD and the lens of the camera capturing the photo.
Recent technology has improved on some of the problems discussed in this section. Pryor et al. developed the virtual retinal display, using lasers to project images through an optical combiner onto the userís retina [PRYO98]. These displays produce images with less ghosting effects and transmission losses than an LCD or CRT-based design. Kiyokawa et al. produced a research display that can block out the physical world selectively using an LCD mask inside the HMD to perform proper occlusion [KIYO00].
Figure 2‑14††††† Optically combined AR captured with a camera from inside the HMD
Figure 2‑15††††† Schematic of video overlay-based augmented reality
Video combined see through HMD systems use video cameras to capture the physical world, with virtual objects overlaid in hardware. This technique was first pioneered by Bajura et al. in 1992 [BAJU92]. An example implementation is depicted in the schematic in Figure 2‑15, with a video camera capturing images of the physical world that are combined with graphics generated by a computer. The display for this technique is opaque and therefore the user can only see the physical world through the video camera input. The combination process can be performed using two different techniques: using chroma-keying as a stencil to draw the video where AR pixels have not been drawn, or using the computer to draw the AR pixels on top of the video. The final image is then displayed to the user directly from an LCD or CRT display through appropriate optics.
In general, most current AR systems based on video combined displays share the following properties:
∑ The display is opaque and prevents light entering from the physical world, making it also possible to use for virtual reality tasks with no modifications required.
Some form of image processing is used to merge physical and virtual world images. Real-time image transformations may be necessary to adjust for resolution differences, spherical lens distortions, and differences in camera and display position.
The capture of the physical world is limited to the resolution of the camera, and the presentation of both physical and virtual information is limited to the resolution of the display. The final image viewed by the user is pixelated and delayed, with consistency between physical and virtual depending on whether the camera and display have similar resolutions.
The entire image projected to the user is at a constant focal length, which while reducing some depth cues also makes the image easier to view because the focus does not vary between physical and virtual objects.
More accurate registration may be achieved since the computer has access to both incoming and outgoing images. The computer may adjust the overlay to improve registration by using a closed feedback loop with image recognition.
The image overlay has no ghosting effects since the incoming video signal can be modified to completely occlude the physical world if desired.
By using video cameras in other spectrums (such as infra-red or ultraviolet) the user can perceive the physical world that is not normally visible to the human eye.
Demonstrations to external viewers on separate monitors or for recording to tape is simple since the video signal sent to the HMD may be passed through to capture exactly what the user sees.
An example image from a video combined AR system is shown in Figure 2‑16, with a 3D virtual table overlaying the physical world. Slight blurring of the video stream is caused by the camera resolution differing from that used by the display.
Figure 2‑16††††† Example video overlay AR image, captured directly from software
Table 2‑1 lists a summary of the information presented concerning optical and video combination techniques, comparing their features and limitations. Neither technology is the perfect solution for AR tasks, so the appropriate technique should be selected based on the requirements of the application.
Table 2‑1†††††††† Comparison between optical and video combined AR systems
To render graphics that are aligned with the physical world, devices that track in three dimensions the position and orientation of the HMD (as well as other parts of the body) are required. A tracker is a device that can measure the position and/or orientation of a sensor relative to a source. The tracking data is then passed to 3D rendering systems with the goal being to produce results that are realistic and match the physical world as accurately as possible. There have been a number of survey papers in the area: Welch and Foxlin discuss the state of the art in tracking [WELC02], Holloway and Lastra summarise the technology [HOLL93], and Azuma covers it as part of a general AR survey [AZUM97a]. This section covers the most popular technologies for tracking, with a particular focus on the types that are useful when working outdoors. This section is by no means a complete discussion of tracking and does not present new tracking results. I simply use currently available devices in this dissertation to provide tracking for my applications.
There are a number of different tracking technologies used, varying by the number of dimensions measured and the physical properties used. Holloway and Lastra discuss the different characteristics of various tracking systems [HOLL93], and these are summarised as follows:
∑ Accuracy Ė the ability of a tracker to measure its physical state compared to the actual values. Static errors are visible when the object is not moving, while dynamic errors vary depending on the motion of the object at the time.
Resolution Ė a measure of the smallest units that the tracker can measure.
Delay Ė the time period between reading inputs, processing the sensor data, and then passing this information to the computer. Large delays cause virtual objects to lag behind the correct location.
Update rate Ė the update rate measures the number of data values per second the tracker can produce. Faster update rates can perform smoother animation in virtual environments.
Infrastructure Ė trackers operate relative to a reference source. This reference may need to be measured relative to other objects to provide world coordinates useful to applications.
Operating range Ė trackers are limited to operating within a limited volume defined by the infrastructure. Signals emitted by sources attenuate rapidly over distance, which limits the range of operation.
Interference Ė various tracking technologies use emissions of signals that can be interfered with by other sources. External interference can be difficult to cancel out and affects the accuracy of results.
Cost Ė trackers range in price depending on complexity and the accuracy provided.
In this section, various aspects of the above factors will be discussed, along with the following extra factors:
∑ Degrees of freedom Ė trackers measure a number of degrees of freedom, being able to produce orientation, position, or some combination of these as results.
Coordinate type Ė some trackers measure velocity or acceleration that requires integration to produce relative-position values. When integrating values that are not exact, errors accumulate over time and cause drift. Absolute values do not require integration and are stable over time.
Working outdoors has a number of problems that are not noticed when dealing with indoor tracking systems. The use of tracking equipment in an indoor environment is simplified due to known limitations of the working environment. Alternatively, when working outdoors the environment is virtually unlimited in size and setting up infrastructure may be difficult. The use of technology that is required to be mobile restricts further the choices of tracking devices available. Azuma discusses in detail many problems to do with performing tracking outdoors [AZUM97b], and some extra factors to consider for comparison are:
∑ Portability Ė the device must be able to be worn by a person for use in a mobile environment, so weight and size are important.
Electrical power consumption Ė the tracking system must be able to run using batteries and not have excessive power requirements.
One of the main points stressed by Welch and Foxlin [WELC02] and Azuma et al. [AZUM98] is that to obtain the best quality tracking and to minimise any problems, hybrid tracking should be used. Since no tracking technology is perfect, hybrid trackers combine two or more different types of technologies with varying limitations to produce a better overall tracker. The last part of this section discusses some hybrid systems in detail.
Mechanical trackers rely on a physical connection between source and object, producing absolute position and orientation values directly.
The first tracker developed for interactive 3D computer graphics was the mechanical ďSword of DamoclesĒ by Sutherland along with his new HMD [SUTH68]. This tracker is a mechanical arm with angle sensors at each joint. By knowing the length of each arm segment and the measured angle at each joint, the position and orientation of the tip of the arm can be calculated relative to the base. Measuring angles at a mechanical joint is very accurate with only very slight delays. Due to the mechanical nature of the device, the motion of the user is restricted to the length of the arm and the various joints that connect it together. The arm is quite heavy for a human and so while counterweights help to make it lighter, the inertia of the arm requires the user to perform movements slowly and carefully to avoid being dragged about and injured.
Sutherland also demonstrated a wand like device to use for 3D input when using the HMD. This device uses a number of wires connected to pulleys and sensors that measure location information. While much more lightweight, this device requires that the wires not be touched by other objects in the room as well as the user, and so the user must take this into account when moving about the room, restricting their motion.
Accelerometers measure linear forces applied to the sensor and are source-less, producing relative-position values through double integration. Accelerometers can measure absolute pitch and roll when measuring acceleration caused by gravity.
Accelerometers are small and simple devices that measure acceleration forces applied to an object along a single axis, discussed in detail by Foxlin et al. [FOXL98a]. Modern accelerometers are implemented using micro-electro-mechanical systems (MEMS) technology that have no moving parts and can be embedded into small IC sized components. Accelerometers vibrate small elements internally and measure applied forces by sensing changes in these vibrations. To acquire velocity this value must be integrated, and then integrated again if relative position is required. The advantages of accelerometers are that they require no source or infrastructure, support very fast update rates, are cheap to buy, have low power requirements, and are simple to add to a wearable computer. The main disadvantage of this technology is that the process of integrating the measurements suffers from error accumulation and so within a short time period the values drift and become inaccurate. Due to the rapid accumulation of errors, accelerometers are not normally used standalone for position tracking. Accelerometers are commercially available from companies such as Crossbow [XBOW02].
When three accelerometers are mounted orthogonally to each other, a tilt sensor is formed that can measure the pitch and roll angles toward the gravity vector. Since gravity is a constant downward acceleration of approximately 9.8 ms-2 on Earth, orientation can be calculated by measuring the components of the gravity force that is being applied to each accelerometer. The tilt sensor output is vulnerable to errors caused by velocity and direction changes since these applied forces are indistinguishable from gravity.
Gyroscopes measure rotational forces applied to the sensor and are source-less, producing relative-orientation values through integration.
The first gyroscopes were mechanical devices constructed of a wheel spinning on an axis. Gyroscopes are induced to maintain spinning on a particular axis once set in motion, according to the laws of conservation of angular momentum. When an external force is applied to a gyroscope, the reaction is a motion perpendicular to the axis of rotation and can be measured. Gyroscopes are commonly used for direction measurements in submarines and ships, being accurate over long periods of time but typically very large and not portable.
Gyroscopes may also be constructed using MEMS technology and contain an internal vibrating resonator shaped like a tuning fork, discussed in detail by Foxlin et al. [FOXL98a]. When the vibrating resonator experiences rotational forces along the appropriate axis, Coriolis forces will cause the tines of the fork to vibrate in a perpendicular direction. These perpendicular forces are proportional to the angular velocity and are measured to produce output. Since each gyroscope measures only one axis of rotation, three sensors are mounted orthogonally to measure all degrees of freedom. To gain absolute orientation the velocity from the sensor must be integrated once, but this drifts over time and is not normally used for standalone orientation tracking. These devices are similar to accelerometers in that they require no source or infrastructure, support very fast update rates, are cheap to buy, have low power requirements, and are simple to add to a wearable computer. Another common name for these devices is a rate sensor, and companies such as Crossbow [XBOW02] manufacture gyroscopes for a wide range of non-tracking related commercial uses.
The most accurate gyroscope technology is based on lasers and the change in phase of photons that occurs between two intersecting laser beams and a detector. A Ring Laser Gyroscope (RLG) uses mirrors to bounce a laser beam around back to a detector, while a Fibre Optic Gyroscope (FOG) uses a coil of fibre optic cable wrapped around a rotation axis back to a detector. When a change in rotation occurs, the photons will take slightly more or less time than under no motion, and by measuring the phase difference and integrating it the total motion and hence relative orientation can be calculated. The TISS-5-40 FOG described by Sawada et al. [SAWA01] exhibited results with attitude better than Ī0.1 degrees and heading drift less than 1 degree per hour. In comparison, MEMS-based gyroscopes drift by a degree or more within minutes of time passing (or even less).
Ultrasonic tracking measures the time of flight of ultrasonic chirps from transmitter sources to microphones, producing absolute position and orientation values directly.
While the mechanical tracker developed by Sutherland for use with his HMD was accurate and fast [SUTH68], the weight of the device was difficult to work with and had limited motion due to the mechanical linkages. Sutherland also developed an acoustic tracker which was not tethered, and worked by sending out pulses of ultrasonic sound from the head, and measuring the time of flight to reach numerous sensors dispersed across the ceiling. While the tracker worked and demonstrated the possibilities of tracking without cumbersome mechanical linkages, problems were encountered with the ultrasonic pulses interfering with each other.
Ultrasonic tracking is limited by the properties of the pulses sent for time of flight detection. Noise in the environment caused by the jingling of keys will cause the tracker to fail, and environmental effects such as wind reduce the quality of the results [WELC02]. Since the pulses travel at the speed of sound, delays in tracking increase as the sensor moves away from the transmitter. By relying on the speed of sound, environmental effects such as temperature, humidity, and air currents can have an impact on the accuracy of the measurements.
While time of flight can produce accurate position values in a room using triangulation, calculating orientation is more difficult because multiple transmitters and receivers must be adequately spaced apart to get an accurate result. Furthermore, the orientation updates are quite slow compared to other technology. Foxlin et al. [FOXL98b] mentions that in the Constellation tracking system, the orientation values are combined with accelerometers and gyroscopes using a Kalman filter to increase the update rate and smooth the output.
Passive magnetic tracking measures the forces generated by the Earthís magnetic field as a source, producing absolute heading values directly.
When a freely suspended ferromagnetic object is exposed to a magnetic field, it will rotate so that its magnetic domains are in opposing alignment to the applied field. The Earth generates a magnetic field and a ferromagnetic object can be used to find the directions of the north and south poles of this field anywhere on the planet. By attaching a measuring scale to a freely suspended ferromagnetic object (to form a compass), the orientation of a tracking device can be determined relative to magnetic north. A compass is mechanical and due to the inertia of the magnet and attached parts, there is a settling time where the user of the device must wait to make an accurate reading. Electronic trackers have been constructed that use mechanical parts, but a more efficient method is to use solid state components.
A magnetometer is a solid state electronic device that can detect magnetic fields. As a magnetic field passes through a coil of wire, this produces an induced current that is proportional to the strength of the field and the incident angle to the coil. By aligning three magnetometers orthogonally, the direction to magnetic north can be calculated. These devices do not have inertia like the mechanical equivalent and so produce faster and more accurate results. Solid state magnetometers are available from a number companies such as Crossbow [XBOW02], who manufacture them for a number of non-tracking related commercial uses. Since the Earthís magnetic field exists everywhere on the surface, no infrastructure is required to be setup and there is no range limitation. Although the magnetic field produced by the Earth is quite strong, at the surface it is relatively weak when compared to the field produced by a local magnetic source. When other ferromagnetic objects are brought close to a magnetometer, the Earthís magnetic field is distorted locally and this affects the measurements of the sensor.
Active magnetic tracking measures the magnetic fields generated by a local transmitting source, producing absolute position and orientation values directly.
Rather than just relying on weak magnetic fields generated by the Earth to perform direction sensing, a tracking device may generate its own powerful local magnetic field. The tracking sensor measures this local magnetic field to determine position and orientation measurements between the sensor and source. The first tracker to implement this technique was designed in the 1970s to be used inside aircraft cockpits by Polhemus [POLH02], and is discussed by Raab et al. [RAAB79]. This technology uses three magnetometers arranged orthogonally as with a passive magnetic tracker, and a transmitter to generate a field for it to detect. The transmitter is constructed with three magnetic coils also arranged orthogonally, and each coil is pulsed with an AC signal to generate a magnetic field that is then detected by the sensor coils. By pulsing each transmitter coil separately and measuring the response in the sensor coils, both position and orientation can be reconstructed with good accuracy at close distances. A limitation of the AC signals used by Polhemus trackers is that changing eddy currents form in nearby metal objects and this causes distortions in the measured results. With this limitation in mind, one of the original engineers left the company to create a new company to fix these problems. The new company, Ascension Technologies [ASCE02], developed trackers that were similar but uses a DC pulse that generates stable eddy currents in nearby metal. To improve accuracy further, measurements from the sensors when the transmitter is not active are used to measure background magnetic fields. With these two improvements, magnetic tracking is less susceptible to interference by metal but it is still a problem. Both Polhemus and Ascension trackers work in environments where the tracking range is reasonably small and cables must be used to connect both the transmitter and multiple sensors to the controller unit.
GPS tracking measures the time of flight of signals from satellites in space to the user, producing absolute position values directly.
The Global Positioning System (GPS) was developed by the US military to provide reliable and real-time navigation information not previously available using existing methods such as dead reckoning and celestial navigation. The system is based on a constellation of 24 satellites that orbit the Earth, each transmitting specially encoded radio waves that contain highly accurate timing information. A receiver unit (with knowledge of the current position of the GPS satellites) can calculate its position by measuring the time of flight of these signals from space. The GPS satellites broadcast on two frequencies, L1 at 1575.4 MHz and L2 at 1227.6 MHz, and can penetrate atmospheric effects such as cloud, rain, smoke, smog, dust, and air pollution [MCEL98]. These frequencies are blocked by physical objects such as buildings and tree canopies, and so GPS cannot be reliably used amongst these objects. Encoded onto these signals are P-code for military users, C/A code for civilian users, and navigation messages containing satellite information. The L1 channel is intended only for civilian use (containing C/A and navigation messages) while L2 is designed for military use along with L1 and contains the more precise P-code information. Previously, the L1 channel was intentionally degraded by the US military using Selective Availability (SA) but this has now been deactivated. Another navigation system that is operated by the Russian Federation is the Global Navigation Satellite System (GLONASS), which operates in the same way as GPS but with different frequencies, satellite geometry, and signal encoding. Some GPS receivers also come with the ability to use GLONASS satellites to improve accuracy, although GLONASS cannot be currently used standalone because only a small number of the full constellation of satellites are in orbit.
The positioning quality resulting from a GPS receiver depends on the accuracy of the processing performed in the receiver as well as other external effects. The quality of the receiver is important because the time of flight measurements and position calculations rely on having an accurate internal clock and high resolution floating point unit. Using three satellites and an accurate atomic clock makes it possible to find the position of the user, but if the clock is not accurate (as is the case with commercial grade units) then an extra satellite is required to resolve the ambiguity. Further errors are introduced by particles as well as magnetic and electrical effects in the atmosphere that affect L1 and L2 bands. GPS uses time of flight and so cannot derive orientation, except when multiple sensors are spaced sufficiently far apart. This technique is not normally used except on large scale construction projects such as bridge building where adequate distances between sensors can be obtained.
Consumer grade GPS receivers come in a variety of form factors, from tiny embedded OEM chip designs to hand-held units with information displays such as the Garmin GPS 12XL [GARM99]. The accuracy of consumer grade GPS units vary depending on environmental conditions, but with the use of differential GPS (DGPS) radio signals, accuracies of 5-10 metres at one update per second can be achieved. DGPS signals are generated by a base station that measures the difference between its known surveyed position and reported GPS position, transmitting corrections for each satellite to GPS receivers located within a few hundred kilometres.
With the development of GPS, surveyors have started to use it for their work but require greater accuracy than is possible with DGPS enabled consumer grade receivers. By improving the quality of the internal receiver, the accuracy of GPS calculations can be improved. For example, the Trimble Ag132 [TRIM02] uses signal processing algorithms to filter out GPS signals reflected from nearby objects (referred to as multi path correction) and to compensate for some errors introduced by the atmosphere. By using clocks and processors more accurate than consumer grade units, as well as DGPS corrections, the accuracy of position measurements is improved to around 50 cm at a rate of 10 updates per second.
Even with these improved GPS units, Allison et al. discuss the use of Real-time Kinematic (RTK) techniques to further improve the accuracy of GPS tracking [ALLI94]. RTK GPS units can achieve accuracy in the range of 1-2 centimetres at 30 updates per second, obtained by counting the number of wavelengths between the satellite and the receiver, and using extra L2 frequency information. As the GPS antenna is moved around, the receiver closely monitors the phase changes in the signal and uses the count of the wavelengths to provide 1-2 centimetre accuracy. Although encrypted, the L2 signal still contains some timing information that can be extracted and RTK correlates this with the normal L1 signal. RTK also uses similar correction techniques as discussed previously and requires a secondary surveyed DGPS source located within a few hundred metres.
A number of different coordinate systems are used for representing locations on Earth, and are discussed extensively in the Geodetic Datum of Australia Technical Manual [ICSM00]. Polar coordinates have been traditionally used by navigators and surveyors and are the most logical for working with a planet that approximates a slightly flattened spheroid. Latitude is measured in degrees north/south from the equator, and longitude is measured in degrees east/west from the prime meridian. Distance from the centre of the Earth is not required in many cases because the user may be assumed to be located on the spheroid surface. A number of different spheroid parameter models (datums) have been developed for representing coordinates on an imperfectly shaped Earth, such as AGD66, AGD84, and WGS84. Polar coordinates are output natively by GPS systems and I refer to these coordinates as LLH (latitude-longitude-height) values.
An alternative to using polar coordinates is the use of Cartesian coordinates relative to the centre of the Earth, referred to as ECEF values. These coordinates are represented in metres as XYZ values, with Z passing through the geographic north pole, X through the equator and prime meridian, and Y through the equator and 90 degrees east. ECEF coordinates are commonly used to transform between coordinate datums but are unintuitive for use by humans. While LLH values only require latitude and longitude to specify position (where height can optionally be assumed to be on the spheroid), ECEF values require all 3 components to be useful at all. For working in small local areas, surveyors have developed special coordinate systems and projections using metres as units with an approximate flat Earth model, referred to as the Universal Transverse Mercator (UTM) grid. The Earth is divided up into a number of zones, each with separate origins so that coordinates within can be expressed easily. UTM coordinates are specified as northings and eastings values in metres from a local origin, and are simple to handle using standard trigonometry. ECEF and UTM coordinates are both useful when dealing with 3D renderers, which are designed to operate using Cartesian and not polar coordinates. There are a number of standard algorithms for accurately converting between LLH, ECEF, and UTM coordinates, although they are beyond the scope of this dissertation and described elsewhere [ICSM00].
Optical tracking can be implemented using active sources or passive features, producing either relative or absolute values depending on the technology in use. Trackers that use a known source such as a transmitter or fiducial marker produce absolute values directly. Source-less trackers require integration to produce relative orientation and position values.
When navigating outdoors, humans primarily rely on their eyesight to capture images of the local environment and match these against previous memories to work out their current position. By using information known about the world as well as cues such as occlusion, relative size, relative density, height, environmental effects, motion perspective, convergence, accommodation, and binocular disparities [CUTT95], people estimate the size and relative position to visible objects. There are current investigations to perform similar operations with video cameras and computer-based vision systems to estimate both position and orientation information. This research involves a number of disciplines such as artificial intelligence and computer vision, and is currently not mature enough to produce a tracker capable of working under arbitrary motion [AZUM01].
For environments where features are not known in advance, a number of techniques have been developed that attempt to perform full position and orientation tracking based on arbitrary objects in the scene. Some examples of these markerless trackers are by Simon et al. [SIMO00], Simon and Berger [SIMO02], Genc et al. [GENC02], and Chia et al. [CHIA02]. These researchers have developed algorithms for finding parts of the scene to track and calculate the motion of the user. There are many problems that include selecting appropriate features to track, dealing with the unexpected movement of non-static objects, blurred images caused by high speed motion, varying lighting conditions, distinguishing between translation and rotation, and drift of the integrated results over time. Bishop describes a tracking system implemented in a custom integrated circuit that measures optical flow at orders of magnitude faster than using video cameras, reducing drift and blur problems [BISH84]. Behringer attempted to match silhouettes on the horizon against those generated from local terrain models for orientation sensing, although suffers from the horizon being obscured by objects such as buildings and trees [BEHR98].
The most successful optical tracking methods so far all involve the use of markers that are placed in the environment for image recognition methods to detect. State et al. discuss problems similar to those mentioned earlier, and that fiducial markers or landmarks simplify the computations needed to analyse the image [STAT96]. Since landmark tracking still has problems with robustness, State et al. use magnetic tracking as the primary source and then correct it with tracking from live video streams. The magnetic tracker simplifies the vision tracking mechanism by providing an estimate for the location of the markers. The markers can then be used to extract out more precise position and orientation values that are pixel accurate. An alternative technique used in other systems is to drag the overlay image in 2D to align with the markers. This is much simpler but produces less accurate results because rotation and depth effects are not handled.
The ARToolKit software library has been developed by Kato and Billinghurst for use in tangible mixed reality applications [KATO99]. Using a single fiducial marker printed on paper, position and orientation information can be extracted from individual video frames. The markers are black squares with a white inner area that can contain a non-symmetrical pattern. By analysing the edges to measure perspective change, the rotation of the pattern, and the distance from the camera, the algorithm can extract tracking information in real-time. This algorithm is simple in that it requires only a single marker for tracking, and generates results that appear to overlay the marker correctly but may be inaccurate from other view points. This software is not intended to be used to implement accurate 6DOF tracking systems and other techniques should be used instead. ARToolKit is widely used in many research applications and is available under the GNU Public License, while others remain proprietary.
Based on the discussions of various tracking technologies in this section, it can be seen that there is no one technology that is able to perform accurate tracking standalone. As discussed earlier, Welch and Foxlin [WELC02] and Azuma et al. [AZUM98] agree that for the implementation of the ultimate tracker, hybrids will be required since each technology has limitations that cannot otherwise be overcome. Tracking outdoors is also even more difficult than indoors because the variables that affect the tracking cannot be controlled as easily. By combining two or more sensor technologies using an appropriate filter, these limitations may potentially be overcome to produce a tracker that is accurate over a wider range of conditions. Many commercial tracking systems implement some kind of hybrid tracking and these will be discussed in this subsection.
For position sensing, strap-down inertial navigation systems (INS) are constructed using a combination of three orthogonal gyroscopes and three orthogonal accelerometers [FOXL98a]. Accelerometers can measure 3D translations in sensor coordinates but the orientation of the sensor in world coordinates must be known to calculate the translation in world coordinates. By integrating the gyroscope values the orientation can be found and used to compensate for gravity in the accelerometers and then to calculate the INS position in world coordinates. The INS is not a complete tracking system however, since all the sensors are only relative devices and are affected by drift. Errors in any of the sensed values will affect other calculations and so introduce additional accumulating errors over time.
For measuring orientation, a simple example is the TCM2 digital compass from Precision Navigation [PNAV02], shown in Figure 2‑17. The TCM2 uses magnetometers to measure the angle of the device to magnetic north, and also tilt sensors based on accelerometers to measure the angle relative to the gravity vector downwards. Each sensor is not capable of measuring the values the other can, and so by combining them full 3DOF orientation values can be measured. These values are absolute and do not drift over time, making them ideal for AR applications, although are relatively low speed, include a lot of noise in the output, and the magnetic heading can be easily distorted by large metal objects.
Figure 2‑17††††† Precision Navigation TCM2 and InterSense InertiaCube2 tracking devices
Again for measuring orientation, Foxlin and Durlach [FOXL94] followed by Foxlin et al. [FOXL98a] developed the InertiaCube, which combines the tilt sensing properties of accelerometers with rate sensing gyroscopes (see the previously discussed INS) to produce full 3-axis orientation values. This device requires no infrastructure (apart from gravity to supply an absolute reference for the tilt sensor) and is very small and portable. A filter is used to combine the tilt sensor values with the drifting gyroscopes to produce absolute orientation values with the desirable properties from both sources. Since the heading information is not corrected with an absolute sensor, this value will drift over time while the others remain stable.
To compensate for problems associated with the previous two hybrid orientation trackers, Foxlin combined the InertiaCube sensor with a 3-axis magnetometer (similar to the TCM2). The specially designed Kalman filter reads in all the sensor values and combines them to correct for errors that occur under different conditions. This device is sold as the InterSense IS-300 and InertiaCube2 [ISEN03], and is shown in Figure 2‑17. Both of these devices produce absolute values that do not drift over time since correction is performed on all orientation axes. The relative values then provide smooth and high speed output under fast motion. Azuma also produced a similar tracker that combined a three axis gyroscope with a TCM2 magnetic compass [AZUM99]. A limitation of these hybrids is if the sensor is exposed to local magnetic distortions for a considerable time, the Kalman filter will incorrectly calibrate the gyroscopes and introduce errors into the output.
Table 2‑2 lists a summary of the information presented concerning 3D tracking technologies, comparing their features and limitations. As described previously, there is no one perfect tracking technology and so combinations of these are typically required.
Table 2‑2†††††††† Comparison between various types of 3D tracking technology
In 1963, Sutherland produced the Sketchpad system [SUTH63] that had a revolutionary interface for its time. Instead of using the computer for batch processing or using a keyboard to enter interactive commands, he developed a new user interface that allowed users to draw graphical objects and manipulate them using a light pen on a display. The interactions occur in real-time, and use what is now referred to as direct manipulation to let the user control the system.
A second important piece of early work was the Put-That-There system by Bolt, where a user may interact with data in a room using simple pointing gestures and speech commands [BOLT80]. The goal of this system was to allow a user sitting in a chair to interact with information projected onto the walls. This system used Polhemus magnetic trackers for the pointing, a speech recognition computer with a pre-programmed grammar for interpreting commands, and a data wall showing information of interest to the user.
The term direct manipulation was first defined by Shneiderman, involving the continuous representation of application data as objects that can be manipulated with an input device [SHNE83]. Other important properties are that operations are rapid, incremental, and reversible with immediate feedback, and usable by both novice and expert users. The most powerful direct manipulation interfaces use analogies that a user can easily relate to, such as turning a dial or dragging a box, and are more intuitive than command-based interfaces. Johnson et al. describes direct manipulation using another guideline: ďData objects should be selected and operated on by simulated physical contact rather than by abstract verbal referenceĒ [JOHN89].
One of the first and most complete implementations of a direct manipulation-based user interface was the Xerox Alto project, later released commercially as the Xerox Star [JOHN89]. This system uses a keyboard and monitor like many others, but for the first time implemented what is referred to as the desktop metaphor. Rather than a user invoking tools to perform operations, the system abstracts these concepts to that of an office and working with documents. Instead of using a command line interface, the user controls the system by directly manipulating icons and dialog boxes presented on the display, using a mouse to simulate physical contact. The presentation of the display was also novel by using a high resolution graphical display, with windows for displaying documents and abstract graphical representations of data. The Xerox Star system inspired many other WIMP user interfaces such as the Macintosh and Windows, which are still used today. It is important to realise that not all systems with a mouse and windows are necessarily desktop-based. Only those systems that abstract away operations on data files to an office desktop metaphor meet this criteria [JOHN89]. For example, X Windows and TWM are only 2D windowing environments for running applications. Systems such as the Macintosh, Windows, KDE, and GNOME are all desktop environments. This dissertation uses the term 2D desktop more generally to refer to any computer system that is placed on a desk. These systems use a mouse-like input device, a keyboard for command entry, and a monitor for the display of information.
This section discusses the evolution of interaction techniques for virtual reality, starting from the development of the first HMDs and tracking devices to the current state of the art. The main focus of this section is on the user interfaces that have been developed, with the majority of the research systems described performing the modelling of 3D objects. Modelling applications are good examples for research because they require complicated user interfaces with efficient and easy to use techniques. Applications that can perform 3D modelling are also still an area of research, as discussed by Brooks [BROO97]. This chapter will only review the introduction of techniques to the VR area, since the previously reviewed AR systems borrowed their interfaces from similar VR systems.
After working on the Sketchpad system, Sutherland presented a new idea that he termed the Ultimate Display [SUTH65] in 1965, when computer graphics was still in its infancy. The goal was to create an interface that would provide data for all the senses of a human, fully immersing them into a simulated reality that does not exist. As previously mentioned, in 1968 Sutherland produced the first HMD [SUTH68], which was transparent and able to overlay the physical world with a simple wire frame cube and labels. To track the motion of the HMD a mechanical tracker was used, giving the user real-time 3D graphics based on their point of view. A 3D input wand with a wire filament tracker (described previously) was used to perform simple interactions with the environment. While the display and graphics were primitive by todayís standards, this is the first demonstrated example of technology for virtual and augmented reality.
Although humans are able to understand 2D views of 3D information, VR is based on the notion that it is more intuitive to hold a 3D object in the hands or to walk around and view an object from different angles. Trying to create and edit 3D information using only a 2D view is cumbersome, and a HMD allows intuitive user interfaces based on the motion of the body. A number of research papers (that will be discussed later in this subsection) such as [CLAR76], [SACH91], [BUTT92], and [LIAN93] all have the common theme that 3D direct manipulation interfaces are superior to 2D-based environments that impose limitations on operations that are naturally 3D.
The first interactive 3D editing application on a HMD was a surface editor by Clark [CLAR76] that made use of the HMD, wand, and mechanical trackers designed by Sutherland. This system removes the need for keyboard commands and numeric entry of data that was common in previous 3D systems. A user wearing a HMD can walk around to get different views of a spline surface, and then manipulate points interactively using a wand. This allows designers to freely explore changes to the surface using the direct manipulation metaphor, and focus on the task of designing a suitable surface. Clark concluded his paper with the comment that ď3-D computer-aided surface design is best done in real-time with 3-D tools. To expect a designer of 3-D surfaces to work with 2-D input and viewing devices unnecessarily removes a valuable degree of freedomĒ.
The systems presented by Sutherland and Clark demonstrated important ideas but the technology was too cumbersome for regular use. With the availability of new technology, Fisher et al. implemented the Virtual Environment Display System [FISH86], using a stereo HMD with a wide field of view that matches the capabilities of the human vision system, and active magnetic tracking (discussed previously) that allows much more freedom of motion. To interact with the system, speech recognition for commands and a tracked pair of gloves for direct 3D manipulation is used. While the direct manipulation of objects is intuitive, the level of sophistication of the user interface was very primitive compared to the state of the art methodologies available for 2D interfaces, such as the Xerox Star [JOHN89].
Although Clarkís surface editor [CLAR76] was the first demonstration of modelling using a HMD and 3D input device, it was quite primitive due to its limited functionality compared with existing CAD systems. Later research was performed using 3D tracked input devices to improve usability but still used 2D desktop monitors as displays. These systems allow the user to directly manipulate 3D objects, but do not use the body to move around the environment. Later research then used immersive VR displays that supported the full use of the body to interact with the environment.
Sachs et al. presented a system named 3-Draw [SACH91], which allowed users to perform 3D modelling tasks using a pair of 6DOF tracked Polhemus sensors: one held in the hand as a stylus and the other attached to a tablet. The model being created is positioned relative to the tablet and is represented on a desktop monitor. By rotating and moving the tablet, various views of the object may be seen interactively. Using the tracked stylus, the user can sketch curves in 3D and then deform them into various shapes. Since both stylus and tablet are tracked the user can freely manipulate them to get the best view for comfortable modelling. Although allowing the interactive specification of views, the view cannot be moved beyond the range of the tracker, limiting the size of the modelling universe. Sachs et al. made the observation that ďthe simultaneous use of two sensors takes advantage of peopleís innate ability - knowing precisely where their hands are relative to each otherĒ. Sachs et al. demonstrated that by using 3D input, encouraging results were achieved when compared to existing 2D techniques used by CAD systems. Sachs et al. also state that their approach of focusing on the modelling task was more efficient than working at the non-intuitive control point level where direction and magnitude must be specified manually.
The JDCAD 3D modelling system also uses a desktop monitor as a display, and Liang and Green identified a number of key problems with the use of 2D input devices for 3D modelling, justifying the use of 3D input devices [LIAN93]. A designer using a 2D input device must break down 3D problems into unnatural 2D steps, therefore changing their thinking to suit the modelling tool. Some examples are creating vertices by using the cursor from two different view points, or rotating objects one axis at a time using widgets. Another insightful comment made from testing JDCAD was that users found it hard to control all six degrees of freedom (position and especially rotation) at the same time. Being able to constrain position and orientation separately is useful - while having only 2D inputs is limiting for a designer, full 6DOF controls can be hard to control accurately. Compared to the previously discussed 3-Draw system that is limited to the work area of the tablet, JDCAD implements techniques for flying and action at a distance. These techniques allow the user to perform modelling tasks at any location and obtain arbitrary views of objects. Pioneering techniques (described later) were also developed for selection and manipulation of objects out of armís reach, and the execution of commands using 3D menus.
Using techniques developed in previous work, Butterworth et al. developed a fully immersive HMD-based modelling system named 3DM [BUTT92]. The use of traditional keyboards and mice is no longer available when immersed in VR, and so alternative user interface techniques are required to interact with the system. 3DM was the first immersive system to support a user interface with 3D menus and tool palettes. The interface performs the selection of options using direct manipulation, similar to traditional desktop user interfaces. The authors state that the application was inspired by the ease of use of the interface for the MacDraw program, which uses similar menus and toolbars. 3DM is able to create simple geometric objects such as cylinders, boxes, and cones, and triangle strips. During the process of creation, as well as during edit operations, the user may directly manipulate these objects at a number of levels: vertices, objects, and groups of objects in hierarchies. For objects that are too far away or at a size that is difficult to work with, the userís scale and location in the world can be adjusted as desired by the user. The 3D menus and tool palettes pioneered in 3DM are concepts still used in many VR applications.
Figure 2‑18††††† CDS system with pull down menus and creation of vertices to extrude solids
(Images courtesy of Doug Bowman Ė Virginia Polytechnic Institute)
The CDS system designed by Bowman [BOWM96] takes concepts from VR described previously, and extends these with extra features for the construction of new objects at a distance. Rather than just placing down objects within armís reach, CDS is capable of projecting points against the ground plane using a virtual laser beam originating from the userís hand, as shown in Figure 2‑18. The points on the ground plane can then be connected together using lines and extruded upwards to form solid shapes.
Figure 2‑19††††† CHIMP system with hand-held widgets, object selection, and manipulation
(Images courtesy of Mark Mine Ė University of North Carolina, Chapel Hill)
Mine et al. produced the CHIMP system [MINE97a], integrating many of the techniques from previous VR systems (such as laser pointing, 3D widgets, and menus) as well as the concept of proprioception, the intuitive knowledge the user has about objects placed on or near the body. Rather than working at a distance, CHIMP is designed to allow users to interact with the environment within armís reach, since humans are more adept at working within this range. To interact with objects at a distance, Mine et al. introduced the concept of scaled world grab, where the world is scaled so that the selected object appears at a workable size in the hand. The user can then easily adjust the object with widgets that are within armís reach, such as shown in Figure 2‑19. By placing items such as menus and tools near the body, the user can reach out and grab them using proprioception without having to see them directly with the eyes. Users can indicate commands that are similar to the physical world using intuitive gestures.
Figure 2‑20††††† Immersive and external views of the SmartScene 3D modelling environment
(Images courtesy of Paul Mlyniec Ė Digital ArtForms Inc)
SmartScene from Multigen [MULT01] is a commercial 3D immersive modelling system and is shown in Figure 2‑20. It implements many of the techniques presented previously, combining them together to produce a powerful 3D modelling system that is capable of performing a wide range of tasks on both HMD and projector displays. It uses direct manipulation, tool palettes, menus, scaled world operations, and the creating and editing of geometry in real-time, controlled using two 6DOF tracked pinch gloves.
This subsection presents a detailed summary of interaction techniques that are currently used in VR modelling systems. The previous discussion only mentioned the development of the most notable systems over time, while this subsection introduces the many techniques contributed by various researchers. Techniques are discussed for interaction within armís reach, action at a distance, and command entry. Bowman and Hodges [BOWM97] and Poupyrev [POUP98] both provide surveys and comparisons of some of the 3D manipulation techniques discussed.
The most intuitive way to manipulate objects in a VR system is to use the concept of direct manipulation - reaching out and grabbing an object using a tracked prop with buttons or a set of gloves with finger press sensors. When an object is selected, it is slaved to the userís hand and can be freely translated and rotated in all 6DOFs. The implementation of operations such as flying, scaling, and grabbing using affine transformations in a scene graph is discussed by Robinett and Holloway [ROBI92]. Based on existing direct manipulation work in 2D user interfaces, the next natural progression is to implement concepts such as menus, tool palettes, and icons, as implemented first by Butterworth et al. in 3DM [BUTT92].
Conner et al. introduced the concept of 3D widgets[CONN92], based on extensive previous work in the 2D desktop area. Conner et al. define a widget as being an encapsulation of geometry and behaviour, with different widgets implementing a range of geometry and behaviours. Widgets were first introduced in 2D user interface toolkits to assist with application development, designed to abstract away the user interface from the program performing the task. By extending this concept to 3D, Conner et al. propose the development of 3D user interface toolkits with similar goals in mind, but supporting more powerful interactions using the extra DOFs available. Since 3D environments contain a number of extra DOFs, the definition of 3D widgets is much more complex than in previous 2D environments.
With research by Mine et al. into working within armís reach and proprioception [MINE97a], the use of hand-held widgets was proposed as a way of efficiently adjusting controls in the environment. Instead of grabbing and rotating an object at a distance, the user grabs an object and it appears in their hands with various widget handles around it. Using the other hand, the user can then grab handles to perform operations such as scaling or rotation. This interaction is much more efficient because users are familiar with the concept of holding a physical object in one hand and manipulating it with the other hand. By holding 3D dialog boxes in the hands, users can manipulate widgets to enter values and perform operations that have no direct mapping with the physical world. Mine et al. also introduce the concept of storing interaction widgets relative to the body that the user intuitively knows where to find using proprioception. For example, to access a menu the user lifts their hand into the air and pulls down, causing the menu to be dragged down into view. To delete an object, the user grabs it and uses a throwing over the shoulder gesture.
While direct manipulation may seem intuitive, one problem is the lack of haptic feedback. Users are familiar with reaching out and feeling an object while grabbing it, making virtual grabbing difficult because the user can only rely on visual cues for feedback. Interacting with user interface objects such as tool palettes and menus is also difficult due to the same lack of haptic feedback. Another limitation of direct manipulation is that if the object is not within armís reach, the user must walk or virtually fly to a location that is closer. When walking or flying is not possible, alternative metaphors to direct manipulation are required.
In some cases it is not possible to express an operation using an intuitive direct manipulation operation. Operations such as selecting a lighting model or adjusting the number of triangles used in a primitive have no physical world counterpart and so an abstraction must be introduced. Brooks offers an insight into what interactions are suitable for different operations in virtual environments [BROO88]. For cases where a discrete interactive change to a virtual world parameter is required, Brooks suggests the use of menu selections. For dynamically changing parameters, dynamic input devices should be used instead. The only time Brooks recommends character-based commands be used is when retrieving an object by name or attaching a name to an object. In general, designers of VR and AR systems should avoid using keyboard entry where possible because command-based systems are highly abstract and unintuitive.
Brooks also suggests that the specification of commands and operations in the view area should be separate and assigned to different cursors, since it ďinterrupts both the visual and tactile continuity inherent in the operand cursorís natural movementĒ. Using this methodology, the command interface can be changed to other formats without affecting the interactions in the view. Brooks suggests that command selection is also a natural candidate for speech recognition engines. As previously mentioned, the Put-That-There system by Bolt implemented an interface that used separate hand tracking for pointing and speech recognition for command entry [BOLT80].
Liang and Green implemented the first 3D interaction menus, named Daisy and Ring [LIAN93]. The Daisy menu presents a series of commands arranged evenly around a sphere, and by rotating the input device the desired menu option can be moved to within a selection basket and a button pressed. Liang and Green found that rotating a Polhemus sensor about all 3 axes was difficult because of the hanging cable and so developed an improved menu. The Ring menu presents the options in a ring shape (so there are fewer options than with Daisy) and only one DOF is used to rotate the ring and move an item into the selection basket. The Ring menu was demonstrated to improve usability even though fewer options are available at any time. The HoloSketch system by Deering also uses ring style menus, and is used to support a stereo desktop 6DOF modelling application [DEER95]. Deeringís 3D pie menus pop up around the 6DOF cursor when a wand button is pressed, and the user then moves the wand to the desired option and selects it. Sub-menus are supported by pushing the menu back and floating a new pie menu on top. These pie menus are designed to use accurate position tracking and minimise both travelling distance and screen real estate by appearing around the wand.
As discussed earlier, Butterworth et al. [BUTT92] and Mine et al. [MINE97a] both implement 3D menus and tool palettes that appear in front of the user. The user indicates 3D items with their hands to directly make selections, but requires accurate tracking to be usable. The pull down menus and tool palettes implemented are structured similar to those used in 2D desktop style user interface toolkits. This form of command entry can be quite tiring if the user is constantly reaching out in front of their body to make selections, and lacks the haptic feedback that is expected by the user.
Bowman and Wingrave developed a VR menu system [BOWM01] that employs Pinch Gloves [FAKE01] as the input device. Instead of using a pull down menu, the top-level items are mapped to the fingers on one hand and the second-level options to the other hand, with no 3D tracking of the hands required. The user selects a top-level option with the matching finger, the system updates the display with a list of second-level options, and the user then picks one using the other handís fingers. Using their small finger, the user can cycle through options if there are more than three options available. This menu design is limited to a depth of two, and is not easily scalable to a large number of hierarchical commands.
Instead of using a menu or tool palette to execute commands, Zeleznikís SKETCH system uses a three button 2D input device to sketch out 3D pictures using gestures [ZELE96]. Hand gestures are analysed to initiate various commands such as selection, manipulation, and deletion. Gesture-based inputs are limited to working in environments where fast and accurate tracking is available, and to a limited set of commands that are expressed using real life actions. When abstract concepts that have no logical gesture mapping are performed, unintuitive gestures must be created and learned, or less direct interfaces such as those discussed previously must be used.
Wloka and Greenfield developed a device named the Virtual Tricorder, a generic tool that can be used to perform many operations in VR [WLOK95]. This device is tracked in 3D and contains a set of input buttons that are mapped to different operations. By overloading the deviceís operations the number of tools to be carried is reduced while increasing the functionality. The Virtual Tricorder is limited to the number of buttons that are available, and the overloading of functionality may become complicated for the user to understand.
In many cases it is inconvenient or not possible to fly to an object to be directly manipulated. In the JDCAD system [LIAN93], Liang and Green developed a number of techniques that were later described by Mine using the term action at a distance [MINE95b]. Rather than directly manipulating the object, the user can manipulate it through pointing. The first technique Liang and Green developed is virtual laser pointing, and allows the intuitive selection of objects by projecting a virtual laser from the hands toward an object, just as can be achieved in the physical world. Once the object is selected, it may be attached to the laser beam like a long rod and manipulated by rotating the hand. While this technique can perform remote manipulation very intuitively, it suffers from the amplification of fine hand movements and tracker noise to large motions at a distance. Other transformations such as rotation along an arbitrary axis or varying the distance along the laser beam are also not supported without further extensions to the technique. Bowman and Hodges implemented a fishing reel metaphor that can adjust object translation toward and away from the user after selection [BOWM97]. Liang and Green discovered that at large distances and with small objects the thin laser beam was difficult to select with, mostly due to the amplification of tracker noise. The spotlight technique was then developed, using cone shapes that increase in radius over distance.
Forsberg et al. improved on the spotlight technique to create a new technique named selection apertures [FORS96]. Rather than using a laser or cone originating from the userís hand, this technique originates a cone from the userís head, with the axis of the cone passing through the cursor on the userís hand. A circular selection cursor mapped to the userís hand defines the radius of the cone at the hand, affecting the overall size of the selection cone. An interesting property of this technique is that the cone does not originate from the hands, and so only the position of the cursor is required instead of full 6DOF tracker values. Devices with poor orientation sensing can be used, and selection accuracy can be improved since the user is looking through a virtual scope rather than aiming a virtual laser from the waist.
Another alternative to the direct manipulation of objects is by mapping the userís hand to a selection cursor using a non-linear function. Mine first described such techniques [MINE95a], and discussed how the location of the hands can be used to control the velocity of a cursor flying through the environment. If the user moves their hand beyond a central point, the object will move away with increased velocity, and by bringing their hand closer the object will move toward the user. This technique is similar to using a joystick to control the motion of the cursor, except this technique is in 3D and uses the hands. The use of joystick controls adds a layer of abstraction from direct manipulation that may degrade performance in the environment.
The GoGo arm [POUP96] was developed by Poupyrev et al. as a way of manipulating objects at a distance with a similar technique as described by Mine, but using absolute position values instead of a velocity abstraction. This technique uses the volume within reach of the userís hands and maps the closest two thirds directly to the cursor for direct manipulation. The remaining one third of the volume away from the user is mapped to a non-linear quadratic function that increases rapidly with distance. The overall function used has a smooth transition and allows working within armís reach and at long distances without changing modes. Since this technique controls a 3D cursor, it can be used for both selection and manipulation, although the accuracy of the technique will degrade according to the function used as the distance is increased.
On a standard desktop display, 3D objects can be selected with a mouse by positioning the 2D cursor on top of the object of interest. To select an object, the system finds the intersection with all objects underneath the cursor and returns the closest one. This technique can also be used in virtual environments by placing the cursor over an object at a distance, and projecting a selection ray from the head through the hand cursor. Pierce et al. describe these as image plane techniques [PIER97], and indicate how they can be used to perform selection of objects out of armís reach. Four selection techniques are proposed: head crusher, sticky finger, lifting palm, and framing hands, as an alterative to previous laser and spotlight techniques. Although Pierce et al. did not discuss manipulation of objects using this technique, the same mechanism (as in laser and aperture selection) can be used to adjust the position and orientation of the object at a distance. An interesting comment from the discussion of an informal user study by Pierce et al. is that ďno user has had any trouble understanding how the techniques workĒ, and that arm fatigue is minimised since hand selection time is reduced compared to other VR techniques.
Rather than trying to extend the direct reach of a user with the use of extensions such as laser beams and non-linear mappings, Stoakley et al. proposed a new interaction metaphor named Worlds-in-Miniature [STOA95]. In this metaphor, the user holds a small copy of the 3D world in their hands. By viewing the WIM in the hands, objects that are currently obscured in the immersive VR view can be easily seen from overhead. Objects in the WIM can also be manipulated directly using the hands, with these changes made visible in the immersive VR view. The advantage of this technique is that it can perform selection and manipulation tasks using direct manipulation, even through the object may be very far away from the user. For cases where the world is very large however, the WIM model must be scaled to fit the userís hand and so small objects may be invisible to the user.
The scaled world grab technique by Mine et al. [MINE97a] uses similar concepts to perform remote interactions within armís reach. After selecting an object the entire world is scaled and translated so that the object of interest appears in the hand. The user can then interact with the object and others nearby, with the world being returned back to its original scale when finished. Since the entire world is scaled during the grab operation, the user can still see other nearby objects and there is no need to divide up screen space between a WIM and the immersive view.
Another technique designed to overcome the shortcomings of WIMs is the Voodoo Dolls technique by Pierce et al. [PIER99]. In this technique, the user works in a standard immersive view and then selects an object of interest. When selected, the system creates a unit sized ďdollĒ in the hands that represents the object in the environment. Changes made to a doll held in the hand are reflected immediately in the normal environment. When dolls are held in both hands, the dolls are scaled around the non-dominant doll of unit size. By varying the position and rotation of the hands the relative placement of the dolls can be adjusted in the environment. Dolls can be created by selecting, released by letting go with the hands, and passed between the hands. To provide context for the user, the dolls are rendered with the selected object as well as others that are close by.
Users intuitively know how to manipulate objects in the physical world, and so by using tracked physical props these can be used as user interaction devices. Previously mentioned work normally uses gloves or button controllers to interact with the environment, but these are generic devices that do not have real world equivalents. Hinckley et al. demonstrated evaluations of using props for the visualisation of 3D models of the brain [HINC94b]. A small dollís head with an embedded Polhemus sensor is used to represent the brain, while a tracked cutting plane and pointer are used to select slices or points in the virtual model. The surgeon can very intuitively interact with these props since their operation is obvious and uses the surgeonís existing manipulation skills. In other research, Hinckley et al. again demonstrated that well designed tracker props are easier to understand and use than the generically-shaped tracker sensors supplied by the manufacturer [HINC97]. The use of props can be cumbersome if there are too many discrete operations to represent, or if the task is too abstract to map to any physical world prop. The use of props also prevents the use of the hands for other tasks that may be required.
The Personal Interaction Panel developed by Szalavari and Gervautz [SZAL97] makes use of tablets as a prop-based input device, and has been used in collaborative AR work by Schmalstieg et al. [SCHM00] and Reitmayr and Schmalstieg [REIT01a]. The PIP is held in the hands and uses AR to overlay 3D widgets indicating various controls that can be adjusted with a hand-held pen (see Figure 2‑7). The tablet may be quite small and implemented using a variety of technologies such as pen tracking or pressure sensing, making it portable and easily carried. Another feature of the PIP is that it provides haptic feedback for the user as they press the pen against the tablet, in contrast to the hand-held widgets and tool palettes discussed previously. Lindeman et al. demonstrated that by providing passive-haptic feedback to the user in precise direct manipulation tasks, user performance is significantly increased [LIND99]. In this study, the best results were achieved when the user was able to hold a tablet in one hand and then press against it with a tracked finger. Other methods such as fixing the tablet to the world or having no haptic feedback produced lower user performance values.
Table 2‑3 lists a summary of the information presented concerning interaction techniques for virtual reality (and also augmented reality), comparing their features and limitations.
Table 2‑3†††††††† Comparison between forms of VR interaction techniques
Being able to capture the physical world into a digital model has become a critical part of modern professions such as surveying, building, and architecture. These areas have traditionally used paper to record information, but over time the amount of data required has increased and now computers are used to streamline these tasks. Brooks discusses the problems associated with the capture of physical world objects from a computer graphics perspective [BROO97]. One suggestion he makes is that an iterative refinement strategy is desirable, where the most resources are focussed on complex objects and not on those that can be approximated with no noticeable loss of detail. This section discusses various techniques used to capture physical world data into a computer.
Surveyors are responsible for measuring and capturing the geometry of landscapes for various uses such as construction and the division of property boundaries. Using a known reference point on the Earth, coordinates of other points may be found from relative orientation and distance measurements. Originally, surveying was performed by using measuring chains, where the chain is pulled between two points and the length is calculated by counting the number of links. The accuracy of the chain is affected by its physical properties as well as distortions caused by gravity. The angle is measured using a theodolite, which resembles a small telescope mounted onto a tripod. By aiming the theodoliteís crosshairs at a target, the angle can be mechanically measured relative to the base. Laser range fingers are now also used on theodolites to instantly measure distances without the use of chains, achieving accuracies in the order of millimetres. With integrated angle and distance measurements in theodolites, quick and accurate measurements can be performed in the field.
GPS technology has also improved since its introduction and is now widely used in the surveying field as well. As previously mentioned, RTK technology allows accuracies of 1-2 centimetres and is accurate enough to be used for surveying. Using a pole-mounted GPS, surveyors can instantly record the position of an object by placing the pole down at the location and pressing a button. The use of modern equipment such as GPS, theodolites, and laser range finders enables the surveyor to be more efficient and accurate compared to traditional techniques.
To capture the model of a building, the most basic method is to physically measure the structure with a tape measure, and then record the dimensions and geometry on paper. This information can then be used to recreate the object as a 3D graphical model using a desktop CAD system. The main problem with this technique is that it is very time consuming, as each point needs to be manually measured, recorded on paper, and then entered into a computer. This process is also prone to errors, and it will only be obvious during entry into the computer that a mistake has been made when points do not line up correctly. Errors require the user to repeatedly go back outside and make new measurements until the model is satisfactory. While existing plans for buildings can be used as 3D models, Brooks points out that in many cases these plans show the object as designed but not as actually constructed [BROO97]. Apart from just discussing the capture of models into the computer, Brooks argues that working with static world models is a substantial engineering task, similar in magnitude to a software engineering task. A rule of thumb he proposes that the complexity of an object can be measured by counting the number of polygons, similar to counting lines of source code when programming.
Figure 2‑21††††† Partial UniSA campus model captured using manual measuring techniques
(Image courtesy of Arron Piekarski)
At the start of this thesis work, Arron Piekarski used the manual techniques described above to capture a number of buildings on the university campus. This process took about a week to achieve the level of detail and accuracy required and was used to create the AutoCAD model shown in Figure 2‑21.
Images captured with cameras from two known positions may be used to reconstruct the 3D geometry of objects. Cameras placed at even slightly different locations will receive images differently due to perspective depth effects. By matching features between the images and measuring the differences in position, 3D mesh surfaces can be automatically produced. This technique can be applied using stereo cameras at a fixed distance apart or from a single camera that is moving along a known path (such as on an aircraft or a vehicle). Sester et al. describe a number of problems with these image-based techniques [SEST00]. Environments containing large height discontinuities or occlusion by other objects will prevent features being accurately measured. If the sampled images contain repetitive patterns or lack of unique textures then matching between images becomes difficult or impossible.
Stereo cameras can only capture a 3D surface from one particular view and cannot produce a fully closed model unless multiple surfaces are merged together. The FaÁade system by Debevec et al.† uses photographs taken from multiple view points to accurately capture the geometry and textures of objects such as buildings [DEBE96]. While the final models are completely closed surfaces compared to those produced by stereo image capture, the data must be processed offline and requires intervention by the user. The user manually specifies similar feature points between various images to assist with the processing. Image-based reconstruction is still an active research area and no fully automated techniques currently exist for arbitrary shaped objects.
Laser scanning devices are commonly used to capture large outdoor structures, with examples such as the commercially available I-SiTE [ISIT02] and Cyrax [CYRA03] scanners. These devices sweep a laser over an outdoor area and capture distance measurements, constructing a detailed 3D mesh of the area. Since the scanner cannot view all faces of the object it must be repositioned at different view points and the resulting 3D meshes merged together. The specifications for the I-SiTE scanner claim to measure a point every 30 cm at a distance of 100 metres, and to be able to reconstruct a large building from four different angles within one hour. Laser scanners suffer from occlusion by other objects, absorption of the laser on very dark surfaces, no reflections from sharply angled surfaces, and bright reflections caused by specular surfaces [SEST00]. For objects such as buildings, laser scanning produces much more accurate results than those available from image-based reconstruction, although the scanning devices are much more expensive.
An alternative technique usually deployed from aircraft or satellites is the use of Synthetic Aperture RADAR devices (SAR). SAR sends out RADAR pulses along a range of angles and measures the returning pulseís intensity and phase to form an image. Images are repeatedly captured as the SAR platform moves through the world, and are not obscured by clouds or trees since the visible light spectrum is not used. By matching features between images the phase differences can be used to calculate relative distances for each point on the image [SEST00]. This approach is mostly used for the capture of large areas such as mountains or cities and suffers from similar problems to stereo image reconstruction.
Stereo images, laser scanning, and SAR imaging all require line of sight with the particular light spectrum to capture the geometry of objects. Any objects that are occluding the model will introduce areas of uncertainty, and some objects may include features that are self occluding, such as the pillars in front of a building. Areas that cannot be scanned will form shadows in the 3D mesh that incorrectly represent the physical world shape. If the scanner cannot be repositioned at a different angle to see around the occluding object, it will be impossible to scan correctly. While multiple 3D meshes from different view points can be merged together to form a single mesh, this is difficult and requires accurate correspondences to be made.
Scanning techniques rely on the brute force capture of millions of data points to sample objects, but for flat surfaces there will be many unnecessary points and for areas with sharp changes in features there will not be enough points. For example, when modelling a house, the doors and windows each only need a single polygon but the frames around them require highly detailed geometry to represent their structure. The capture time for models also remains the same no matter what the complexity of the object is, whether it is a simple cube or a detailed sculpture. For simple capture tasks, it is not possible to spend only a couple of minutes to capture the approximate outline of a building since the key corner points may be missed by the scanner as the step size is increased. Large models with millions of polygons are also time-consuming to render or simplify on current hardware, and so these capture processes can require extra processing to achieve the desired geometry detail.
All of the described capture techniques in this section produce 3D output that is relative to the device that performed the capturing. To provide absolute positions for the 3D model, a GPS (or some other positioning device) must be used to measure the location of the scanner. The accuracy of the final world-relative model will therefore depend on the least accurate of all the devices in use. Finally, these techniques are limited to objects that already exist in the physical world. For capturing objects that do not exist, CAD modelling tools are needed for users to express their design and visualise it.
Computer Aided Design (CAD) systems are used to create accurate 2D and 3D representations of physical world objects. These systems form a core part of most design work, since the computer can perform a wide range of useful calculations and processes that help to reduce time and costs. Much research has gone into the development of these systems, and this section provides an overview of their use and underlying technologies that will be useful in later chapters of this dissertation.
Figure 2‑22††††† Screen capture of Autodeskís AutoCAD editing a sample 3D model
Systems used in commercial environments have evolved on 2D desktop-based machines, with a typical example of a popular system being Autodeskís AutoCAD [ACAD03] (see Figure 2‑22). Mine reviews a wide range of CAD systems and discusses their features extensively [MINE97b]. Simple drawing systems are capable of creating 2D vector-based primitives such as points, lines, arcs, and text. CAD systems extend these primitives to support other higher-level features such as layers, dimensioning, and template objects that can be used for complex 2D designs. 3D CAD systems can be used to model real life objects using solid modelling operations. These 3D models may be analysed before construction to ensure it meets the design requirements, and then possibly sent to an automated milling machine to produce a physical representation. There are many powerful CAD tools that can perform a wide range of tasks but these will not be discussed in this dissertation.
CAD systems are generally 2D applications that project the specified view point on to a display, and allow the user to draw and edit both 2D and 3D objects. Given only a 2D input device and a keyboard, CAD systems implement a number of techniques to enter in 3D information. Using direct keyboard entry of numerical values for 3D locations is the most exact input method since there is no ambiguity. An alternative is the use of multiple views from different angles, where the user can select the same point in each view and the 3D location is calculated through intersection. CAD systems also introduced a concept named working planes that is described by Mine [MINE97b]. Working planes are surfaces of infinite size that are placed into the modelling environment, and the 2D cursor on the display is projected against these. Given any 2D cursor position, the working plane can be used to calculate 3D coordinates for other operations. Working planes can be defined using numeric input, graphically while perpendicular to a view point angle, or relative to the surface of another object.
Working planes have features that can be explained best using an example of constructing a model of a house. Given an empty model, the 2D top down view is selected and a working plane is created at height zero. The user can click a series of points to define lines forming the perimeter of the house. The user switches to a side view and then extrudes the perimeter up to create a solid shape. Inbuilt objects supplied with the CAD system such as pyramids and wedges (or previous extrusion techniques) can be used to form the roof shape. Up to now this example has only used working planes that are perpendicular to the current view. The true power of working planes is most apparent when the object cannot be aligned to the view point. In this scenario, instead of using the coordinate system to specify working planes, an object facet itself can be used. To draw a window onto a wall of the house, the user nominates the wall and then simply draws against the surface. As each point is entered it is projected against the wall (working plane) and used to create a 3D vertex. If a picture is hanging on the wall, it can be moved along the surface of the working plane instead of on the plane perpendicular to the view point. Working planes act as a constraint mechanism that assists with the specification of three degrees of freedom using only a two degree of freedom input.
While a shape may be defined by specifying each vertex manually and then joined into polygons, this is a very time consuming process. In many cases, it can be seen that shapes contain surfaces similar to those in primitive objects such as boxes, cylinders, spheres, and cones. Using constructive solid geometry (CSG) techniques, CAD systems can take objects that are mathematically defined and combine them using Boolean set operations. In his detailed survey paper of the field of solid modelling, Requicha describes the foundations of using CSG representations for 3D objects in this way [REQU80]. An example of traditional Boolean set operations is depicted by the Venn diagrams in Figure 2‑23, with two overlapping closed 2D regions and various logical operators. The examples depicted are inverse, union, intersection, and difference operators.
Figure 2‑23††††† Venn diagrams demonstrating Boolean set operations on 2D areas A and B
For the examples depicted in Figure 2‑23, any kind of closed space (such as a 3D solid object) can be used instead. A point is defined as being part of a set if it is enclosed by the surface, and the region inside the surface is assumed to be a solid volume. Figure 2‑24 demonstrates similar Boolean set operations but using 3D solid objects and operating on a pyramid as object A and a sphere as object B. The union, intersect, and difference operators produce shapes that would be difficult to express otherwise and yet it is obvious what input primitives were used.
(A » B)
(A « B)
(A Ė B), (A « !B)
Figure 2‑24 CSG operations expressed as Boolean sets of 3D objects
(Images courtesy of Leonard Teo)
To simplify the calculations for computing CSG objects, the input objects should all be definable using mathematical surface equations. Surface equations can be used to generate polygon meshes and surface normals at any level of detail since they are continuous functions with well defined values. 3D shapes may be defined mathematically using equations that equal zero when the X, Y, and Z coordinates are on the surface of the object. For example, a sphere surface of unit dimensions can be defined using the equation x≤ + y≤ + z≤ - 1 = 0. If coordinates inside the sphere are used then the equation will return a negative value, and it will be positive for all points outside the sphere. The surface equation of a cylinder that is infinite along the Z axis and of unit radius in X and Y can be similarly represented using the equation x≤ + y≤ - 1 = 0. Similarly, the surface equation for a plane is Ax + By + Cz + D = 0 and when zero is calculated then the point lies exactly on the surface. The surface normal of the plane is represented in the equation using the vector [A, B, C]. An interesting property of the plane equation is that it is not enclosed since the plane moves off to infinity in all directions and so one would assume that it does not have an inside or outside. A plane does have an inside and outside though, since it possesses a surface normal vector for direction and cuts the universe into two halves (see Figure 2‑25). The portion above the plane is defined as outside while the portion below is inside. The surface equations previously defined can all be categorised as quadric surfaces, each expressed using the general equation Ax≤ + By≤ + Cz≤ + Dxy + Exz + Fyz + Gx + Hy + Iz + J = 0. Other more exotic shapes can be represented using this and other higher order equations, but will not be discussed here.
Figure 2‑25††††† Plane equation divides the universe into two half spaces, inside and outside
Figure 2‑26††††† Finite cylinder defined by intersecting an infinite cylinder with two planes
The definition of the infinite cylinder introduced previously is not useable for rendering since it is infinite in length while physical world objects are generally finite in length. What is desired is the ability to place end caps on the cylinder to limit its length, but this cannot be easily expressed using a single surface equation. A capped cylinder can instead be defined with the combination of a cylinder and two planes, the planes being used to provide the end caps. Using the layout shown in Figure 2‑26 it can be seen that the planes define a region bound along the Z axis, but infinite along X and Y, while the cylinder defines a region bound in X and Y, but infinite in Z. By combining these regions using the intersection operator, a new solid region that includes the common parts between all inputs is defined and appears as a capped cylinder. It is critical that the surface normals for the two planes are correctly placed pointing out from the cylinder, otherwise the final object will be non-existent since there is no common inside.
Using a similar technique as used previously, a box can be defined using six plane equations. Figure 2‑27 shows how these planes can be arranged so that their inside regions all overlap Ė when this area is intersected a box shape is formed. This construct is interesting because while a box shape cannot be defined using a single quadric surface, it can be defined using a collection of planes. Using CSG techniques, most shapes can be created given enough inputs and iterations, making this a very powerful way of expressing objects with only simple inputs.
Figure 2‑27††††† Box defined using six plane equations and CSG intersection operator
One drawback to CSG is that since the objects are defined using quadric equations, they cannot be directly rendered on typical 3D graphics hardware that supports only polygons. Requicha describes in detail the use of boundary representations made up of facets to represent solid models before and after CSG operations [REQU80]. Many algorithms have been developed for the calculation of CSG boundary representations, including real-time ones such as those by Laidlaw et al. [LAID86] and Thibault and Naylor [THIB87].
This section introduces the concept of a wearable computer, and how one can be used to perform outdoor augmented reality. Wearable computers are defined, input devices are reviewed, and then the problems associated with performing mobile AR are then discussed. Much of the information presented in this section is based on knowledge I have accumulated after designing mobile computers since 1998.
For the purposes of this dissertation, I will define a wearable computer to be a self powered computing device that can be worn on the body without requiring the hands to carry it, and can be used while performing other tasks. The first known instance of the development of a wearable computing device was in the late 1950s by Thorpe and Shannon [THOR98]. This device was used to predict the motion of roulette wheels at casinos and contained twelve transistors worn by the user, with a foot mounted switch as an input device and a small concealed earpiece for feedback. This was pioneering work because the wearable was completely hidden in clothing and able to pass the careful inspection of casino security staff.
Wearable computers have now progressed to the wearing of hardware with internal architectures similar to that available on standard computers, with systems developed by Carnegie Mellon University (such as VuMan [BASS97] and Spot [DORS02]) and the Massachusetts Institute of Technology (such as MIThreal [MITH03]). Systems are now commercially available from companies such as Xybernaut [XYBE03] and Charmed [CHAR03], and combined with a HMD are being deployed to assist workers with tasks that require information to be presented while keeping the hands free. Systems have been tested in the field with studies such as those by Siegel and Bauer [SIEG97] and Curtis et al. [CURT98]. Research such as designing for wearability by Gemperle et al. [GEMP98] and embedding wearables into business suits by Toney et al. [TONE02] are examples of research focusing on making computers a part of everyday clothing.
A key feature of a wearable computer is the ability for a user to operate the computer while being mobile and free to move about the environment. When mobile, traditional desktop input devices such as keyboards and mice cannot be used, and so new user interfaces are required. Thomas and Tyerman performed a survey of various input devices for wearable computers and how they can be used for collaboration tasks [THOM97a]. Thomas et al. evaluated three different input devices for text entry on wearable computers: a virtual keyboard controlled by trackball, a forearm keyboard, and a chordic keyboard [THOM97b]. While these devices demonstrated improvements in accuracy and speed after training the user, they still are not as efficient as a standard desktop keyboard. Although many devices have been developed for communication with wearable computers, there is still much research to perform in this area as the devices are still cumbersome and force the user to use unnatural interactions. Some currently available devices include:
∑ Chord-based keyboards (the Twiddler2 [HAND02] is shown in Figure 2‑28a)
Forearm-mounted keyboards (the WristPC [LSYS02] is shown in Figure 2‑28b)
Track-ball and touch-pad mouse devices (a generic track-ball mouse is shown Figure 2‑28c, and the Easy Cat touch-pad [CIRQ99] is shown in Figure 2‑28d)
Gyroscopic and joystick-based mouse devices (the Gyration wireless mouse [GYRA04] is shown in Figure 2‑28e)
Gesture detection of hand motions
Vision tracking of hands or other features
Figure 2‑28††††† Wearable input devices suitable for use in outdoor environments
(a) chordic keyboard, (b) forearm keyboard, (c) track-ball mouse,
(d) touch-pad mouse, and (e) gyroscopic mouse
Many wearables such as the ones discussed previously are small and can be concealed on the body, with construction that is able to survive daily use. When working with AR and interactive 3D graphics however, most wearable computers lack the computational power available on standard desktop and laptop systems to perform these tasks. Instead of using small and compact wearable computers, the applications presented in this dissertation require equipment that is bulky, power inefficient, and heavy, such as laptops, trackers, and batteries. If current trends in the miniaturisation of hardware continue, these devices will reduce in size to that of current wearable computers today. Since the equipment currently required is quite bulky, I have used a mobile backpack configuration similar to that used by Feiner et al. [FEIN97]. Although large and heavy, these are still wearable just like any other smaller computer, although are less comfortable and ergonomic.
The design of mobile backpack systems that can perform AR has a number of problems that are not experienced when working on a desktop computer. Designing a system that is powerful enough, uses mobile power sources, is able to work outside, and withstand tough environmental conditions is difficult and introduces tradeoffs. Azuma also discusses some of the problems associated with making AR work outdoors [AZUM97b]. The designs possible vary depending on the requirements, and some of the constraints are as follows:
∑ Weight and size - Wearable computers should not be a burden on the user to carry and use.
Power supply and run time - Components that have large electrical energy requirements need more batteries, which add to weight and size. The amount of time the system can run for is controlled by the amount of power supplied by the batteries and the efficiency of the components.
Performance - Calculations for rendering and tracking require large amounts of processing power, and to meet certain requirements larger and more energy intensive devices may be required. The power consumption of devices is directly proportional to clock speed and heat dissipation.
Ruggedness - Sensitive electronic equipment needs to be protected from the environment or it will be damaged easily. Connectors, cables, and components normally used indoors may be unsuitable outside due to forces caused by the user moving around, as well as being immersed in an atmosphere with dust, moisture, and heat.
Price - Cheaper devices are always desirable when possible.
The previous requirements are all interdependent. If the designer optimises for one particular category such as increasing performance, the weight, size, and price of the system will also increase. By optimising for low weight and small size, the wearable will not require as many batteries, but will result in the ruggedness protections being removed, diminished performance in the computer, and an increase in the price to pay for miniaturisation of the components. A standard design guideline is that when designing systems, it is possible to optimise for one case at the expense of most of the others, so there are always trade offs to be made. Mobile AR systems tend to be optimised for performance, with most other factors being sacrificed. This makes them large, energy intensive, extremely fragile, and very expensive. While technology is improving, there is still much progress to be made before these systems may be easily and commonly used.
HMDs operated in an outdoor environment also suffer from portability problems, as well as dealing with a wide and dynamic range of lighting conditions, from darkness to full bright sunlight, and effects such as shadows and reflections. For optical displays, bright lighting can cause the optical overlay to not be visible and can also enter the userís eyes via the sides of the HMD. Darkness causes the opposite effect with the physical world not easily visible while the overlay is very bright. When working with video overlay displays, video cameras are required that can operate over wide ranges of lighting. These cameras still do not have performance even approximating that of the human eye however.
This chapter has discussed the current state of the art in AR technology. While the current level of technology is very impressive, much of this technology has been around since the time of the first HMD by Sutherland in 1968. Although there have been a number of improvements and the quality of systems has increased substantially, there are a number of unsolved and important problems that prevent mainstream use of AR applications. This dissertation will address some of the problems with user interfaces and 3D modelling tasks for AR, with a particular focus on operating in mobile outdoor environments.
"If you can find a path with no obstacles, it probably doesn't lead anywhere"
Frank A. Clark
This chapter describes new interaction techniques for augmented reality to support manipulation and construction of geometry at large distances away from the user. Existing 3D techniques previously described in Chapter 2 extend the userís interaction beyond armís reach, but focus on operating at distances relatively close to the user where there are many cues available to accurately estimate relative position. This chapter references various studies to show that beyond a certain distance the ability of humans to perceive depth is severely attenuated. This affects the accuracy of interactions that can be performed at large distances, which are important when interacting in an outside world with structures beyond the depth perception capability of humans. A new technique named AR working planes is described, using the projection of 2D cursors onto 3D planes to avoid the specification of depth values directly by the user. This technique only requires the use of 2D inputs and so can be implemented using a wide range of input devices, making it ideal for use in mobile environments. The AR working planes concept is described in detail, discussing their placement in various coordinate systems and creation relative to the user or the world. The use of AR working planes for action and construction at a distance is then described, including the manipulation of existing objects and the placement of vertices to create new geometry. To perform operations using AR working planes, it is important that the plane is correctly aligned with the physical world to ensure the accurate capture of information. I demonstrate that an accurate way to perform this is by using the eye to align physical world features and therefore ensuring the body and head are correctly placed. Using the AR working planes technique developed in this chapter, the human is capable of performing interactions that are limited only by the accuracy of the tracking equipment in use and not by their lack of depth estimation capabilities.
Figure 3‑1††††††† 3D objects are projected onto a plane near the eye to form a 2D image
Humans gauge the distance to objects and their layout through visual cues acquired with the eyes, along with any other available senses such as sound, touch, and smell. The human sense of vision is unique in that it is capable of gathering information from a virtually infinite range of distances, whereas other senses tend to be useful only within close range. Human vision can be approximately modelled as a 2D array of pixels (similar to a video camera) gathering light to produce a 2D image representing the 3D environment. While horizontal and vertical placement of objects in the image is easily obtainable, depth is ambiguous due to the flattened representation of the image, as depicted in Figure 3‑1. Depth information can only be estimated by analysing the contents of the images captured. The eyes and brain process a number of vision cues that occur in images to determine the depth positioning of objects in the scene, and are combined together to improve accuracy. Drascic and Milgram [DRAS96] present a survey of perceptual issues in AR, discussing various depth cues and how mixed reality systems are limited in presenting them to the user. Cutting and Vishton [CUTT95] followed by Cutting [CUTT97] [CUTT02] provide detailed surveys on previous work in the area of perception and the determination of distance relationships between objects using visual cues. Cutting and Vishton collected results from a large number of previous studies and categorised nine cues (rejecting another six), describing the range they are accurate over and the kind of depth information that can be extracted. Not all visual cues can produce absolute measurement information however; some cues can only provide relative ratios between objects or simple ordering information. The nine cues described by Cutting and Vishton [CUTT95] are as follows:
∑ Occlusion Ė when objects at varying distances are projected onto the retina of the human, objects that are closer will overlap objects that are further away. This allows ordering information to be extracted and works over any distance, but cannot be used to form any absolute measurements.
Relative size Ė by measuring the size of a projected image on the retina and knowing that two objects are of a similar size, both ordering and size ratios can be calculated but without absolute values. No prior knowledge of the objectís size is required for this cue except that the objects are of the same size, and can be placed at any visible distance.
Relative density Ė this cue is similar to relative size and uses two similar objects (but of unknown sizes) and compares the density of textures that are placed on them. By comparing the texture patterns, ordering and size can be calculated, although absolute values are still not possible. This cue is also useable at any distance, assuming the objects are visible.
Height in the visual field Ė this cue relies on gauging the distance of objects by comparing their heights relative to each other. Assuming the objects are all placed onto the ground plane, that the eye is at a known height, and that the ground is indeed flat, then this cue can produce absolute distance measurements. In most cases however, not all the previous conditions can be met and so only ordering is available. This cue is only effective from about 2 metres onwards as the human must be able to see the objects touching the ground plane.
Aerial perspective Ė when objects such as mountains and buildings are at very large distances, environmental effects such as fog, lighting, and distortion begin to affect the image of objects. As distance increases, the image of objects becomes gradually more attenuated and so this can be used as a measure of distance. Since this cue is only effective at large distances, calculating absolute values based on the attenuation of objects may be difficult since they might not be easily visible.
Motion perspective Ė when moving sideways through the environment, images of objects that are at a distance will move across the retina slower than closer objects, caused by perspective distortion. This cue attenuates over distance and works best when the eye can easily focus onto the objects in motion; therefore objects that are so close that they move by very quickly will be difficult to process. While absolute distances may be extracted given knowledge of the movement and height of the eye, motion perspective is best able to be used for relative ratios and ordering.
Convergence Ė when objects are in close range, the eyes will adjust their angle to point toward the object of interest. As the distance increases, the angle of the eyes gradually widens to the point where they are both looking in parallel directions when an object is at very large distances. Convergence requires knowledge of the distance between the eyes, and when used within a range of about two metres, this cue is able to give accurate absolute distance measurements.
Accommodation Ė in order to perceive objects clearly, the eyes will focus the image by adjusting internal lenses controlled by a muscle, similar to a camera. This cue can be used to calculate distance for an object, and is usually combined together with convergence. Accommodation operates up to approximately two metres, although the eyeís ability to focus deteriorates with age. Similar to convergence, absolute distance information within its limitations can be calculated.
Binocular disparities Ė when two eyes are both focused on the same object, if it is within a close range the images presented to each eye will vary slightly. Using the eyes in stereo may capture depth information with absolute values assuming the distance between the eyes and convergence is known, and also correspondences between points in the images can be found. This cue produces absolute distances from very close ranges and attenuates linearly as distance increases.
Cutting and Vishton also mention a number of other cues discussed in various literature, but eliminate them from consideration because they are based on the previously identified cues, or not demonstrated as being effective during user studies. In normal daily life, the brain combines these cues together to produce situational awareness for the human. In VR environments, some of these cues can be simulated with the use of HMDs. HMDs can produce stereo images with offsets to match the distance between the eyes, and software can simulate fog and some environmental effects. While stereo HMDs give the user some feeling of depth perception, this is limited because the brain may be confused by inconsistencies in the sensor information normally acquired.
To summarise the various cues and their effectiveness at different distances, Cutting and Vishton produced a graph depicted in Figure 3‑2 that indicates the accuracy of each cue. This figure uses a log scale for distance along the X axis, and a normalised log scale along the Y axis with the smallest distance change measurable divided by distance. A value of 0.1 on the Y axis may indicate the ability to discern a 1 metre change at a distance of 10 metres, or a 10 metre change at a distance of 100 metres. Each of these curves is based on the data from numerous previously performed user studies and demonstrates that each cue is effective at different distances.
Based on their analysis of the nine available cues, Cutting and Vishton defined three separate spaces around the body at different distances to better categorise the depth estimation available. The first area defined is named personal space and ranges from the body to up to 2 metres. Personal space is where humans perform most of their close up interactions, and so depth perception is highly refined due to its importance in daily life. From 2 to 30 metres is a second area termed action space. In this space, users may interact reasonably accurately with other objects (such as throwing a ball to hit a target), but with less cues and accuracy than personal space. Beyond 30 metres is vista space, where objects appear flat and distance estimations become quite poor compared to closer spaces. Figure 3‑2 includes divisions showing where the three spaces are located relative to the accuracy curves previously described.
Figure 3‑2††††††† Normalised effectiveness of various depth perception cues over distance
(Adapted from Cutting and Vishton [CUTT95])
Based on this discussion, it seems that a humanís ability to reconstruct 3D information about a scene is most capable when operating within close range to the body. Since humans mainly deal with objects that are within armís reach, this sense (named proprioception) is highly refined and was used by Mine to improve user interfaces for 3D environments [MINE97a]. At larger distances however, these abilities attenuate very rapidly to the point where beyond 30 metres or so it is difficult to perceive absolute distances. When modelling large outdoor structures such as buildings, distances of 30 metres or greater are quite common. If distances cannot be perceived accurately for the modelling tasks required, then performing action and construction at a distance operations will require extra assistance to be useable.
Figure 3‑3††††††† Graph of the size in pixels of a 1m object on a HMD plane 1m from the eye
Previously described techniques such as working planes [MINE97a], selection apertures [FORS96], and image planes [PIER97] were developed to project the locations of display-based cursors onto a 3D environment. These techniques are useful for selection and manipulation operations in vista space because there are no restrictions on the range of use, and the techniques are just as easy to use within armís reach or kilometres away. Image planes and selection apertures are not capable of specifying distance however, since the plane from the view frustum is used for the cursors and depth is not required to be resolved. Assuming a typical perspective projection, the accuracy of vertical and horizontal motion in all of these techniques is proportional to the size and distance of the object, but attenuates at a constant rate less than that of human depth perception. With the use of HMDs, the cursor is represented using pixels and introduces a pyramid of uncertainty specified by the pixel size at the projection plane. To simplify this argument, I will ignore anti-aliasing effects that may occur when points and lines are drawn smoothly onto pixel arrays. Figure 3‑3 plots the effect of distance on the projection of a 1 metre object onto a Sony Glasstron PLM-700E HMD with pixels of size 0.618 mm at 1 metre from the eye (the derivation of this value is described in Section 3.8). From Figure 3‑3, it can be observed that the 1 metre object is not properly visible beyond approximately 1618 metres since it is less than a single pixel in size. An important property of interactive modelling is that users can only perform manipulations that are visually verifiable. There is no need to provide the user with the capability to move a mountain on the horizon 5 centimetres to the right because it is not visually noticeable. Only by approaching the object will the user notice any accuracy problems, and these can then be corrected since it is a change that can be verified. Based on this argument, the use of projection techniques imposes no accuracy limitations noticeable by the user.
Using the previously discussed projection concepts, these can be extended into the AR domain to perform interactive modelling outdoors. I have developed a concept named augmented reality working planes that is based on the working planes concept used in traditional CAD systems. AR working planes can be created in the environment relative to the user or other objects, and stored in one of four possible coordinate systems. These planes can then be used as a surface to project a 2D cursor on to, resolving full 3D coordinates to manipulate existing objects and create new vertices. Since planes are by definition infinite in size, the user can project the cursor onto the plane from almost any location, although the accuracy decreases as the plane becomes parallel to the userís view. AR working planes improves on existing image plane-based techniques because the plane can be any arbitrary surface, allowing the calculation of depth at any distance and interaction in all three dimensions. This technique is also a mobile alternative to desktop CAD systems because the 3D view and working planes can be specified using the body in the physical world. To control the cursor projected against the AR working plane, any 2D input device can be used. The cursor is projected onto the surface of the plane and so no depth information is required, allowing a wide range of input devices to be used. Chapter 5 focuses on the implementation of a mobile input device suitable for use with AR working planes.
The use of AR working planes does impose some limitations on the user, and requires them to specify distance by creating a plane and then drawing against it from a different direction. Two operations from separate locations and orientations are usually required so that depth can be extracted without requiring the user to estimate it. While Chapter 2 reviewed previous research by Liang and Green that indicated that the decomposing of 3D tasks into 1D or 2D units was not efficient [LIAN93], in the scenario of working in vista space there is no alternative. As a support of my argument, Ware [WARE88] and Hinckley [HINC94a] both state that reducing degrees of freedom is useful when it is hard to maintain precision in certain degrees while adjusting others. In vista space, depth estimation is poor and so removing this degree of freedom is the best option to preserve accuracy.
In CAD systems, working planes can be placed in the environment using exact numeric keyboard entry, by drawing the planeís cross section from a perpendicular view, or by selecting another objectís facet [MINE97b]. The first two cases may be difficult and unintuitive because people think in terms of objects relative to their body rather than abstract coordinate systems and view points. My extensions to working planes for AR can create these planes using the userís body, making them much more intuitive to use when operating outdoors. An important improvement is that these AR working planes can be created and fixed to a number of coordinate systems that humans intuitively understand.
Figure 3‑4††††††† Coordinate systems used for the placement of objects at or near a human
Feiner et al. discuss the presentation of information in AR displays and how this information can be in surround-fixed, display-fixed, or world-fixed coordinates [FEIN93b]. As the user moves around the virtual environment, information in each coordinate system will be displayed differently. By selecting an appropriate coordinate system for each type of information, it can be more intuitively understood by users. Mine and Brooks discuss the placement of tools such as menus and tool palettes relative to the body, and how the user can find these easily since they are carried around relative to the user [MINE97a]. Using these concepts, a number of different coordinate systems can be identified that are suitable for performing modelling tasks, as depicted in Figure 3‑4. I have named these coordinate systems world, location, body, and head. In Figure 3‑4, the user operates in a world coordinate system that is anchored to some fixed point in the physical world. Using a positioning device, location coordinates are measured relative to world coordinates and represent the location of the userís feet but without direction. Using an orientation sensor mounted on the hips, body-relative coordinates can be calculated by applying an offset to transform from the feet to the hips and then applying the orientation. Head-relative coordinates are similarly calculated with the appropriate height and orientation of the userís head. The height values used for body and head coordinates can be either measured once and stored as a constant, or captured from a tracking device. I have only identified these coordinate systems as the main ones of importance for this research, but there are many others if appropriate tracking devices are available.
Information can be stored relative to any of the coordinate systems described in Figure 3‑4. The surround-fixed windows by Feiner et al. map to body-relative, display-fixed windows map to head-relative, and world-fixed windows map to world-relative. The menus and tool palettes floating about the user implemented by Mine and Brooks map to body-relative. Using the coordinate systems defined here, I extend the concepts of Feiner et al. to include not only the presentation of information, but also the placement of AR working planes so that points may be created and objects manipulated at a distance. This section describes AR working planes that have been created relative to each of the four coordinate systems and the effect that user motion has on the created planes. Although body-relative coordinates are described here, they are not implemented in later chapters since no sensor is used to measure body rotation, and is included only for comparisons to existing work. Based on the orientation and position sensors that I have used, figures are used to show the effect on each AR working plane of body translation, head rotation, or combination movements in the environment.
Translate / Head Rotate
Figure 3‑5†† World-relative AR working planes remain fixed during user movement
World coordinates are the top-level coordinate system used to represent positions over a planet or other large areas of interest. Objects that are specified relative to the origin of the world coordinate system are anchored to a fixed place in the physical world, and are completely independent of the userís motion, as depicted by (1) in Figure 3‑4. In virtual environments, most objects are created world-relative since they are not attached to the user and may move independently, with examples being buildings, trees, and automobiles. The userís coordinate systems are also specified in world coordinates, since their position and orientation are returned from tracking devices that are world-relative. Figure 3‑5 depicts a user moving in the environment with the AR working plane remaining since it is in coordinates independent of the user. World-relative AR working planes are commonly used when working with buildings and the user desires to keep the planes fixed relative to the walls at all times.
Translate / Head Rotate
Figure 3‑6†† Location-relative AR working planes remain at the same bearing from the user and maintain a constant distance from the user
Location coordinates are derived by taking the current position of the user from a tracking device and adding this to the origin of the world coordinate system. The axes for both location and world coordinates are still aligned except there is a translation offset between the two, as depicted by (2) in Figure 3‑4. With location coordinates the orientation of the user has not been applied, and so any changes in rotation will have no effect. An object placed in location-relative coordinates will always appear at the same true compass bearing from the user and maintain the same distance during motion. Location-relative coordinates are particularly useful for displaying an immersive compass to the user - the compass labels are attached around the user at a fixed radius and stay at the same orientation no matter what direction the user is looking. Another use is to attach a virtual camera at a fixed distance and direction from the user at all times, which follows the userís location but does not move with head or body rotation. Figure 3‑6 depicts the effects of user motion on an AR working plane that is location-relative, where the plane moves with the user around the world. With user translation the plane moves with the same transformation, but rotation has no effect. The main uses for location-relative coordinate systems are the placement of vertices and object manipulation at fixed orientations. These fixed orientations are useful when working with buildings, keeping the AR working plane parallel to the walls but still moving relative to the user.
Translate / Head Rotate
Figure 3‑7†† Body-relative AR working planes remain at a fixed orientation and distance to the hips and are not modified by motion of the head
Although it is possible to define any number of coordinate systems, this will not be performed since in many cases it does not make sense to create objects relative to arbitrary parts of the body. A userís sense of proprioception is focused about its main components such as the hips and the head, and so these will be the main focus. Body coordinates are defined relative to location coordinates except that orientation of the hips is added, as depicted by (3) in Figure 3‑4. Objects placed in body-relative coordinates will always appear in the same location-relative to the hips as the user moves around, with a good example being a tool belt worn by a worker. When walking around or when moving the head, the tool belt always remains in the same fixed position, ready to be accessed by the hands. Body-relative differs from location-relative in that the rotation of the hips affects the attached objects, whereas location-relative ignores any rotations by the user. The cockpit of an aircraft is also similar, where controls are always at the same location-relative to the userís hips but the aircraft can fly around and keep the controls mapped to the same locations. Figure 3‑7 depicts the effects of user motion of the body on an AR working plane that is body-relative, where the plane is attached to the hips of the user as they move around the world. Although body coordinates are very intuitive within armís reach due to proprioception, they become more confusing at further distances since extra visual inspection is usually required. Some possible uses for body-relative coordinate systems are the placement of tools on a belt for easy access and display of non-critical status information.
Head-relative coordinates are similar to body-relative in that they add rotations to the location-relative coordinates, and can be defined relative to either location or body coordinates, as depicted by (4) in Figure 3‑4. The only difference between head-relative and body-relative coordinates is the part of the body that the information is attached to. Objects placed in head coordinates will always appear in the same location-relative to the userís head, with a good example being a floating status indicator on a HMD. No matter what the position or orientation of the user, the status indicator will always be visible at the same location. Figure 3‑8 depicts the effects of user motion of the head on an AR working plane that is head-relative, where the plane is attached to the head of the user as they move around the world. When the user moves through the world, the plane will be translated and rotated to remain fixed within the field of view. The main use for head-relative coordinate systems is the placement of display status information and object manipulation. Head-relative mode is the most natural choice for object movement since it allows the user to adjust all three degrees of freedom by moving the body.
Translate / Head Rotate
Figure 3‑8†† Head-relative AR working planes remain attached to the head during all movement, maintaining the same orientation and distance to the head
In order to take advantage of AR working planes, the plane must first be created in the environment. During creation, AR working planes must be located in one of the coordinate systems defined earlier, which will affect the operations that can be performed. This section discusses different methods of creating planes that may then be used for manipulation and vertex creation.
Figure 3‑9 depicts a user creating a plane originating from the userís head, parallel to the direction that the head is viewing. If the user is viewing in the direction of true north, then the plane will be infinite in the north and south directions, with east and west divided by the plane. Constraints may be applied to the orientation of the head so that only some degrees of freedom are used to create the plane. Since AR working planes are only useful when facing the user for cursors to be projected onto it, the user must be able to move independently of the plane to new viewing locations. This method is only relevant with world-relative coordinates since the plane is decoupled from the userís motion.
Figure 3‑9††††††† AR working plane created along the head viewing direction of the user
Figure 3‑10††††† AR working plane created at a fixed offset and with surface normal matching the view direction of the user
Figure 3‑10 depicts a user creating a plane that is located at a fixed distance away and with surface normal matching the userís view direction. If the user is viewing in the direction of true north, the plane will have a surface normal pointing north and be infinite in the east and west directions. Constraints may be used to restrict the degrees of freedom of the orientation of the head for creating the plane. Since the plane is facing the user it is ready to draw on and is suitable for use with all coordinate systems defined previously. The limitation of these planes is that the distance from the user must be specified with another input method, and the user may not be able to perform this accurately.
This technique is very similar to the previous in that the planeís surface normal is based on the userís view direction. The difference is that the plane is created so that it passes through the intersection point a user has selected on an object in the world. Figure 3‑11 depicts a user creating a plane at the intersection point of an object. These planes are most useful when created in head-relative coordinates for manipulation operations, although any other coordinate system is also possible.
Figure 3‑12 depicts a plane created to match the surface of a nominated facet on an object. Each of the objects has an AR working plane that is coincident with the selected facet, making it invariant to the userís current position and orientation. As long as the object facet is visible and can be selected, then it can be used to spawn an AR working plane in the environment. Since the plane is created visible to the user it is immediately ready to draw on and is suitable for use with all coordinate systems. World coordinates are the most logical usage however, since the planes are defined relative to an object that is typically in world coordinates. Uses for other coordinate systems are discussed in the next sections.
Figure 3‑11††††† AR working plane created at intersection of cursor with object, and normal matching the userís view direction
Figure 3‑12††††† AR working plane created relative to an objectís surface
Using a similar technique to that discussed previously, the facet of an object may supply a surface normal for an AR working plane created at another object. Figure 3‑13 depicts a plane created at the point where the userís cursor projection intersects an object in the environment. The surface normal is copied from an object selected previously with the same method. This technique is useful for manipulating objects relative to the surfaces of others and so is the most logical with world-relative coordinates, although other coordinate systems are possible as well.
Figure 3‑13††††† AR working plane created at a nominated object based on the surface normal of another reference object
Figure 3‑14††††† Manipulation of an object along an AR working plane surface
Figure 3‑15††††† Depth translation from the user moving a head-relative AR working plane
Figure 3‑16 AR working plane attached to the head can move objects with user motion
Figure 3‑17††††† Scaling of an object along an AR working plane with origin and two points
Figure 3‑18††††† Rotation of an object along AR working plane with origin and two points
Given the ability to place down AR working planes in the environment, one possible use is the implementation of translate, scale, and rotate operations. The first step is to create a working plane in the environment using one of the previously described techniques relative to an appropriate coordinate system. The choice of coordinate system determines the type of operations that can be performed. When using AR working planes in head coordinates, these techniques share similar properties to selection using image plane [PIER97].
Translation operations where the object is accurately moved across the AR working plane surface can be performed as shown in Figure 3‑14. Two points are projected onto the plane and are used to calculate a translation. This translation is then applied to the object to move it to the desired location, with the offset always being along the surface of the plane. If the AR working plane is attached to the location, body, or head then varying the userís position will drag the object around, as depicted in Figure 3‑15. When using body or head coordinates, translations and rotations can be combined together, such as depicted in Figure 3‑16. By combining these techniques with cursor motion along an AR working plane, complex manipulation operations can be performed.
Scaling operations can be performed along the surface of an AR working plane and requires three input points Ė an origin for the scaling operation, and two points to specify a direction and magnitude vector. The two cursor points are used to calculate a new scaling transformation relative to the origin and then applied to the object, as depicted in Figure 3‑17.
Figure 3‑19††††† Vertices are created by projecting the 2D cursor against an AR working plane
Figure 3‑20 AR working plane attached to the head can create vertices near the user
Rotation operations can be performed about the surface normal of an AR working plane with three input points Ė an origin for the axis of rotation, and two points to specify an angle. The two cursor points are used to calculate a new rotation transformation relative to the axis of rotation and then applied to the object, as depicted in Figure 3‑18.
The second more novel use for AR working planes is the placement of points in the environment. Selection and manipulation of existing objects has been implemented previously using a number of techniques, but there is still a lack of techniques for the creation of new geometry at a distance. Figure 3‑19 depicts how a user can project the cursor against an AR working plane and create vertices anywhere on the surface. Similar to the previous object manipulation section, this operation can be performed using an AR working plane in any coordinate system and created using any technique.
Apart from just creating points against fixed surfaces, if the AR working plane is relative to user coordinates then it will move with the motion of the user, as depicted in Figure 3‑20. As the user translates and rotates, the AR working plane will also move and points will be created in world coordinates against the current surface. While this technique may be used to create complex collections of vertices, this can be tedious for many objects. Chapter 4 will introduce techniques designed to simplify the creation of object geometry given certain assumptions.
When creating AR working planes using the position and orientation of the body, it is important that the user be as accurately placed as possible. To create vertices specifying the outline of a building, working planes must be created that are in alignment with the walls. Any errors in the placement of the working planes will cause projected vertices to deviate from the true physical wall surface.
The eye is an incredibly accurate measuring device that can notice even minute shifts between two objects that are in alignment. While fishing offshore with my father, I was shown how to look at large features along the coastline such as hills, towers, and buildings. When a fishing spot was discovered that we would like to come back to, my father would look along the shore to find landmarks that were visually aligned. After selecting aligned landmarks, these would then be recorded in his diary, producing a diagram similar to the example in Figure 3‑21. Lining up two landmarks would place the boat along a particular bearing, and then lining up a further two landmarks along another bearing would fix the position of the boat down to the intersection of the two lines. We could accurately find previous fishing spots within a few metres accuracy without the use of any tools except visual inspection using the eye. Even when using his GPS unit, my father would only use it to get within its 5-10 metre accuracy and then use line of sight techniques to improve the position of the boat. The alignment of landmarks varies even when walking around the boat and so performing measurements from the same seating position is required to achieve the best accuracy. The main difficulty with this technique is that it is limited to spots where landmarks can be found to align. Books for amateurs by Pescatore and Ellis [PESC98] and the web site by Poczman [POCZ97] are examples of collections of fishing locations around Adelaide marked using this technique.
Bowditch describes similar techniques used by professional sailors when navigating close to shore [BOWD02]. Figure 3‑22 shows the placement of official navigational aids named range lights, which are used to indicate safe channels that boats can travel along. In many harbours there are obstacles that can easily damage ocean vessels, and so by keeping the range lights aligned the navigator can keep the ship very accurately in the marked channel without straying off course.
Figure 3‑21††††† Example fishing spot marked using various shore-based landmarks
(Sketch courtesy of Spishek Piekarski)
Figure 3‑22††††† Example of range lights in use to indicate location-relative to a transit bearing
(Adapted from Bowditch [BOWD02])
The alignment of landmarks can also be performed using a video-based HMD but with reduced accuracy compared to the eye since the resolution is much lower. According to Rose, the human eye has the capability to resolve single dots at approximately 1-2 minutes of arc [ROSE73], although the brain is capable of achieving resolutions at least one order of magnitude higher by processing the image further. In comparison, the HMD described in this section has a resolution of approximately 2 minutes of arc with no further enhancements possible. This section uses the easily measurable properties of a HMD to simplify the calculations, as the human vision system contains a wide range of processing that is not fully understood and difficult to model.
Figure 3‑23††††† Sony Glasstron HMD measured parameters and size of individual pixels
This section uses geometry to prove that the alignment of landmarks is useable with a video overlay mobile AR system to assist with the specification of planes in the environment. With landmark alignment, the creation of planes is limited mainly by the tracking equipment and not by the userís perceptive capabilities. Using the known parameters of a HMD and using the distance to two marker objects from the user, the maximum sideways translation the user can move without observing a change in alignment can be modelled. To simplify the calculations, subtle visibility effects that occur at sub-pixel levels when two objects appear to visually interact with each other will be ignored.
Figure 3‑23 depicts the layout for a Sony Glasstron PLM-700E HMD, which has a resolution of 800x600 pixels projected onto a focal plane 1.25 metres from the userís eye. The perceived image has approximate measured dimensions of 0.618 metres by 0.464 metres at the focal plane. Given this layout information, the size of each pixel may be calculated using similar triangles. Each pixel is approximately square and so is 0.773 millimetres in width and height at 1.25 metres. At a normalised focal distance of 1 metre, the pixels are 0.618 millimetres in width and height. Since each pixel is assumed to be square, normalised horizontal and vertical sizes are both represented using D.
If a landmark at some distance is to be visible on the HMD, it must be projected onto at least one pixel (or a significant portion of a pixel) on the display. Given the previous distance of 0.618 mm for a pixel at one metre, this can be extended out for any distance with similar triangles. For example a 100 metre distant marker must be 61.8 mm wide to be visible as a single pixel on the HMD. Using this concept, a diagram of similar triangles can be drawn (see Figure 3‑24) with a marker A of width aD at distance a, and marker B of width bD at distance b. The minimum required size of these markers is proportional to the distance from the HMD.
Figure 3‑24††††† Distant landmarks must be a minimum size to be visible on a HMD
Figure 3‑25††††† Dotted lines indicate the angle required to separate the two markerís outlines
When the user and both markers are in line, there will be an exact overlap between the markers, and when viewed separately, each will form an image on the HMD that is exactly the same size. If the user moves sideways any distance at all, the objects will no longer overlap (as in Figure 3‑22) and appear to gradually separate apart. The goal of the following calculations is to estimate d from Figure 3‑25, the distance the user must move so that the projections of both markers no longer overlap (with a small gap between), ensuring visibility on the HMD. The distance d also represents the error in positioning possible using line of sight techniques, and can help to analyse their usefulness. The diagonal dotted line in Figure 3‑25 depicts the line that the user must look along to notice a separate pixel for each marker. To simplify the calculations, I assume that the markers appear on the display small enough that the geometry can be treated as straight lines rather than arcs. This is possible given the small size of the pixels in millimetres and the large distance of the markers in metres. Based on the dotted lines from Figure 3‑25, Figure 3‑26 depicts the arrangement of the similar triangles that need to be solved to estimate the error distance.
Figure 3‑26††††† Similar triangles used to calculate final positioning error function
Table 3‑1†††††††† Alignment accuracies for markers at various distances from the user
Using the similar triangles in Figure 3‑26, the equations can be derived to calculate a final equation that represents the accuracy d of this technique, shown in Figure 3‑27. This final error equation is useful because it allows a simple analysis of the accuracy of landmark alignment over a variety of distances. A single constant is used to linearly scale the equation depending on the pixel size calculated earlier. As the markers both approach the same distance, the technique rapidly increases errors due to an asymptote in the function when a=b. However, when the markers are sufficiently spaced apart from each other, the accuracy of the technique is quite incredible considering the distances involved. Table 3‑1 demonstrates this with the accuracies achieved using markers placed at different distances from the user.
Figure 3‑27††††† Rearrangement and simplification of final positioning error equation
Figure 3‑28††††† Derivation of alignment equation when marker B is at an infinite distance
When working in a 3D environment, the tracking hardware will also impose limitations on the measuring accuracy of the system. If the landmark alignment is more accurate than that of the tracking hardware, it will be adequate for the required modelling task. Figure 3‑29 depicts a 3D surface with contour lines for the accuracy equation and is capped at the 2 cm limit of a Real-Time Kinematic GPS unit. Figure 3‑30 depicts a similar 3D surface restricted to the 50 cm accuracy obtainable from a high quality differential GPS unit. The sloped regions indicate distances where the accuracy of the technique is within the performance of the respective GPS units. As an example, using a building with corners at 100 metres and at 150 metres, this gives an accuracy of 18.54 centimetres that is within the accuracy of a 50 cm high quality GPS unit. This accuracy is quite poor compared to the 2 cm accuracy of an RTK GPS unit however, and to achieve accuracies better than 2 cm the near corner must be closer than 22 metres (therefore the far marker must be closer than 72 metres). These values may be calculated using the equation in Figure 3‑27.
Figure 3‑29††††† 3D surface plot with marker distances achieving alignment accuracy of 2 cm
Another property of landmark alignment is that as one landmark approaches an infinite distance, the slope of the 3D surface begins to match a linear approximation, most visible in Figure 3‑30. This slope is then only controlled by the distance of the closer marker, and as it moves toward the user the technique becomes more accurate. This property is useful when working with very long buildings at a close distance for example, where the further marker is so distant that only the close marker affects the accuracy of the technique. The equation in Figure 3‑27 can be rewritten into Figure 3‑28 to calculate the error in this case by using a limit with marker B approaching an infinite distance.
The previously discussed equations and graphs show that by visually aligning landmarks through a HMD, very accurate positioning of the body can be obtained. While a humanís ability to perceive depth rapidly attenuates as distance increases, the landmark alignment process is highly accurate over any distance given visible markers at a suitable distance apart.
Figure 3‑30††††† 3D surface plot with marker distances achieving alignment accuracy of 50 cm
Existing techniques for VR have been developed mainly to solve the problem of manipulating existing virtual objects at a distance, and do not address the problem of creating new vertices and geometry that are out of armís reach. This chapter demonstrated that a humanís ability to perceive depth rapidly attenuates in the vista space beyond 30 metres, making it difficult to correctly specify distances. Accurate depth specification is required to perform the modelling of large outdoor structures and these are almost always within vista space, and so suitable techniques are required to overcome the limitations of humans. I developed the concept of augmented reality working planes based on previously developed CAD and VR systems, performing the projection of 2D cursors onto 3D surfaces to specify depth information. AR working planes restricts degrees of freedom that the user is not capable of specifying accurately, and breaks the operation into logical tasks that can be easily understood by the user. AR working planes can be created using a number of methods, stored relative to world, location, body, and head coordinates, and used for object manipulation and vertex placement. By implementing working planes in AR, I take advantage of features that are only possible with the physical presence of the user in the environment. By using accurate positioning based on the alignment of objects in the environment, operations can be performed at large distances with only minor accuracy degradation caused by the user. AR working planes is a core concept for outdoor modelling used to support action at a distance, and the construction at a distance concept introduced in the next chapter.
ďApplying computer technology is simply finding the right wrench to pound in the correct screwĒ
This chapter presents a series of new augmented reality user interaction techniques to support the capture and creation of 3D geometry of large outdoor structures. I have termed these techniques construction at a distance, based on the action at a distance term used by other researchers. My techniques address the problem of AR systems traditionally being consumers of information, rather than being used to create new content. By using information about the userís physical presence along with hand and head gestures, AR systems may be used to capture and create the geometry of objects that are orders of magnitude larger than the user, with no prior information or assistance. While existing scanning techniques can only be used to capture existing physical objects, construction at a distance also allows the creation of models that exist only in the mind of the user. Using a single AR interface, users can enter geometry and verify its accuracy in real-time. Construction at a distance is a collection of 3D modelling techniques based on the concept of AR working planes, landmark alignment, CSG operations, and iterative refinement to form complex shapes. The following techniques are presented in this chapter: street furniture, bread crumbs, three types of infinite planes, CSG operations, projection carving, projection colouring, surface of revolution, and texture map capture. These techniques are demonstrated with a number of examples of real objects that have been modelled in the physical world, demonstrating the usefulness of the techniques.
Current research in AR applications (as discussed previously in Chapter 2) has focused mainly on obtaining adequate tracking and registration, and then developing simple interfaces to present display information to the user. One important problem that has not been properly addressed is the authoring of the content that is displayed to the user. Since most AR systems are being used simply as a visualisation tool, the data is prepared offline with standard editing tools and then transferred to the AR system. Brooks states that one of the still unsolved problems in VR is the creation and capture of 3D geometry [BROO97], which is also relevant for AR models. To develop content for AR systems, I have developed a number of techniques I collectively termed construction at a distance. These techniques use the AR system itself to capture the 3D geometry of existing structures in the physical world, and create new 3D models for virtual objects that do not yet exist. Construction at a distance makes use of the AR working planes and landmark alignment techniques discussed previously in Chapter 3, and builds higher-level operations to perform the capture and creation of 3D models. This section describes some of the main features of my modelling techniques: supplementing existing physical capture techniques, working at a fixed scale in the environment, taking advantage of the previously defined AR working planes, performing iterative refinement of models, and the use of simplified operations to avoid the entry of vertices where possible.
The purpose of these techniques is not to replace existing object capture methods discussed in Chapter 2, which are highly accurate and can produce excellent results given the correct conditions. When working in unfavourable conditions, construction at a distance may be able to overcome these and capture a 3D model. Some examples of where my techniques are most useful are:
∑ I use a human operator that is capable of accurately estimating the geometry of planar shapes, even when partially occluded by other objects in the environment. When trees occlude the edges of a building, a human can estimate the layout based on the image information available and other knowledge.
The eye is a highly accurate input device capable of aligning along the walls of buildings within the limitations discussed previously. Accurate modelling is still possible when working from a distance and direct access to the object is not available.
Existing capture techniques have a fixed operation time no matter what the complexity of the scene is, whereas in my methods the human can judge the most appropriate level of detail. In many cases the user wants to create only simple shapes such as boxes to represent buildings, and so these techniques are ideal for quick operations.
Existing techniques require the object to already exist so it can be captured, whereas my methods allow the human to specify any geometry desired. This allows the creation of new shapes that do not physically exist and may be used to plan future construction work.
While the eye and brain are powerful capture devices, there are limitations introduced by the resolution and accuracy of the tracking devices used to record the inputs. For example, when using a GPS accurate to 50 centimetres the object size that can be modelled is in the order of metres (such as a car), while using a 1 millimetre magnetic tracker allows much smaller objects (such as a drink can). This research does not attempt to address problems with registration or accuracy of tracking devices, but instead works within the limitations of current technology to provide the best solutions that can be achieved.
In previously discussed VR research, a number of techniques have been developed for use in modelling applications. These applications traditionally provide tools to create and manipulate objects in a virtual world, and to fly around and perform scaling operations to handle a variety of object sizes. While techniques for action at a distance such as spot lights [LIAN93], selection apertures [FORS96], and image plane techniques [PIER97] have been developed, these only perform simple manipulations on existing objects and cannot be used to create new ones due to the lack of generating distance values. Techniques such as flying [ROBI92], Worlds in Miniature [STOA95], scaled world grab [MINE97a], and Voodoo Dolls [PIER99] can perform the creation of points by bringing the world within armís reach, but accuracy is affected by the scale. Due to their non-exact freehand input methods, all of these systems are also limited to conceptual modelling tasks and not precision modelling. CAD systems use snapping functions or exact numerical entry to ensure accurate inputs, but require an existing reference to snap to or non-intuitive command-based entry.
Although AR environments share some similar functionality with VR, AR is unique in that it requires registration of the physical and virtual worlds. Flying and scaling operations require the breaking of AR registration and so cannot be used. Scaled world representations force the user to divert their attention from the physical world to perform miniature operations within the hands. Existing VR techniques cannot create models of objects the size of skyscraper buildings without breaking the 1:1 relationship between the user and the virtual world. With construction at a distance techniques, the scale of the world is fixed and only the userís head position controls the view. The virtual geometry is created using absolute world coordinates and is always registered and verifiable against the physical world in real-time. By using the physical presence of the user as an input device, the body can be directly used to quickly and intuitively control the view rather than relying on a separate input device.
As discussed previously, humans are much more capable of accurately estimating and specifying horizontal and vertical displacements compared to distances. By using the AR working planes and landmark alignment techniques formulated earlier, simple 2D input devices can be used to draw points in 3D. An AR working plane can be defined at any time from the body along the direction of view (maximising accuracy with landmark alignment) or relative to an existing object (maintaining the same accuracy as the source object), and the user can then move around to a different angle to draw against this surface. With AR working planes, the user is able to draw points that are at large distances and at locations that are not normally reachable, maintaining a 1:1 relationship between the virtual and physical worlds.
In order to draw against the working plane surface, a 2D input device must be used. This chapter is written without specifying any particular input device technology to demonstrate the generic nature of these techniques. Some of the examples in this chapter show a glove with fiducial marker-based tracking in use as the input device. A 2D cursor is overlaid on top of the fiducial marker and this is used to project points onto the AR working planes. Other input devices such as trackballs or joysticks can just as easily be used for this task. Chapter 5 will discuss further the implementation of the user interface, the input devices used, and how construction at a distance is implemented within applications.
Construction at a distance relies on a set of fundamental operations that by themselves cannot generally model a physical world object. Combining a series of these fundamental operations by making iterative improvements can produce complex shapes however. As the modelling operation is taking place the user can see how well the virtual and physical objects compare, repeatedly making changes until the desired quality is gained. The previously discussed CSG techniques used by CAD systems also rely on this principle to produce highly complicated shapes that would otherwise be difficult to specify. The ability to instantly verify the quality of models against the physical world helps to reduce errors and decrease the total modelling time.
The process of iterative refinement for VR modelling is discussed by Brooks [BROO97], and he recommends that a breadth-first iterative refinement strategy is the most efficient. Each major object should be created using a simple representation at first, and then of each minor object of lesser importance. By refining the objects that require it, guided by the eye of the user, Brooks suggests that poor approximations will be immediately obvious and should be the first objects to correct. I have used these VR guidelines for my construction at a distance techniques, and take the refinement process one step further by using the unique ability of AR to compare virtual and physical worlds simultaneously. Instead of attempting to capture millions of polygons, construction at a distance focuses on simplicity and can be used to capture models at very simple detail levels if desired by the user. As mentioned in the background chapter, a laser scanner typically takes about one hour to capture a building from four different angles, producing a fixed number of millions of polygons with no control over detail. In comparison, if a simple model with only important features is required, the user can focus their efforts on these parts only and reduce the modelling time considerably. As an example, a building with four walls and a sloped roof was captured in the few minutes it takes to walk around and sight along each wall of the building.
Table 4‑1†††††††† Top down view of building shapes with vertices (v), edges (e), and facets (f)
Some techniques have been developed previously for the interactive creation of data in virtual environments with no prior information. The previously discussed CDS system by Bowman can create vertices by projecting a virtual laser beam against the ground plane [BOWM96]. By connecting these points together and extruding the 2D outline upwards, full 3D solid objects can be created although they are limited in complexity by being constant across the height axis. The previously discussed work by Baillot et al. performed the creation of vertices located at the intersection of two virtual laser beams drawn from different locations [BAIL01]. After defining vertices these can then be connected together to form other shapes of arbitrary complexity, limited only by the time available to the user. Since these techniques both operate using vertex primitives that are then connected into edges, polygons, and objects, the complexity of this task increases as the number of facets on the object increases. Given a building object with n walls (along with a roof and a floor) there will be 2n vertices, 3n edges, and n+2 facets to process. Table 4‑1 shows some example objects with a varying number of faces, with linear growth for vertex and edge complexity. Rather than treating objects as collections of vertices, construction at a distance mainly operates using surfaces and solid objects, so an object with 10 facets can be modelled in 10 steps rather than as 20 vertices and 30 edges.
This section describes techniques involving the direct placement of objects within armís reach. While not being truly construction at a distance, these techniques may be used as inputs for other operations. These techniques are the simplest to understand for the user, although can be time consuming due to the physical movements required.
Figure 4‑1††††††† AR view of virtual table placed in alignment with physical world table
The simplest way to perform modelling is to use prefabricated objects and place them at the feet of the user as they stand in the environment. The object is placed when commanded by the user, with its orientation specified relative to the viewing direction of the user and always placed level to the ground plane. I have termed this technique street furniture, as it can be used to place down objects that commonly occur on the street, such as the table in Figure 4‑1. This method works well when objects to create are known in advance, and the user can avoid having to model the object each time. Due to the direct nature of the technique, no abstractions are required for the user to understand since physical movements are used to control the object placement. By instantiating objects at the userís feet, tracking of the hands can be completely avoided to simplify the task further. While this is not construction at a distance according to the definition, it is the most basic and simplest operation that can be performed using a mobile outdoor AR computer. Later techniques described in this chapter use direct placement for the creation of infinitely sized plane surfaces in the environment. The main limitation of this technique is that the user must be able to walk up to the location desired. When the object cannot be reached, techniques that can perform true construction at a distance must be used.
Figure 4‑2††††††† VR view of bread crumbs markers defining a flat concave perimeter
Figure 4‑3††††††† AR view showing registration of perimeter to a physical world grassy patch
Using the previously defined street furniture technique to place down prefabricated shapes is useful but limited to objects created in advance. For large objects such as rivers, lakes, trails, roads, and other ground features I have developed a technique named bread crumbs. In many cases it is possible to walk near the edges of these ground features, and so a direct system of marking out their vertices is realisable. The bread crumbs technique is inspired by the childrenís fairytale Hansel and Gretel [GRIM22]. In this story, the children are taken out into the forest by their parents in the hope they will not come back home again. Hansel was carrying a loaf of bread however, and dropped small crumbs of bread where they walked, enabling them to find their way back home again. On the second occasion, the children found themselves unable to get home because the birds in the forest ate up the trail.
Figure 4‑4††††††† Example bread crumbs model extruded to form an unbounded solid shape
With a mobile AR system, less edible and more reliable virtual markers can be placed down on the ground to simulate bread crumbs. The user walks along the perimeter of the object they wish to model and manually specifies markers to place at points of interest under the feet, as shown in Figure 4‑2. This is the same as marking waypoints when using a hand-held GPS while walking. When the user fully walks around the object, a closed perimeter is automatically formed and converted into polygons, as in Figure 4‑3. While the initial perimeter defined is a thin polygon, it can be infinitely extruded up to define a solid building outline, or infinitely extruded down to approximate the bottom of a lake or river. The resulting extruded object is unbounded in the vertical direction as shown in Figure 4‑4, and must be completed using other techniques described later.
The bread crumbs technique has been used to model roads, parking lots, grassy areas on campus, and other concave outline style shapes. Paths and navigation routes may also be defined using bread crumbs, except treated as a line segment instead of a solid polygon.
This section describes a series of construction at a distance techniques based on the userís physical presence in the environment. Using simple head-based pointing, the geometry of planes originating from the body can be specified, taking advantage of the userís sense of proprioception. Using CSG techniques, these planes can be used to easily define solid building shapes out of armís reach. Since many buildings in the physical world can be modelled using planes, the process of modelling can be accelerated compared to the simplistic approach of creating each vertex and edge manually.
Figure 4‑5††††††† Infinite carving planes used to create a convex shape from an infinite solid
Buildings in the physical world tend to approximate collections of angled walls in most cases. As described in Chapter 2, a solid convex cube can be formed with the specification of six planes arranged perpendicular to each other and a CSG intersection operator. Instead of specifying these planes numerically, the user can create these same planes in an AR environment by projecting them along the line of sight. By looking along the plane of a wall of a building and aligning the two ends (a very accurate method of positioning discussed previously) the user can project an infinite plane along this wall (in a similar way to AR working planes). Each plane defines a half space that when combined with a CSG intersect operation will form a finite solid shape.
Figure 4‑5 depicts a five-sided building and the location of the mobile AR user as they are sighting down each of the walls, showing the infinite volume being iteratively bound by the infinite planes. At the beginning of the operation, the AR system creates an (approximately) infinite solid volume that will be used for carving. When the user is aligned with a wall, they project an infinitely long vertical plane along the view direction into the world. This plane divides the previous infinite solid into two parts and the left or right portion (decided by the user) is carved away from the solid and removed. As the user sights along each wall, the solid will be reduced down to an object that is no longer infinite in the X and Y axes. At completion, a floor is automatically created at ground level, and the roof is left unbounded for carving using other techniques, since it is impractical to sight along the roof of a very tall building. The final 3D shape is stored using absolute world coordinates and reflects the geometry of the physical building.
With this technique, the object can be carved away iteratively and the user receives real-time feedback of the infinite volume being bounded, allowing immediate undo in case of a mistake. Compared to the direct methods described previously, this plane-based technique allows the capture of buildings from a distance without having to actually stand next to or on top of the building. Since the user is in direct control of the modelling process, the positions of occluded surfaces can be estimated using their knowledge of the environment. These features are useful because many existing physical capture methods require a full view of the object, GPS trackers do not work well near large buildings, and standing on top of a building may not be possible or too dangerous. This technique is also much more efficient than vertex and edge specification since each wall is handled with a single primitive that is easy to create accurately. A limitation of this technique is that using only planes and a CSG intersection to define objects restricts usage to convex buildings with no indentations, and this will be addressed further at the end of this section.
Another limitation of the orientation infinite planes technique is the dependence on an orientation sensing device for the head. While GPS units may have reliable accuracies in the order of 2 cm, orientation sensors vary in accuracy due to problems with interference and limitations of the technology. These variations affect the placement of planes in the environment and as the distance from the user increases, angular errors cause increasing positional errors. The last section of this chapter will further discuss the accuracy of these techniques in more detail, but using techniques that can avoid the use of orientation sensing should be able to produce much more accurate results.
Figure 4‑6††††††† Orientation invariant planes generated using multiple marker positions
In order to take advantage of the stability of position tracking, the orientation infinite planes technique described earlier can be modified to use two or more position points to specify orientation, making it invariant to errors in orientation tracking devices. Using the same landmark alignment concept discussed previously, the user can accurately sight along a wall and mark a position. To indicate direction, the user walks closer while maintaining their alignment and marks a second point. These two points can then be used to project an infinite carving plane, with Figure 4‑6 depicting the position markers and planes used to form a complete building shape. By increasing the spacing of the marker points or using a line of best fit amongst multiple points, the accuracy of this technique can be further improved.
Figure 4‑7††††††† Relationship between GPS accuracy and required distance to achieve better than 1 degree of orientation error for two different GPS types
To measure the accuracy of this technique, Figure 4‑7 depicts how the angular error is affected by the positional error in the GPS and the distance between the two marker points. To make this technique useful it must have an accuracy that is better than is available using traditional orientation sensors. Assuming a maximum error of 1 degree, Figure 4‑7 contains the calculations to find the distance required between markers for 50 cm and 2 cm accurate GPS units. Using the RTK example, if 1.1 metres is required for less than 1 degree error then 10 or more metres will produce orders of magnitude better results than previously possible. For a 50 cm GPS these results are not so promising since the marker distance required of 29 metres is quite large - the user will have to choose between the errors introduced by the GPS or those from the orientation sensor.
Figure 4‑8††††††† Orientation invariant planes formed using first specified angle and markers
This technique is similar to the position infinite planes technique in that it is invariant to orientation sensing errors. The previous technique required the user to specify the orientation for each plane by using two points, but if the angles at each corner are known to be equal then only one orientation is needed and the others can be calculated automatically. The user creates the first plane using the same method described previously, but for each additional plane only one position marker is recorded. Based on the number of positions marked, the system knows the number of walls to create and calculates the orientation for each position point based on the first plane. This technique uses nearly half the number of points and yet produces the same accuracy if the first plane is properly placed and the building meets the required properties. Figure 4‑8 depicts the markers created by the user and how they are used to project planes through the environment to form a solid shape.
Many objects in the world are not the same shape as simple boxes, cylinders, spheres, and cones. While it may seem that many objects are too complicated to model, they may usually be described in terms of combinations of other objects. For example, the process of defining a cube with a hole using vertices is time consuming, but can be easily specified with a CSG operation. As discussed in the background chapter, CSG is a technique commonly used by CAD systems, supporting Boolean set operations such as inversion, union, intersection, and subtraction [REQU80]. The manufacture of objects in the physical world is also performed in a similar manner - to produce the previous example a drill is used to bore out a hole in a solid cube.
Figure 4‑9††††††† Box objects can be moved into a building surface to carve out windows
To demonstrate CSG operations outdoors, Figure 4‑9 depicts an example where a user is applying the CSG difference operator to subtract cubes from a building shape. This could be used when the user needs to carve out indented windows. The first example shows a cube placed at a distance (1a), and then dragged sideways until it enters the building shape (2a). The second example shows a cube attached to a working plane (1b) and then pushed (2b) into the surface of the building (similar to a cookie cutter), requiring close access to the building. As the cube is being positioned by the user, the CSG difference operator is interactively calculated and displayed to the user.
The infinite planes examples previously described have only dealt with convex shapes, limiting the types of buildings that can be modelled. A convex shape can be simply described as one that appears to have the same shape when a sheet of rubber is stretched over its surface, such as the trapezoid in Figure 4‑10. A concave shape is more complex and contains holes or other indentations that will not be visible when a sheet of rubber is stretched over. Concave buildings similar to the T, L, and O shapes in Figure 4‑10 cannot be modelled directly using a single set of infinite planes because planes from one part of the object will exclude other parts. Concave objects can be created using other convex objects as inputs however, such as with the subtraction of one box from another depicted in Figure 4‑11. The two boxes can be individually created using infinite planes, and then combined using a CSG difference operator to produce a concave object. When combined with CSG techniques, infinite planes become more useful with the ability to also model concave objects.
Figure 4‑10††††† Convex trapezoid and concave T, L, and O-shaped objects
Figure 4‑11††††† Concave object created using CSG difference of two convex boxes
This section describes a series of construction at a distance techniques based on AR working planes. The previous techniques are capable of placing prefabricated objects and capturing bounding boxes for large objects, but detailed modelling is not provided. Using AR working planes and a 2D input glove (as used in these examples), the user can specify much more intricate details to create realistic 3D models.
The projection carving technique modifies existing objects by projecting points against surfaces and then cutting away extrusions to produce new highly concave shapes. This technique provides the ability to construct features such as zig-zag roofs and holes that are difficult or impossible to model using previously described techniques. Figure 4‑12 depicts an example of how this technique can be used to carve two peaked roofs onto a building model. These building models may have been created using infinite planes and projection carving can be used to restrict the infinite roof to a finite volume. The AR working plane is created relative to a polygon that has been selected by the user. The object that contains the polygon is then used as the input for the upcoming carving operation. The user then creates vertices along the surface of the AR working plane and these are connected together to form a 2D concave outline. This outline is then extruded along the surface normal of the working plane and used as an input tool for a CSG difference carving operation.
Figure 4‑12††††† AR working planes are used to specify vertices and are projected along the surface normal for carving the objectís roof
The projection is performed using orthogonal extrusion from the AR working plane, and is position invariant so points can be entered from any location in front of the polygon. This enables the user to cut a flat roof on a 100 metre high building while standing at ground level and looking up. If the cursor was directly used to carve the object directly like a laser beam, the system would produce pyramid-shaped extrusions. For some buildings, the user may only desire to create a flat roof or a single slope, and by creating only one point the system will create a horizontal cutting plane, and with two points a diagonal cutting plane is created. More than two points implies the user wishes to cut with an outline and so it must be fully specified as in Figure 4‑12. The CSG operation can be switched from difference to intersect if desired, with the effect being that the user can cut holes or split an object into separate parts instead of carving the outside. Used in this form, orthogonal extrusion is limited to carving operations that can be seen in a silhouette representation Ė other features such as indentations that are not visible from the side can not be captured with this technique. Some of these limitations can be overcome by limiting the depth of the extrusion used for carving. By using a small fixed value or controlling it by moving the body forward or backward, the extrusion can be controlled by the user and used for features such as windows or doors.
This technique is first demonstrated with a simple example of modelling a building with a sloped roof such as in Figure 4‑13. The user first captures the overall geometry of the building using orientation infinite planes with an unbounded roof. To carve the roof, the user positions their body somewhere in front of the building so that the entire slope is easily visible. The user then indicates with the cursor the first vertex defining the roof on the left, then the peak of the roof, and then the right side of the roof, as shown in Figure 4‑14. To complete the selection, the user must enter vertices around the overall building to indicate that the rest of the object should be kept. The selection region is then used to define a carving tool that removes all objects outside the region, to produce the final shapes shown in Figure 4‑13 and Figure 4‑15.
Figure 4‑13††††† AR view of infinite planes building created with sloped roof
Figure 4‑14††††† AR view of infinite planes building being interactively carved with a roof
Figure 4‑15††††† VR view of building with sloped roof, showing overall geometry
A second example demonstrating this technique is a small automobile being modelled outdoors in Figure 4‑16. Firstly, a prefabricated box is created and scaled to approximate the overall dimensions of the car. The user next views the car from the side and intersects points against the box surface to define the silhouette. Figure 4‑16 shows the markers being placed on the box, and Figure 4‑17 shows the final solid shape of the car approximately matching the physical world. The object can then be carved along any other polygons to further refine the model until it suits the userís requirements.
Figure 4‑16 Frames of automobile carving, with markers placed at each corner
Figure 4‑17††††† Final resulting automobile shown overlaid in AR view, and in a VR view
Once a building has been created, the user may desire to place windows, doors, and other extra details onto the model. While it may be possible to draw these details onto a texture map (which cannot be zoomed arbitrarily), or to place extra polygons outside the building to represent these (covering the original building), the building model itself remains untouched. If these new polygons are removed or manipulated, the original solid object remains since the changes are only superficial. A more desirable scenario is that polygons of a different colour are actually cut into the subdivided surface of an object, so that if they are deleted it is possible to see features inside the object that were previously concealed. I have named this technique projection colouring and its operation is depicted in Figure 4‑18. Using the same steps as projection carving, vertices are projected against an AR working plane created relative to the surface and then connected into an outline. Instead of carving away the outline, the surface is subdivided and the colour of the outlined polygon is modified. The newly coloured polygons may then be deleted or manipulated freely by the user if desired. The window and door in Figure 4‑18 have been cut into the surface using this technique, with the door then openable using a rotation.
Figure 4‑18††††† Schematic illustrating the painting of a window onto a wall surface
Figure 4‑19††††† Examples showing surface of revolution points for tree and cylinder objects
When working outdoors and modelling natural features such as trees and artificial features such as fountains, box-shaped objects are usually poor approximations to use. In an attempt to model these objects, I have used surface of revolution techniques (as used in many desktop CAD systems) to capture geometry that is rotated about an axis. The user starts by creating an AR working plane in the environment, with the most intuitive way being to sight toward the central trunk of the tree and project the AR working plane along the view direction. The user then projects vertices onto the AR working plane, defining one half of the outline of the object. After specifying the vertices along the axis of rotation, the system generates a solid object by rotating the outline around the axis, as depicted in Figure 4‑19. Figure 4‑20 shows an example where the vertices of a tree have been specified with a preview shape generated, with Figure 4‑21 showing the final shape in the environment. This technique generates good results when modelling natural objects such as pine trees that are highly symmetrical about the trunk. For trees that grow with deformities and other non-symmetrical features this technique may not generate suitable approximations. To improve the approximation, previously described carving techniques may be applied to refine the model until the user is satisfied with the object.
Figure 4‑20††††† AR view of surface of revolution tree with markers on AR working plane
Figure 4‑21††††† VR view of final surface of revolution tree as a solid shape
When implementing live AR video overlay, the mobile computer includes a video camera that may also be used to supply textures for polygons. By using the same tracking devices that are already required for AR, the system can automatically match up images from the camera to polygons in the scene. Captured models are normally only presented using a single colour and texture maps increase the realism for users without having to add extra polygons for detail. This texture map capturing technique is an alternative to the previously described projection colouring technique when only superficial details are required. To perform texture map capture, the user stands at a location where the texture for an objectís polygon is clearly visible to the camera. The user selects the polygon to activate capture mode and the system projects the polygon vertices onto the AR video overlay to map the still image as a texture. The user repeats this operation for each polygon until the object is completely textured. An example is shown in Figure 4‑22, where a stack of pallets is modelled using a 1 metre box and then textures are captured for the polygons. The final resulting model is shown in Figure 4‑23.
Figure 4‑22††††† Outdoor stack of pallets approximating a box, before modelling
Figure 4‑23††††† VR view of final model with captured geometry and mapped textures
The best results for this technique are obtained when the object is fully visible and fills as much of the HMD as possible, as well as being perpendicular to the userís viewing direction. Since OpenGL only supports linear texture mapping, if the angle to the polygon is large (such as the roof of the box in Figure 4‑22 and Figure 4‑23), the texture will be highly distorted since a non-linear mapping is required. Although techniques for capturing textures of 3D models have been described previously, this has not been performed in a mobile outdoor AR environment. Previously discussed work by Debevec et al. implemented the capture of 3D models from photographs and extracted textures for each facet [DEBE96]. Lee et al. also implemented the capture of textures in AR but with surfaces being modelled within armís reach using a wand, with the system automatically capturing textures when video frames were deemed suitable [LEE01]. The video stream used with mobile AR suffers from problems with motion blur and tracker registration, and having the user choose the moment to capture the texture generates the highest quality output.
While a number of techniques can perform the modelling of simple and useful shapes, the true power of construction at a distance is expressed when used in combinations. This integrated example is designed to highlight all of the features of the described modelling techniques with the construction of an abstract building in an outdoor environment. The user walks outside to an empty piece of land and creates a landscape that they would like to preview and perhaps construct in the future. As an added feature, this model may be viewed on an indoor workstation either in real-time during construction or at a later time. Some other similar applications are for creative purposes such as an abstract art or landscape gardening design tool.
Figure 4‑24 and Figure 4‑25 show different views of this example at the end of the construction process. The first step is to create the perimeter of the building shape using the bread crumbs technique - the user walks around the building site and places down markers at the desired ground locations, forming a flat outline. Next, the outline is extruded upwards into a solid 3D shape. Using projection carving, the user cuts a main roof to make the object finite and then carves a slope using various control points. After the overall roof structure is created, the object is lifted into the air. At this point, the supporting columns, trees, tables, and avatar people are created using street furniture placement of prefabricated models at the desired locations. The building is then lowered by visual inspection onto the supporting columns. Next, the user performs further carving and a large hole is created through the centre of the building. Projection carving is then used to cut out two large sections of the building, causing it to exist as three unattached solid shapes most visible in Figure 4‑24. After around 10 minutes for this example, the desired model is complete and the user can now move around the environment to preview it from different view points.
Figure 4‑24††††† AR view of final abstract model, including street furniture items
Figure 4‑25††††† VR view of final abstract model, including street furniture items
The construction at a distance techniques rely on the position and orientation sensors for all tracking, and so increasing the accuracy of these devices will produce improved results and affect the minimum model size that can be properly captured. Errors from each sensor have different effects on the captured models since one is measured as a distance and the other as an angle. When rendering the AR display, results are also affected not only by the errors in the current tracker data, but also those from the capture process. Tracking devices affect the overall operation of the system and how it can be deployed in the physical world.
The position sensor used in these examples is a Trimble Ag132 GPS, with an accuracy of approximately 50 centimetres and working reliably amongst small buildings and light tree cover. To ensure the most accurate positioning possible, an indicator on the HMD shows the quality of the GPS signal to the user. For orientation, an InterSense IS-300 hybrid magnetic and inertial sensor is used, although the tracking is unreliable when there are magnetic distortions present in the environment or when the user is moving quickly and disturbing the sensor. Since the accuracy of the IS-300 may vary unpredictably, this is the most critical component in terms of reliability and accuracy.
When modelling a new object, the accuracy of projection-based techniques is dependant on the userís current location and the direction they are looking. For the highest accuracy, it is desirable to be as close to the object as possible, minimising the distance the projection can stray from the desired direction caused by angular errors in the orientation sensor. When viewing an existing virtual object, the registration errors with the physical world caused by the GPS will be the most accurate when viewed from a distance due to perspective, while standing very close to an object will cause these errors to be more noticeable. For registration errors caused by the IS-300, these remain constant on the display at all distances due to their angular nature.
While GPS is fast and reliable (with accuracies of RTK GPS units at 2 cm in ideal conditions), improvements in orientation sensing technology are required to make the construction at a distance techniques more accurate. While the IS-300 is one of the best mid-range priced trackers on the market, it is still not completely adequate for modelling outdoors. Due to its use of magnetic sensors, distortion caused by sources of magnetic interference such as the backpack, underground pipes, and street lamps affect the accuracy of the tracking. To correct for this, the backpack contains a touch pad mouse attached to the userís chest to apply a correction offset. The touchpad allows the user to fine tune the calibration while moving outdoors through varying magnetic fields. I am currently investigating methods of automating this calibration, with methods such as optical natural feature tracking holding promise, but these are still current research problems.
This chapter has presented my novel construction at a distance techniques, designed to support the capture and creation of 3D models in outdoor environments using AR. Construction at a distance takes advantage of the presence of the userís body, AR working planes, landmark alignment, CSG operations, and iterative refinement to perform modelling tasks with mobile AR systems. The techniques described in this chapter include street furniture, bread crumbs, three types of infinite planes, CSG operations, projection carving, projection colouring, surface of revolution, and texture map capture. When used in an AR environment, users can capture the geometry of objects that are orders of magnitude larger than themselves without breaking AR registration or having to touch the object directly. These modelling techniques are intuitive and support iterative refinement for detail in areas that require it, with AR providing real-time feedback to the user. While existing techniques are available for the capture of physical world objects, these still have limitations and also cannot be used to create models that do not physically exist. The construction at a distance techniques were field tested using a number of examples to show how they may be applied to real world problems. By discussing insights gained from these examples I have identified areas for improvement that currently cause accuracy problems.
Wernher Von Braun (1912-1977)
This chapter presents new user interface technology that I have designed and developed for use with mobile outdoor AR systems. This user interface is used to control the previously described AR working planes and construction at a distance techniques, and is implemented in a mobile outdoor AR modelling application named Tinmith-Metro. The implementation of the techniques described in this dissertation demonstrates their use, and allows different ideas to be tested and iteratively refined through user feedback. Testing the techniques out in the field has also given insights into more efficient methods after experimenting with early prototypes. The user interface described in this chapter is designed to be implemented within the limitations of current technology while still being intuitive to use. The solution developed uses a pair of vision tracked pinch gloves that interact with a new menuing system, designed to provide a cursor for AR working planes and command entry to switch between different operations. The user requires feedback on the HMD while using the system, and so the design of the display with its various on screen objects is discussed. To implement the full Tinmith-Metro application, a large hierarchy of commands was created to control the various modelling techniques and these are presented in a format similar to a user manual. This chapter ends with a summary of a number of informal user evaluations that were used to iteratively refine the interface.
Augmented and virtual reality both use the motion of the body to control the image that is displayed to the user. While tracking the body is critical to rendering AR and VR displays, fine grained manipulation and selection operations typically require the hands to intuitively specify. The previously described Smart Scene [MULT01] and CHIMP [MINE96] applications have intuitive user interfaces that involve the use of hand gestures supplied by high quality tracking systems. These user interfaces implement many best practice techniques for direct manipulation in VR that are desirable for modelling tasks. While outdoor AR is similar and using these techniques is desirable, there are a number of unique problems (discussed in Chapter 2) that require solutions. Previously discussed outdoor AR systems use indirect style user interfaces involving tablets, keyboards, joysticks, trackballs, gyro mice, and other hand-held devices but do not support direct manipulation operations. The user instead steers a cursor across the display using indirect motions of the hands. Some devices such as velocity-based inputs have a further level of indirection and so are even less intuitive to use. Shneiderman [SHNE83] and Johnson et al. [JOHN89] both discuss how adding levels of indirection may make user interfaces less intuitive to operate. Furthermore, any AR user interface developed must use mobile technology so the user may roam freely in the outside environment. It must take into account restrictions on size, performance, electric power consumption, and weight.
I was initially inspired by the elegant combination of commands and pointing implemented by Bolt in the Put-That-There system [BOLT80]. The user is able to point their hand toward objects projected onto the walls and then speak commands to perform operations. The use of menus by many other applications is an unintuitive abstraction that forces users to select suitable options from a list, usually with the same input device. The direct command entry in Put-That-There is similar to how people operate in the real world, and can be observed when watching a supervisor explain an operation to a worker: ďPick up that brick and place it over thereĒ, while at the same time pointing with the hands to indicate exactly what object and where to put it. Brooks also agrees and discusses how commands and operations should be separated in interactive systems [BROO88].
Based on this initial inspiration from Bolt and Brooks, I have developed a user interface involving the use of tracked gloves so the user can point at objects and interact with them in a mobile outdoor AR system. Since the user is wearing gloves, there is no need for any other devices and the hands are free to perform physical world tasks. Implemented using vision tracking, the interface can locate the userís hands in the field of view. Using optical methods (discussed further in Chapter 7) is one of the only ways to perform tracking of the hands outside, although the 3D accuracy is quite poor compared to indoor magnetic tracking. One property of the optical tracker is that its 2D accuracy is quite good, and 2D cursors can be calculated from the hand positions very easily. These 2D cursors are suitable for input to the AR working planes techniques and used to perform interactions in 3D environments. Due to poor distance and orientation detection, these values are not used to control the user interface. The use of tracked gloves to perform selection and manipulation of objects is a very direct user interface because natural hand gestures are used to express actions. Although the object at a distance is not being touched directly, humans still intuitively understand this operation. When talking with others, humans naturally point and understand these gestures even when the object is very far away. Pierce et al. demonstrated a number of simple selection techniques demonstrating this using image planes [PIER97].
Brooks suggested that commands and operations should be kept separate and so I have included this important idea into the user interface. Many systems implementing direct manipulation also make use of menus or other widgets that the user must reach out and grab. These systems use the same cursors for both interaction and command entry, and these ideas do not work well when implemented with poor tracking. Rather than having unreliable command entry caused by poor tracking, I have developed an interface based on the pinching of fingers to select commands from menus fixed to the display. The pinching of fingers is a very reliable input that is easy to implement in any environment, and allows the hands to be outside the field of view and untracked during command entry. The use of finger pinches mapped directly to the menu also allows the same hand to perform simultaneous interaction and command entry.
By separating the command interface from pointing, Brooks describes how the command input method can be changed without affecting interaction. He also suggests that speech recognition is a natural candidate for command selection, as used by Bolt in Put-That-There. Johnson et al. also point out that the use of command interfaces such as speech input encourages the user to think of the computer as an assistant or co-worker rather than a tool [JOHN89]. While it would be interesting to explore the use of speech recognition for command entry, the current software available to implement it is very CPU intensive. When using a mobile computer there are limited resources available, and speech recognition would prevent the rest of the AR system from functioning adequately. Problems such as lack of accuracy and interference from noise (especially when outside) were common in older systems, and although improvements have been made they have not been eliminated. Currently this user interface does not implement speech recognition, but is designed to support this and is discussed as a future area of research at the end of this chapter.
The user interface is made up of three components: a pointer based on the tracking of the thumbs with a set of gloves worn by the user; command entry system where the userís fingers interact with a menu for performing actions; and an AR display that presents information back to the user. The display for the interface is fixed to the HMDís screen and presents up to ten possible commands as menu options at any one time. Eight of these commands are mapped to the fingers as depicted in Figure 5‑1, and the user activates a command by pressing the appropriate finger against the thumb. When an option is selected, the menu refreshes with the next set of options that are available. Ok and cancel operations are activated by pressing the fingers into the palm of the appropriate hand and are indicated in the topmost boxes of the menu. The interaction cursors are specified using fiducial markers placed on the tips of the thumbs, as shown in Figure 5‑2. With this user interface, the user can perform AR working planes, action at a distance, and construction at a distance techniques in mobile outdoor AR environments.
Figure 5‑1††††††† Each finger maps to a displayed menu option, the user selects one by pressing the appropriate finger against the thumb
The concept of AR working planes and two example uses were presented in Chapter 3, one for the manipulation of objects and the other for the creation of new vertices. AR working planes are designed to support interactions at large distances and only 2D input is required, simplifying the tracking requirements. Using the previously mentioned gloves, fiducial markers are placed on the thumbs to provide 3D tracking information using the ARToolKit [KATO99]. Due to the implementation of ARToolKit, the 6DOF accuracy can be quite poor but overlaid objects always appear to register with the targets. When the 3D coordinates from the tracker are projected onto the image plane of the display, an accurate 2D cursor can be obtained, as displayed in Figure 5‑2. With the availability of suitable tracking, the user interface contains cursors for both hands as well as a head cursor fixed to the centre of the HMD. Each operation is implemented using varying combinations of these input cursors depending on what is most appropriate. This section describes the implementation of one, two, and zero-handed AR working planes techniques with examples demonstrating their use outdoors.
Figure 5‑2††††††† Immersive AR view, showing gloves and fiducial markers, with overlaid modelling cursor for selection, manipulation, and creation
To simplify manipulation tasks for the user, operations such as translate, rotate, and scale are implemented using separate commands. This is on the assumption that the user will wish to work with certain degrees of freedom without affecting others. Researchers such as Hinckley [HINC94a] and Masliah and Milgram [MASL00] have demonstrated that users have difficulty controlling both the position and orientation of the hands at the same time. Constrained manipulation is also useful when poor tracking is used, so that other degrees of freedom are not affected when simple changes are made. Manipulation handles (as used in many previous 2D and 3D systems) are not used since they would be difficult to grab with the poor tracking available. The user instead activates the desired manipulation operation with a menu command and may grab any visible part of the object. This is also more intuitive since objects in the physical world can be moved by grabbing any part of the object.
Users may perform selection operations using a single hand controlling the 2D cursor. The cursor is projected into the environment and intersected against the nearest object, similar to the image plane techniques described by Pierce et al. [PIER97]. The object closest to the user will be selected, placed into a selection buffer, and rendered transparently in a selection colour for user feedback. Since the most common operations performed by users only involve single objects, the implementation has been streamlined so that selection is automatically performed when a manipulation command is activated.
To operate on many objects simultaneously, the user can select several objects and collect them together into a selection buffer. When a manipulation task is then specified by the user, they may select any object that is a part of the collection and then all objects will be used as inputs. The ability to collect objects together is also useful for specifying operations such as CSG where two inputs are required and both may contain multiple objects. Multiple objects are currently collected together by selecting each object, although other techniques such as two-handed rubber banding are simple to implement if required. To support modelling, an arbitrary number of selection buffers may be stored in a circular list for later use. Overall then, a modelling session may have a number of selection buffers (with one being active), and each buffer contains collections of objects that must all be operated on together. Selection buffers are similar to groups used in drawing editors, except no changes to the scene graph are made to express this relationship. Each selection buffer is represented using a different transparent colour to assist the user with identifying groupings. The active selection buffer may be nominated by cycling through the available list or selecting an object that is part of the desired selection buffer. Within a selection buffer, references to objects are also stored in a circular list, with one object nominated as the last object. When a new object is added to the selection buffer it will become the new last object, and extra commands are provided to operate on only the last object instead of the entire selection buffer. The last object pointer is useful for performing a type of undo to allow objects to be deleted in the order of creation.
Direct manipulation-based translation operations may be performed by the user with a single hand. At the start of the operation, the user selects an object (and automatically any other sibling objects which are part of the same selection buffer, if any) and an AR working plane is created using head-relative coordinates with normal pointing toward the user, located at the point of intersection with the object. As the userís hand moves, the cursor is continuously projected onto the AR working plane and used to offset the object accordingly, performing translation and maintaining the same object to user distance. If the user moves their body during the translation operation then the object will be moved in the same direction as this motion. If the user changes their head direction then the object will rotate around with the userís head. Figure 5‑3 shows an animation of a typical translation operation with a virtual tree being directly manipulated across the landscape by the userís hand and head motion. Instead of using head-relative coordinates, the AR working plane could also be defined in world, location, or body-relative coordinates to support different motion constraints. The properties of AR working planes relative to these different coordinate systems were described previously in Chapter 3.
Using AR working planes, the hand tracker and 2D cursor may also be used to project 3D vertices. Chapter 4 described a number of techniques involving the placement of vertices for the carving of objects and creating surfaces of revolution. When these techniques are activated, an appropriate AR working plane is created and the user can place vertices onto the plane using finger pinch commands.
The AR working planes scale and rotate operations require more inputs than the translation operation because an axis to scale or rotate about must be defined along with the adjustment vector. Scale and rotate may be performed more naturally through the use of two-handed input, which has been shown to improve performance and accuracy for users. Two-handed input techniques were first pioneered by Buxton and Myers in a study using 2D environments [BUXT86], and then discussed in terms of 3D environments in a survey paper by Hinckley et al. [HINC94a]. Sachs et al. found that using a tracked tablet in one hand and a pen in the other was more natural than having a fixed tablet and one-handed input [SACH91]. Zeleznik et al. presented some two-handed 3D input methods for desktop applications, of which some are implemented here [ZELE97]. These previous works all demonstrate that by using the non-dominant hand as an anchor, the dominant hand can accurately specify operations relative to this. Scaling may be performed by stretching the object with the two hands. Instead of using the orientation of the hand, rotation may be performed by using the angle between the two hands.
Figure 5‑4††††††† Scale operation applied to a virtual tree with the userís hands
Scaling operations are initiated by selecting an object and any associated selection buffer sibling objects with the non-dominant hand, and an AR working plane is created at the object in head-relative coordinates. Since the operation begins immediately after the scale command is issued, the non-dominant hand must be selecting the object and the dominant hand should be in a suitable position to specify the new relative scale adjustment. Similar to translation, the AR working plane could also be expressed in other coordinate systems to support different scaling constraints. The AR working plane is then used to capture initial 3D coordinates for the input cursors so that any relative changes can be applied to the object selected. Since the scale operation is performed against an AR working plane, only the two dimensions perpendicular to the surface normal of the AR working plane may be adjusted. This implementation uses the head cursor as the scaling axis and relative changes in the distance between the hands to control the scaling factor applied. Figure 5‑4 shows an animation using a sequence of frames with a tree being scaled about the head cursor using both hands. Scaling about the head cursor was implemented because I did not want the scaling axis to vary as both hands are being stretched apart. An alternative implementation is where the object is grabbed by the non-dominant hand to specify the scaling axis, and the dominant hand specifies a relative scaling adjustment to apply. If the non-dominant hand moves during the operation then the object may either move with the hand or remain still while the scaling axis moves instead. Performing simultaneous translation and scaling on an object may be undesirable because it is too many degrees of freedom to control with relatively inaccurate tracking.
Figure 5‑5††††††† Rotate operation applied to a virtual tree with the userís hands
Rotation operations are performed using similar two-handed input techniques as scaling, and started by selecting an object and any associated selection buffer sibling objects with the non-dominant hand. Similar to the previous scaling operation, the non-dominant and dominant hands must be in the desired positions because the rotation operation begins when the rotate command is selected. An AR working plane relative to the head is created at the object (similar to translation and scaling) and the initial 3D coordinates of the input cursors are captured. Since rotation is performed against an AR working plane, the rotation is fixed to occur on an axis parallel to the surface normal of the AR working plane. The head cursor is used as the axis of rotation and the angle between the hands is used to apply a relative rotation to the selected objects. Using two-handed rotation has an added advantage of performing well with tracking systems that produce high quality position but poor quality rotation values. By ignoring the orientation values and using only the positions for rotation, I avoid these errors and maximise the accuracy of the tracking system in use. Figure 5‑5 shows an animation using a sequence of frames with a tree being rotated about the head cursor using two-handed input. Similar to scaling, an alternative implementation is where the non-dominant hand grabs the object at the desired rotation axis while the dominant hand rotates the object. If the non-dominant hand moves then the object will move or the axis of rotation will move, and this may be an undesirable number of degrees of freedom to control.
Since the user interface has very powerful command entry which is not based on the cursor inputs, a number of manipulation and creation operations are possible without requiring the hands to be visible. The nudge operation allows the user to perform precise manipulations based on fixed increments. The head cursor can be used similar to the hands by pointing with a cursor fixed to the centre of the display.
To perform accurate translation, scale, and rotate commands, an operation named nudging is provided to operate in fixed increments and without requiring vision tracking. Nudging is based on the use of arrow keys or other discrete controls for interaction which are commonly used in 2D desktop-based modelling systems. The nudge move operation is used to offset the object in one metre increments in any direction relative to the user (left, right, up, down, towards, and away) and is useful in cases where the distance is known exactly or the user cannot walk to drag the object. Scaling is performed similarly using fixed units in any of the three axis directions or with the axes linked together to ensure uniform scaling. Rotation implements fixed angular offsets and can rotate about any of the three axes individually. The scale and rotate nudge operations also provide the ability to scale away from the user or rotate around the ground plane normal, which cannot be performed normally since AR working planes flattens these directions. Nudging does not require any input device tracking at all, and so is useful when objects are too far to judge adjustments, or when tracking is of extremely poor quality.
While the head cursor can be used to perform translation operations similar to those implemented using the hands, the main use is for the placement of objects in the world. AR working planes and orientation infinite planes may be projected from the head into the environment, and the head cursor is used to define the direction from the user that this plane will follow. Using the head cursor is an obvious way to project planes since the user is already aligning objects by sighting along the head cursor cross in the centre of the display. The placement of street furniture objects is also performed relative to the head cursor, with the objects being placed either with zero distance at the userís feet, or at a distance in front of the user and placed in the direction of the head cursor. The head cursor is further used to define surface normals for head-relative AR working planes definitions, with the normal being calculated using the location of the userís eye and the virtual head cursor in the centre of the display.
The cursor can be used to perform a range of powerful interaction operations, but a method of specifying which operation to perform is required to support complex applications. This section describes the hierarchical menu system I have developed to support a large number of commands. As previously mentioned, finger pinches on the gloves are utilised to control the menu system, allowing the user to select other menus and specify commands. Since no tracking is required the user can hold their hands anywhere they desire, and there is no need to dock the cursor onto menu options for selection. This especially increases the speed of command entry under poor tracking conditions. A menu-based system was chosen as a simple way of grouping common tasks to reduce searching time and form the basis for a command entry system. Having a logical and efficient layout of menu options is also critical to making it intuitive for users. This section compares the design of the menu system against a number of other relevant AR and VR command entry systems to demonstrate its novel features.
This dissertation has presented the benefits of using hand pointing for interaction and so a menu design that is compatible with this style of input is required. Using another 2D input device would be cumbersome and force the user to switch between different input methodologies. Therefore the design of the menu system is not based on the traditional WIMP 2D style interface with selection of objects using a cursor. Instead, the design is closer to that of the original MicroPro WordStar application shown in Figure 5‑6 and used in the 1970s and 1980s [XTRE03]. This application uses a menu bar along the bottom of the display to show the commands that are mapped to the F1-F10 keys on the keyboard. To select a menu option, the user presses the appropriate F-key and either the command is performed or the menu moves into a sub-level of extra options. Before the introduction of the mouse to user interface design, menu design was quite common and is discussed by Shneiderman [SHNE92]. Interacting with menus of this type requires a discrete number of inputs and the user can directly select what they want without having to control a dynamic input device.
Figure 5‑6††††††† Original WordStar application, showing menu toolbar at bottom of screen
(Image courtesy of Mathias Winkler - XTree Fan Page)
The command entry interface I have designed uses the fingers of the glove to select the desired menu node from a set of vertical menu strips placed on the sides of the display. Figure 5‑1 depicts how each finger on the glove uniquely maps from top to bottom onto the lower four coloured boxes forming the menu display. Transparency is used to allow the user to see the physical world through the menu, reducing visual clutter. The vertical placement of the menu strips intentionally matches the orientation of the hands when held comfortably at rest and when performing cursor operations. The direct mapping of finger pinches to menu options helps the user to quickly select the correct menu node. In addition to the userís eight fingers available for selection, two extra menu items located on top of the existing list are activated by pressing the fingers onto the palm and indicate major operations such as ok and cancel. The menu display is always fixed to the screen of the HMD and is ready to perform command entry regardless of the view direction or the position of the hands.
When the user selects a menu option, the system indicates the item by colouring it red, and then on release an audible tone is given. Menu nodes can execute internal commands, move to a deeper level in the menu, or both, and after performing these actions the display is updated with the newly available options. When a task is completed or cancelled, the menu returns back to the top-level node to perform the next operation. An added feature from the development process is that the menu can also be controlled using a traditional keyboard similar to the WordStar interface described earlier. This is useful for debugging when working indoors and for externally helping out a troubled user during a demonstration.
The command entry system has a task oriented nature, which is customised towards a particular set of operations that are being performed. When a user enters a mode such as carving, the menu does not return to the top-level but instead remains at the carving menu. Various carving operations are then available with only a single press required to activate them. At the completion of the carving task with the ok palm gesture, the menu returns back to the top-level so the user can select another task. The alternative is that the user has to navigate through many levels of a menu for each step in a task, which is highly inefficient. Each task is represented using a node in the hierarchy, with each step stored as a child of the parent task. When performing a particular task, only steps that are relevant at that time are presented to the user, reducing clutter. Section 5.5 describes the layout of the Tinmith-Metro menu tree, demonstrating how the commands are arranged to support a complex modelling application. This hierarchy has been optimised to ensure that commonly performed tasks are easily accessible to the user.
There has been a large body of previous work in the area of command entry for both VR environments and wearable computers, some of which is possible to leverage for use in mobile outdoor AR. This subsection compares some of the most relevant existing work against the user interface described in this chapter.
As previously described, Bowman and Wingrave have developed a VR menu system that also uses pinch gloves as an input device [BOWM01]. The top-level menu items are mapped to the fingers on one hand and the second-level options to the fingers on the other hand. Using the small finger, the user can cycle through extra options if more than three are available. This technique is limited to a depth of two due to its design and does not scale easily to a large number of commands. While Bowman and Wingraveís use of finger mapping is similar to my approach, my method (first described in [PIEK01b]) is superior because it allows a larger number of commands and minimises the number of presses required to perform the desired task.
In the outdoor AR domain, the original Touring Machine work by Feiner et al. utilised a small tablet computer with a web browser interface to control the main system [FEIN97]. Other research with tablets is also performed in the Studierstube environment by Szalavari and Gervautz [SZAL97] and extended later by Schmalstieg et al. [SCHM00] and Reitmayr and Schmalstieg [REIT01a]. Both of these user interfaces are useable while mobile because the tablets are compact devices that can be held in the hands. The disadvantage of these interfaces is they are not hands free, and require the tablet to be the centre of the userís attention to operate. Using the tablet to manipulate objects in the 3D view is also a less direct form of interaction and requires the user to divide their attention between the 3D view and the tablet.
The N-fingers technique was created by Lehikoinen and Roykkee for mobile outdoor navigation and context awareness using AR cues [LEHI01]. N-fingers uses gloves with metallic contacts similar to pinch gloves, except the contacts are arranged in a diamond shape in the middle of the fingers. This technique is primarily designed for scrolling through menus and lists, since the diamond shape maps easily to a set of directional arrows. By mapping the fingers to scrolling directions, the user can work with large lists of items but this also prevents jumping directly to desired options in one press. Since N-fingers can only be used for command entry, it requires another input technique to provide 3D interaction.
Blasko and Feiner present the use of a touchpad mouse for controlling a menu [BLAS02]. Instead of using the touchpad to control a relative mouse cursor, this interface uses the absolute position of the fingers to select from menu options. Menu options are directly selectable and so the user does not need to track the motion of a cursor or look at the hands, making it ideal for wearable applications. With the number of menu options limited by the userís four fingers and the resolution of the touchpad, menu hierarchies are used to extend the number of options that can be provided. Since the fingers must press against the touchpad surface mounted on the body, the user cannot perform cursor manipulation with the same hand as command selection.
The SKETCH system by Zeleznik et al. uses a gesture-based interface controlled by a 2D mouse to draw 3D pictures [ZELE96]. Hand gestures are analysed to initiate various commands such as selection, manipulation, and deletion. As previously discussed, gesture-based input requires the use of fast and accurate tracking to capture the motions correctly. There are also limits to the number of hand motions that can intuitively map to commands, with the rest requiring the user to memorise them. With the availability of limited outdoor tracking and a large number of abstract modelling concepts, higher-level approaches such as menus were chosen for the user interface.
The user interface provides a wide range of information to the user apart from just cursors and menus. Useable AR applications require other status information so that the user understands the state of the application and the environment around them. This section describes the display features that are used in the Tinmith-Metro mobile AR modelling application.
When viewing virtual objects outdoors, a 3D view registered with the physical world using tracking devices is required for the AR overlay. This overlay contains virtual objects such as buildings and trees that appear to exist in the physical world. The renderer also embeds other information into this view to improve the userís situational awareness. Shadows are rendered underneath objects to indicate their height relative to the ground plane. A 10 metre by 10 metre grid with a 100 square metre size is overlaid on the ground plane to indicate how far objects are away from the user. Tick marks with labels are placed every 15 degrees onto the horizon to indicate directions and provide steering cues.
Other 2D information is fixed to the HMD using screen-relative coordinates and is overlaid on top of the 3D view. 2D information is used to indicate the status of the system to the user and can be selectively turned on an off. Figure 5‑7 depicts the placement of the various display components described below:
∑ Menus - As discussed previously, the green and blue boxes in the bottom left and right corners of the display are used to indicate the currently available menu options.
Notice list - In green text between the menus is a four line long message area, where the system indicates errors and other events as they occur.
Tracker status - At the bottom are status strings for the head, hand, and body tracking devices. The small number superscripted to the left is the number of seconds since the last update. For the hand tracker (Cu) and the head tracker (Hd), the number to the right indicates the rate per second that the updates are arriving. The GPS uses a different notation where the letter X is used to indicate no GPS coverage, G for normal GPS reception, and D for high quality differential reception, followed by the number of satellites being tracked.
Figure 5‑7††††††† Immersive AR overlay display components explained
∑ Heading indicator - At the top right is a blue circle used to indicate the current heading of the userís view. The outlined triangle indicates the direction the user is facing, and the solid green triangle indicates the direction of geographic north. The NSEW labels rotate around the circle as the heading varies.
Project name - The top right contains the string ďTinmith-evo5 @ UniSAĒ with a small university logo. This is used to identify the software in demonstration videos and pictures.
Debugging - The top left contains the current frame rate and delay estimate, a clock showing the current date and time, and a display of the current position of the user.
Clipboard list - Each clipboard is represented as a string with an id number and colour value used for objects that are part of the clipboard. The names of objects contained within are listed underneath the clipboard name.
Colour selector - The cursors can be moved through an invisible cube in the view frustum where XYZ directions map to RGB values on a colour cube. The slider bars indicate the contribution of each colour and above this is a small rectangle showing the final colour. This colour can be applied to the surfaces of objects being modelled.
Cursor overlays - The vision tracking system produces 3D output that is used to overlay a 3D axis object onto each marker. This axis object is then flattened against the display and used to produce 2D cursors that may then be used with AR working planes. A 2D head cursor is fixed to the centre of the display representing the direction that the user is looking. An optional trackball mouse may be used for debugging to provide a cursor instead of the gloves.
Figure 5‑8††††††† Top down aerial view of VR environment in heading up and north up mode
Figure 5‑9††††††† Orbital view centred on the user with a VR style display
AR is designed to overlay virtual objects onto the userís view of the world and is intuitive to use since it is experienced similar to the physical world. This view may cause problems in situations where very large objects such as buildings may exceed the field of view of the display, objects may be too distant to clearly view, or other objects may be occluding an object of interest. The immersive view restricts the user if it is impractical or impossible for the user to move to a new viewing position. In these cases, it is more useful to work in an external VR style view (such as orbital view by Chung [CHUN92] and Koller et al. [KOLL96]) where the user sees a view of the virtual world from an external perspective. The advantages of external views are also discussed by Brooks, who mentions that users find a local map of the world useful to show where they are, what they are looking at, and to build a mental model for navigation [BROO88]. In the external views I have implemented, the ground and sky are both rendered using texture maps so that the user understands they are looking at a fully virtual view and are no longer immersive. Since the external view is designed to be used while wearing the HMD outdoors, the body of the user is shown in the centre of the display and motion about the physical world is updated in real-time. Top down views such as that shown in Figure 5‑8 provide an aerial perspective of the area, with north up mode being fixed while heading up mode rotates the map in the direction the user is looking. Orbital views such as that shown in Figure 5‑9 link all 3DOFs of head rotation to orbiting motions at a fixed distance around the user. Other views such as that shown in Figure 5‑12 are fixed relative to the body location so that it is not affected by rotation. These external views are generally only used while stationary because the physical world is completely blocked out and the user may not be able to safely move. The user is able to adjust the camera view using body position or head rotation (as discussed previously) but not hand gestures, and can freely switch between immersive and a number of pre-programmed external views using menu commands.
The simplest form of AR is optically-based, where the system only draws in the AR detail with the rest of the display remaining black for the physical world to be viewed in the HMD. In order to improve accuracy and achieve higher quality images, my implementations have switched from using optical overlay to video overlay (both of which are described in Chapter 2). All of the pictures presented in this dissertation of the final working system were captured using video AR. Performing video overlay is more complicated than in the optical case, and the computer needs to capture the video from a head mounted camera, render this to the display, and then overlay the 3D and 2D graphics on top to produce the final output for the HMD. The implementation still contains the ability to switch to optical overlay, but this is rarely used.
While this section does not discuss the overall software implementation, the renderer is implemented using OpenGL. The fastest method of rendering video frames is to draw a polygon of the desired shape and map the video frame as a texture map. When the texture map is rendered, it must be placed behind all the other objects in the scene, and so must be at a large enough distance away from the user. To fill the entire view of the user, the polygon must be attached to the userís head and scaled to exactly fit the entire field of view. By default this video texture plane is placed at the focal point of the HMD (usually located at 1-2 metres from the user) and then scaled out to a large distance. Scaling relative to the head will cause the plane to move out and maintain its coverage of the field of view. A polygon that is normally 1m x 1m at a distance of 2m will become 10 km x 10 km at a distance of 20 km, which can be thought of as a large drive in theatre screen attached to the head but far away so it appears the same size. The overall effect is that it appears to the user that the 3D objects are overlaid onto the live video stream and that the physical world is being augmented, as shown in Figure 5‑11.
The use of a texture mapped polygon attached to the head may seem overly complicated to perform a simple video overlay. This polygon has other uses and when the user switches to the VR external view, the texture map is modified so it is drawn at the focal point of the userís view frustum, as shown in Figure 5‑12. As the user rotates their head while in the VR view, the live video stream is rendered and shown in the direction they are looking. This is a very useful way for external users to understand how the video being captured relates to the rest of the world since they are both mapped to the same display. This view is used to represent augmented virtuality on the mixed reality continuum described in Chapter 2. Figure 5‑12 also shows the actual 3D locations of the userís thumbs within the view frustum (along with 2D overlays) and indicate distance when the user moves them away from the head. We have found this view to be very compelling for external users and I see a number of future applications in providing situational awareness to remote users via a wireless network.
Figure 5‑11††††† Immersive view of Tinmith-Metro with 3D cursor objects appearing to be floating over the incoming video image
Figure 5‑12††††† External view of Tinmith-Metro with userís body and 3D environment
The projection video is forced close to the user in this view
The user interface design is integrated into an example modelling application I have developed, named Tinmith-Metro [PIEK01b] [PIEK03c]. This application implements AR working planes, construction at a distance techniques, and the glove-based user interface described in this dissertation. This section discusses some of the implementation specific features that have been added to create a fully functional application. This is provided to give an understanding of the number of commands provided and the userís experience while modelling. While this example implementation is quite extensive, it does not define actual limitations and so many other features can be implemented within the framework discussed.
Figure 5‑13††††† Options available from the top-level of Tinmith-Metroís command menu
Figure 5‑13 is an enlarged view of the menu nodes displayed in Figure 5‑7, and shows the top-level options that are available to the user. These commands represent tasks that are most commonly performed by users of the application. Since there are more tasks than fingers available, some less used tasks are nested in sub-menus. In this example there is one spare menu slot remaining for future expansion and others can be made available by rearranging the hierarchy. The clipboard and controls menus are unique in that they perform overall system functions, and so are mapped to the special palm gestures. The clipboard and controls menus are configured so that when these sub-menus are entered, the same palm gesture is used to return back to the top-level menu. In other menus the palm gestures are used for ok and cancel operations - to quickly return to the top-level menu the user may repeatedly press the cancel palm hand (depending on the nominated dominant hand).
The menu display in Figure 5‑13 represents the top level of the menu hierarchy depicted in Figure 5‑14. Each menu node is broken down into a number of sub-tasks that are described in this subsection. Labels were selected for the menus that would fit on the display and be most familiar to the users, rather than using the actual full technique names. The following description of the tasks contains names in parenthesis that indicate the internal menu node from Figure 5‑14.
Figure 5‑14††††† Menu hierarchy of available options for the Tinmith-Metro application
This menu contains controls for various aspects of the system that affect its operation and does not perform any modelling tasks. Various camera views (camera) such as 2D top down (above, above_far, satellite), external body views (shoulder, shoulder_far, head, orbit, orbit_far), as well as the standard AR immersive view (ok) may be switched between. The current model may be saved to disk as a time stamped file (save) without overwriting previous models. The menu orientation may be switched between that of Figure 5‑13 (vertical) and Figure 5‑15 (horizontal), with vertical being the default. When testing with different users, the system requires calibration to configure for the varying placement of the position and orientation sensors. When the application first starts, the IS-300 input must be initialised by the user placing the head cursor on the horizon toward north and selecting the calibration option (orientation). Another option is provided to offset the GPS if required (gps). Quitting the running software is intentionally made difficult since it causes everything to exit and is not performed very often. The option for quitting (quit) contains a nested menu that requires alternating finger presses from each hand to prevent accidental activation.
The creation of street furniture is performed with this menu, and various prefabricated objects are mapped to options so that they may be created at the userís current location. The objects available are those that occur commonly in the outside environment such as boxes, humans, survey markers, trees, tables, rubbish bins, and lamps. With larger collections of objects, hierarchies may be used to organise them more efficiently. An unlimited undo option (undo) is provided to delete the last object created. The ok gesture commits the active selection buffer to the scene graph, while cancel deletes all the objects created.
This menu implements techniques suitable for the modelling of houses and similar structures. Street furniture style techniques (furniture) are provided for the placement of objects such as humans, tables, boxes, windows, and water heaters. Orientation infinite planes techniques from Chapter 4 are used for the capture of building structures (create). When create mode is enabled, an infinite solid region is created around the user that will be used as an input for future carving. The user then walks around the physical world, and may create walls from the head cursor with normals pointing to the left (left) or right (right), as in Figure 3‑9. The specification of the direction of the walls affects the side of the solid region carved away with CSG since planes have an inside and outside. For cases where the user cannot align themselves along a wall, world-relative planes may be created that face toward the user as in Figure 3‑10 at a fixed distance (left_toward, right_toward) but these are not normally used. An unlimited undo option (del_last) is provided to delete the last wall entered and restore the solid region. The ok gesture commits the building to the scene graph, while cancel deletes it.
This menu is used to perform all the manipulation operations described previously. Free hand translation (move), rotation (rotate), and scaling (scale) as well as fixed unit translation (nudge), rotation (rotnudge), and scaling (scalenudge) are provided. The cursor is used to select an object and any associated selection buffers. When the user is satisfied with the new transformation, it is committed to the scene graph with the ok gesture. The object can be returned to its previous placement with the cancel gesture.
Polygons in the scene may have textures applied to the surface, either prefabricated or captured using the head mounted camera. Existing sample textures are provided for grass, bricks, and clouds. Textures may also be cleared from a polygon and reset to the original base colour (clear). The colour widget may be used to specify any solid colour input by the user (colour). When camera mode is selected (video), the system selects the polygon underneath the glove cursor and prepares it for texture mapping. The final image is then captured from the camera and mapped to the surface with the ok gesture, or aborted with the cancel gesture.
This menu implements the bread crumbs technique, used to mark out perimeters along the ground. The user places down markers (marker) and after three or more markers are placed down the system connects them together in real-time into a polygon. At any time the polygon may be set to one of three specified extrusion thicknesses from the ground plane: a thin slivered polygon (set_flat), a 3 metre building (set_building), or an infinite height object (set_infinite). The infinite height object is useful for objects that are not slivers or 3 metres high and can be carved later on. The direction the polygon is marked affects whether the polygon extrudes upwards or downwards, with upwards being nominated to be clockwise (set_clockwise) or anti-clockwise (set_anti_clockwise). The current position of the hands and the colour widget may be used to nominate the surface colour (set_colour). An unlimited undo option (delete) is provided to delete the last marker entered. The ok gesture commits the final shape to the scene graph, while cancel deletes it.
Existing objects in the system may be modified using the cursor projection carving techniques described in Chapter 4. The user projects markers against the surface of an object (marker), with the first marker being used to nominate the object to carve, and create a world-relative AR working plane at the surface. If one marker is placed, a horizontal plane facing upwards is used to cut off the top of the object. Two markers specify a plane at an angle to cut off the top of the object. Three or more markers are connected into a polygon and extruded into the object, removing all parts of the object that are outside this extrusion. As each marker is placed the system shows in real-time the effect of the change so the user may immediately verify its accuracy. The length of the extrusion is normally infinite (infinite) but may be switched at any time to being a thin sliver (thin) to carve slight surface features. The CSG carving operation applied may be switched between the default (intersect) and difference which inverts the extrusion and removes everything inside the selection instead (difference). An unlimited undo option (delete) is provided to delete the last marker and recalculate the carved object. The ok gesture commits the final carved shape to the scene graph and deletes the original inputs. The cancel gesture aborts the operation and restores the original inputs.
The surface of revolution technique from Chapter 4 is implemented in this menu option. The user initially needs to create an AR working plane and this may be projected with the head cursor (data) as the most common case (as in Figure 3‑9), or placed pointing toward the user (as in Figure 3‑10) at 1 metre away (one_plane) or 10 metres away (ten_plane). Once the plane is created, the user can project markers against this plane (marker) using the cursor. At any time the user may also specify the location of a vertical axis of rotation (axis) on the AR working plane. With an axis of rotation specified the system will calculate the surface of revolution and provide real-time updates as changes are made. The current position of the hands and the colour widget may be used to nominate the surface colour at any time (set_colour). An unlimited undo option (delete) is provided to delete the last marker entered. The ok gesture commits the final shape to the scene graph, while cancel deletes it.
Clipboards have been previously described as selection buffers and provide collections of inputs to modelling tasks. Working with these buffers is normally transparent to the user since most tasks are performed with a single object and the system automatically sets up temporary selection buffers. When the user desires to work with collections of objects, this menu is used to gather multiple objects into selection buffers.
A new selection buffer is created and made active when a user nominates an unselected object (obj_select). If an object is nominated that is already part of another selection buffer it will be transferred to the current buffer. The alternative is to select a buffer (clip_select) by nominating an object, and the buffer containing this object will be made active. If no buffer is active then one will be created and the object added to it. New selection buffers may also be created and made active without the selection of an object (clip_create).
The entire list of objects in a buffer may be deselected (clip_clear) or deleted from the 3D world (clip_delete). As previously discussed, selection buffers maintain a circular list with a pointer indicating the last object that was added. The circular list may be rotated (clip_rotate) to shift a different object to being last. The last object in the active selection buffer may be deleted from the 3D world (obj_delete). Since the system supports a hierarchy of models, the selected object may not be at the desired level in the hierarchy. The last object added to the buffer may be expanded (obj_expand) and the selection is adjusted to include the geometry at the next level up in the hierarchy.
The user interface was developed using an iterative design approach to take initial ideas and then produce a more mature interface over time. At each stage of the design, the Tinmith-Metro modelling application was used to test the interaction techniques with both myself as well as novice users to gain their feedback. Brooks discusses insight into the design of user interfaces and states that the uninformed and untested intuition of the designer is almost always wrong [BROO88]. He suggests that interfaces must be refined with real users, and this section discusses the studies performed and the changes that were made based on this feedback.
As discussed previously, I decided that the use of tracked pinch gloves for pointing and command entry would be an efficient way of interacting with 3D objects in an outdoor AR environment. The initial idea was to use gloves with metallic pads that would allow finger tip to thumb, finger middle to thumb, and finger tip to palm inputs (similar to the Kitty hand worn keyboard [KITT02]), with a total of 24 menu options (12 for each hand) available at any time to select from. While allowing a large number of items at any particular time, I found this design to be uncomfortable when I tried to select all 24 inputs. The presentation of 24 menu options on the display would also produce a confusing menu for conveying the appropriate finger mappings and consume a large amount of screen area. To keep the menu simple and within the limits of human dexterity, the design was reduced to having a single menu node for each finger, and by pressing the fingers into the palm the menu would reset to the top level. To present the layout of the menu, a horizontal strip of menu options was fixed across the bottom of the display to correspond with the layout of the hands when they are laid out flat. Figure 5‑15 shows the layout of the user interface with the horizontal menu options presented within a transparent green window.
The initial user interface implementation was designed to support the first Tinmith-Metro modelling investigations [PIEK01b]. The first evaluation of the design with a functional application was performed in May 2001. This evaluation was performed by my supervisor and I, who were both very familiar with the Tinmith-Metro software and user interface. The menu system worked quite well for selecting commands, as the menu labelling was easy to read and the menu item to finger mapping was straightforward. A problem discovered quickly during trials was that the menu layout was too complex and designed to reflect every single primitive operation provided by the modelling engine. This menu layout was too generic in that every operation was customisable, and made most tasks too difficult or time consuming to perform. Based on this observation, the menus were later redesigned and simplified in April 2002 to be more task oriented. Since the user will be performing common tasks most of the time these need to be made as simple as possible.
Figure 5‑15††††† Original horizontal menu design in immersive AR view
The user interface was also tested with an indoor VR application as shown in Figure 5‑16, where a similar modelling application was interfaced to a Polhemus magnetic tracker [PIEK02e]. While the user interface works well indoors, I have not performed a formal user study to compare it against existing VR techniques. Since the totally immersive VR system blocks out the view of the physical world, the user is not able to see their hands. The user cannot take advantage of the visual mapping between the menu items and their fingers, and they must rely on their own sense of proprioception instead. The main intention of the VR work was to demonstrate that the new user interface was useable across both indoor and outdoor applications. This allows people to use one interface to control environments in a variety of locations without relearning new interactions.
The first Tinmith-Metro application was taken to five different institutions in Europe and the USA in October 2001 to demonstrate the user interface to various researchers and get their feedback. Before using it themselves, the users (a total of 8 from the various institutions) were given a brief presentation explaining how the fingers map to the menus and then a live demonstration. After the initial distraction of viewing the Tinmith-Metro application (in some cases using an AR system for the first time) users were able to navigate through the menus and perform simple object manipulation tasks. Initially, users were slightly confused by the availability of up to 8 selection fingers instead of using only the pointer fingers as most people are familiar with. A number of comments were made that the horizontal menu strip was difficult to understand because when using the interface the user must hold their thumbs up and hence the hands are held vertically - having the menus in a vertical orientation would be more appropriate. Comments were also made that pinching and cursor movement with the same hand causes unexpected cursor motion. By using the non-dominant hand for pinching and the dominant hand for cursor manipulation, these spurious hand motions can be prevented.
Figure 5‑16††††† View of the user interface being tested in a VR immersive environment
To support my new construction at a distance techniques [PIEK03c], the user interface was modified based on the comments from the previously described experiences. The menu strips were modified to be vertical, and employed a more compact and efficient layout (as used in the latest Tinmith-Metro). The menu hierarchy was redesigned to be more task oriented and user friendly. Many of the features that were thought to be important previously were either never used or not required for the new simplified modelling techniques. The removal of these features allowed for a greatly streamlined command structure. An example of a feature that was removed is the menu options to allow the use of trackball mice and head cursors to perform manipulations on objects. These manipulation options were never used since the gloves could perform the same tasks easier, and so the gloves are now permanently activated. Another new feature is the extra green boxes at the top of the menu - rather than automatically jumping to the top-level menu during a palm press, the menu shows the command for when this gesture is used on each hand. A common use of the palm mapped gestures is for ok and cancel operations, depending on which hand is nominated to be dominant by the user. As a result of these changes, the interface was more user friendly and less reflective of the implementation details. When I tested the new Tinmith-Metro outdoors, the improvements in both the menu design and the construction at a distance techniques allowed me to more easily model structures with greater complexity.
During September 2002 I helped present a two day course on AR technology at the University of Tampere in Finland. As part of the course, the students were able to take the Tinmith-Metro application outdoors and use the street furniture application to experience AR modelling first hand. As in previous studies, the students were given a presentation about the user interface and then shown a live demo to see the system operating. In this study approximately 30 students participated and only 5 of them had any prior use of a HMD, with none having any AR experience. The overall trial ran for just over 3 hours and each student used the system for approximately 5 minutes, including time to don the equipment. Since the users had no previous experience with AR systems, initially they were surprised and pleased with the augmented overlay of 3D objects. The users were talked through the process of creating several prefabricated objects and then manipulating them using the hands. While most users required a minute or two to become familiar the interface and made several mistakes, some users were very fast in adapting to the new interface.
Some users experienced difficulty with manipulating objects. The move task required the use of both hands, one to control the menu for start and stop commands and the other to perform the manipulation. This was discussed previously and implemented to avoid having the same hand perform both tasks. I observed the users attempting to use the same hand to select an object, drag it, and then release the fingers to drop it. The mental model employed by the users was similar to that used with a desktop mouse dragging an object. Unfortunately the model must be more complex to support many operations using only a small number of inputs. Another observation was that many users did not seem to notice the menu boxes changing colour to indicate they selected the item, and required more feedback to know what was happening. Currently a beep is emitted at the end of each operation, but the meaning of this beep is hard for novice users to understand what has happened. The volume of the beep was also too low for a noisy outdoor environment; it was quite windy during the trials outside and people were also discussing the live AR view on the laptop display. The most difficult part of this evaluation was that it was initially quite intimidating for the user since they were being asked to perform many tasks straight after wearing an AR system for the first time in their life.
During testing and demonstrations of Tinmith-Metro, I have gained considerable experience with the application and gained an insight into the user interface. An interesting side effect of the suggestion by Brooks that commands and operations should be separated is that hand/arm fatigue was not found to be a problem when using the system outdoors. With most of the commands being performed not requiring an input cursor (except hand-based manipulation, scaling, and rotation) the menu can be easily navigated with the hands at rest on either side of the body. If head-based pointing is used in preference to the hands, the time with the hands at rest can be further increased. By providing a number of different cursors and manipulation techniques, the user is also given a choice as to how they would like to perform the required task. The best technique to use depends on the userís skills, the environmental conditions, and their personal preference. The most sensitive form of interaction is the one and two-handed cursor techniques, since they rely on adequate tracking of the hands which is more difficult than tracking the body and head. The vision-based tracker in use suffers from failures when lighting conditions are too bright or too dark, and is not able to track the hands when the image is blurred due to fast motion. In this case, the user is still able to translate, rotate, and scale the object using the nudge commands, or translate the object using the head cursor. Since the head tracker is very fast and smooth, the user is able to easily move around objects attached to the head cursor. The question of whether hand or head movements of objects outdoors is superior given the tracking conditions available is still an open problem and would be interesting future work.
The start of this chapter described how speech technology was not feasible for implementation in this user interface. There have been a number of recent improvements in speech engines with commercial products such as Nuance, IBM ViaVoice, Dragon Dictate, and Microsoft SAPI. These systems are still very CPU intensive and are developed to work on the latest processors, and so require a second computer to be realisable. The second computer may be either attached to the backpack or placed indoors with the speech audio and recognised text being transmitted using a wireless network. I would like to implement speech controls for the menu so that the user can either supplement the existing glove command entry or replace it with speech entirely. Speech recognition allows extra commands to be added beyond the ten currently available at any menu node and improve the flexibility of the user interface. The accuracy of the speech engine may be further enhanced by supplying a limited grammar of available commands generated from the menu hierarchy.
With a stable user interface to use as a benchmark, performing formal user studies comparing newly designed input techniques is now possible. Interfaces such as speech could be trialled, as well as comparing against 2D input devices such as trackball mice and tablets to verify whether the direct 3D manipulation interface really is an improvement.
This chapter has presented the design of a new user interface I have developed for use with mobile outdoor AR systems. This user interface is designed to be implemented within the limits of currently available hardware so that it can be tested and iteratively refined. Vision tracking and fiducial markers placed on the thumbs of gloves worn on both hands are used to supply input cursors for selection and manipulation. A screen-relative menu system is provided where up to ten possible commands are mapped to pinches and palm gestures with the userís fingers. Interaction modes with zero, one, or two-handed input techniques are provided to manipulate objects with translation, scaling, and rotation operations based on AR working planes. The user interface is also used to control construction at a distance operations and create markers against the underlying AR working planes. The interface also uses a number of display-based techniques that are needed to provide user feedback and develop useable modelling applications. Various features such as 2D and 3D overlays are useful for presenting necessary information, and external VR views of the body are used to gain situational awareness not normally possible from within an AR immersive view. By informally evaluating the interface, insight has been gained from untrained users as to how intuitive the interface is. This feedback has been used to improve the speed and efficiency of the user interface. The Tinmith-Metro application was developed using this interface to demonstrate its usefulness for supporting complex AR modelling applications.
ďA fax machine is just a waffle iron with a phone attachedĒ
This chapter presents a new software architecture that I have developed, named Tinmith-evo5, for the development of the applications described in this dissertation. In order to implement a modelling application with AR working planes, construction at a distance, and user interface, a complex underlying infrastructure is required to simplify the development. Currently there are a limited number of existing toolkits for the development of 3D virtual environment applications, but each is optimised for a particular feature set different from the ones that I require. Tinmith-evo5 is designed for the development of mobile AR and other interactive 3D applications on portable platforms with limited resources. The architecture is implemented in C++ with an object-oriented data flow design, an object store based on the Unix file system model, and also uses other ideas from existing previous work. The Tinmith-evo5 software architecture addresses the following problems currently affecting mobile AR and similar environments:
∑ Hardware changes rapidly over time, and so should be abstracted to allow portability across different environments without changing the source code.
Mobile AR is limited by portability constraints and choices must be made between large and powerful or small and less capable equipment. Software for outdoor use must be efficiently designed and be able to run on mobile hardware that may be a number of generations behind current state of the art indoor computers.
3D graphics systems traditionally operate using a flat Earth model and do not readily deal with large areas of the planet that can be roamed with a mobile AR system. Being able to handle coordinates that span a wide range of scales, from millimetre-level tracking of the hands to moving over hundreds of kilometres of land is required.
User interfaces for mobile AR are quite primitive and there is limited toolkit support for developing applications. This problem is difficult to solve and current development in this area is quite immature.
This chapter begins with a design overview, followed by a summary of previous work describing existing systems and their features. The design of the architecture is then described, including concepts such as data flow and object distribution. The object storage system forms a core part of the architecture and is described in the following section. The next section describes the more interesting aspects of the implementation of the software. The abstraction of trackers and sensors with four different representations is then described. The usefulness of this software architecture is demonstrated with the applications developed for this dissertation and a number of extra demonstrations presented at the end of this chapter.
Three dimensional environments are a challenging area to develop applications for since hardware devices are constantly evolving and non-standardised, there are a number of approaches to user interface design, and each application has different requirements. In 2D desktop environments, similar problems are better understood and high-level software toolkits are available so developers can focus on writing applications rather than implementation details. With 3D environments being newer and less understood, most available software toolkits are simple and low level, such as hardware abstraction layers. Shaw et al. explain how the development of high-level software is not possible until there is a stable base of low-level toolkits to support them [SHAW93]. Beyond simple abstractions however, there are only a few software systems that attempt to address higher-level problems and each is designed for supporting particular types of applications. This chapter develops a high-level architecture that is demonstrated with a mobile AR modelling application but may be also useful in other application domains.
The first main concept used in the software architecture is that of data flow. Figure 6‑1 depicts this data flow from an overall perspective, with sensor data arriving into the AR system, being processed by specific application code and configurations, and then rendered to the HMD of the user. The data flow model is supported by the use of objects to perform specific actions such as processing tracker data, combining results, and rendering 3D graphics. Objects allow problems to be broken down into simple tasks to simplify software development. Objects are connected together into a directed graph, and as new values enter the system, the values are processed through the graph as a flow of data, adjusting the current state and eventually rendering to the HMD. These objects can be distributed across multiple processes or computers in units named execution containers, with the data flow occurring over a network when required.
Figure 6‑1††††††† Overall architecture showing sensors being processed using libraries and application components, and then rendered to the userís HMD
Research toolkits are designed using many different methodologies and are difficult to use together because of conflicting requirements. In the future these may become standardised but for now I avoid trying to make immature and opposing toolkits work directly together so that I can perform research into new ways of developing software. All the components of the architecture are developed from the ground up using a common methodology, with abstractions to hide away any differences from external libraries that are required. The goal is to not to treat the application as a combination of scene graph, tracking library, and shared memory, but instead as a single entity with blurred boundaries.
As part of the integrated component design methodology, the entire system has been structured around the model of a memory-based file system. Instead of using global variables to reference the many objects available in the system, an object storage system based on Unix file system semantics provides a logical interface that is easy to understand. All objects that process data in the system are stored in this object repository, making them accessible to other objects in the system through discovery at run time. The ability to perform distribution across multiple computers is added as an extra component using object data flow, and is not an internal part of the architecture that imposes a constant performance penalty whether in use or not.
The most important goal with the design of this software architecture is performance. Due to limitations in wearable computer hardware, it is important that as much work as possible be extracted out of the resources available. The C++ language and optimising compilers are used for all development, supporting both low-level code and high-level features such as object-oriented programming. The renderer that forms a core component of most applications is implemented using OpenGL and provides high performance graphics support when 3D acceleration hardware is present in the system. The software has been used on a number of small and relatively slow computers and is capable of running adequately in most cases, the exception being the rendering of large 3D scenes.
There has been a wide range of previous work in the area of software toolkits for the implementation of VE applications, although there is no complete solution and is still an active research area. Shaw et al. describe how developing high-level tools requires a good understanding of the types of interactions, as well as low-level toolkits to provide necessary support. To support this statement, Shaw et al. explain how high-level tools for 2D user interfaces did not appear until researchers gained sufficient experience developing interfaces with this style. These high-level tools also did not exist until other more low-level toolkits were created to provide the necessary services. Currently the VE area is still quite immature when compared to 2D environments, and so more understanding is required to implement a usable toolkit.
This section discusses a number of low and high-level toolkits that have been developed to help implement VE applications efficiently. There are a number of areas that need to be addressed, such as data distribution, rendering, user interaction, tracker abstractions, and rapid prototyping. There is no single toolkit that solves all problems however, as tradeoffs must be made to support one feature at the expense of another. Tinmith-evo5 uses many of the ideas developed in these toolkits and applies them to the problems that need to be solved for mobile outdoor AR. Software architectures for areas such as wearable context awareness or other high-level information sharing are not described since they do not support 3D user interfaces.
Virtual environments typically require the use of tracking devices that measure three or more degrees of freedom. Tracker abstraction layers are implemented to hide away various differences in hardware and provide a common model so that the application only needs to be written once for many devices. Similar abstractions are commonly used for 2D desktop input devices such as mice and keyboards. Researchers such as Shaw et al. and Taylor et al. identified the need to have these abstractions in 3D environments and implemented software toolkits such as the MR Toolkit [SHAW93] and VRPN [TAYL01]. Shaw et al. saw the MR Toolkit as being an important low-level component for future development of higher-level user interface libraries. With many VE systems of the time being implemented using multiple computers, both MR Toolkit and VRPN are designed to support the transmission of tracker updates via packets on a network. Trackers can be moved to different machines or replaced with different devices without requiring any changes to the source code of the applications that use them.
Other toolkits have also been developed, increasing the levels of abstraction beyond just input devices: Hubbold et al. developed the MAVERICK system [HUBB99], Bierbaum et al. developed the VrJuggler libraries [BIER01] [BIER98], and Kelso et al. developed the DIVERSE libraries [KELS02]. Each of these systems provides an inner kernel that connects together various components such as input abstractions for trackers, support code for processing data, and output abstractions to various rendering systems such as OpenGL, IRIS Performer, and Open Inventor. These systems all provide similar functionality to those discussed previously, providing network transport for tracker device updates and the ability to modify configurations either at startup or at run time without modifying source code.
Another recent tracker abstraction is OpenTracker by Reitmayr and Schmalstieg [REIT01b]. It implements abstractions similar to those discussed previously, but in the form of a filter graph for arbitrary processing. Three different types of object nodes are provided: source nodes to read values from trackers, filter nodes to take source values and perform operations such as smoothing and conversions, and sink nodes to take the results and output them to other parts of the system. Using XML configuration files, the user can define complex filter graphs without requiring source code to be modified.
Other more high-level libraries (such as Coterie [MACI96], Divisionís dVS [GRIM93], and the Sense8 World Toolkit [SENS03]) discussed later in this section also include abstractions similar to those previously mentioned.
One current area of investigation is the implementation of distributed virtual environments. This involves simulating entities on machines and then viewing them on remote clients over a network. The main focus of this research is on the protocols rather than the toolkits, such as SIMNET by Calvin et al. [CALV93] and NPSNET by Zyda et al. [ZYDA92] that use protocols similar to DIS [IEEE93]. These protocols are not able to send full scene graphs or object databases, but instead only send position, orientation, and other simple data for each entity. The client viewer is responsible for supplying the models and performing the rendering, which is not defined by the protocol. By making these restrictions however, these systems are efficient and tend to scale up to large numbers of entities and computers.
Other systems such as the previously described BARS extend the distribution of data to wearable computers. Brown et al. describes the development of a data distribution mechanism for events to support collaboration between multiple users [BROW03]. Since the system only propagates events, complicated geometry and animation are not transmitted over the network and are assumed to be stored locally. The propagation of events may be used to convey objectives to mobile AR wearable users, communicate messages and reports, and update entity locations and attributes in a database.
A common problem with many distributed systems is that network data is decoded using fixed programs that can only handle protocols known in advance. For example, software that handles DIS protocol requires modifications if any of the enumerated values change meaning. These problems are addressed by Watsen and Zyda with the Bamboo system [WATS98a] [WATS98b]. Instead of having a single monolithic code base, Bamboo contains a small kernel that can perform the loading of other modules. When information arrives from the network that cannot be decoded, a request is sent for a decoder object. When the decoder object arrives, Bamboo loads it into memory so that the incoming packets can be processed. Bamboo uses a plug-in architecture to allow reconfiguring of software at run-time, and does not require modules to be stored locally. Other systems such as Octopus by Hartling et al. [HART01] also support distributed VE applications based on similar protocols to that used in Bamboo.
There are a number of software systems that implement higher-level abstractions, using concepts previously described as a base for further development. Two early commercial toolkits are dVS by Grimsdale [GRIM93] and the World Toolkit by Sense8 Incorporated [SENS03]. These are both designed to be used by developers to develop interactive VE applications involving many computers, different tracking devices, and various kinds of output technology such as video and audio. dVS is implemented using an architecture based on the concept of actors. Each actor performs a task such as display or sensor inputs, and these run in parallel and communicate with each other. This parallelism naturally supports multiple users for collaborative tasks. World Toolkit also provides a wide range of features such as a scene graph, abstractions for absolute and relative tracking devices, objects containing properties, the ability for properties to trigger events, and sharing of data across multiple machines.
There are a number of rendering and scene graph libraries available, with the most commonly used ones being based on hardware accelerated OpenGL calls. OpenGL was first developed by Silicon Graphics and provides low-level interfaces to produce realistic 3D graphics while hiding this complexity from the application programmer. To provide more functionality for application development, Silicon Graphics developed Open Inventor [STRA92] and IRIS Performer [ROHL94]. These are both scene graph libraries but Performer is designed for high performance visual simulation applications while Inventor is used for more complex user interfaces. Similar capabilities for rendering scene graphs are provided in Sun Microsystemsí Java3D libraries [SUNM97].
Scene graph libraries require a format to represent objects when they are being stored or transported across a network. The VRML 2.0 standard was developed by the VRML Consortium [VRML97] and is a declarative language specifying the layout of a scene graph, with a format similar to that developed for Open Inventor. VRML also includes numerous features for implementing interactive applications [CARE97], such as fields and routes. Objects in VRML contain attributes referred to as fields, with inputs such as the centre point, radius, and other values that control appearance. Object attributes may also be used to provide output events, such as when a virtual switch is flicked the field will generate a Boolean event. By using a route command, input and output fields may be connected so that one object can cause changes in another. Using fields and routes implements a kind of data flow approach where changes propagate from one object to another by linking them together.
The research system VB2 by Gobbetti and Balaguer [GOBB93] is designed to demonstrate the use of constraints to implement relationships between objects in virtual environments. When an object has been grabbed and a tracker generates an update, the constraint engine propagates these values into the scene graph, allowing the user to alter the position and orientation of objects. Constraints are one possible method of implementing the flow of data between various objects in a system.
The ALICE system [PAUS95] is a high-level authoring tool allowing novice users to implement VE applications. The user can specify object behaviours using a scripting language and then interact with the environment, exploring various possibilities. Since ALICE is more of an authoring tool, it is not designed to be extended beyond the features accessible with its scripting language and provided function calls.
The Lightning system by Blach et al. [BLAC98] is based on scene graph and data flow concepts developed by Inventor and VRML. A pool of objects is created and maintained by the application, and these are connected together to take events from various objects, process them, and then render them to the display.
The DWARF system by Bauer et al. [BAUE01] is designed as a framework for the development of augmented reality applications. A complete system is built up using a number of components which provide services that are made available using CORBA. The DWARF framework is used to connect these services together within a local host or over a network.
Many of the previously described systems only distribute small parts of their internal state to provide data for other applications. The following systems provide more complete distribution, where entire applications and scene graphs are synchronised between multiple hosts to make complex collaborative applications possible.
The Coterie system was developed by MacIntyre and Feiner to help implement applications for distributed virtual environments [MACI96]. One example of Coterie in use was for the development of the Touring Machine by Feiner et al. [FEIN97]. MacIntyre and Feiner identified a number of problems with simple tracker abstractions and set to develop a more complete solution. Coterie is implemented in Modula-3 and uses modification of language-level primitives to support the implementation of a distributed shared memory. This is integrated with packages that support an in-built interpreted language, threaded processing, tracker abstractions, and 3D animated rendering. Multiple threads in the system execute code within objects and communicate via a distributed shared memory, with each update method call passing through a sequencer to ensure accurate synchronisation between processes. All the components of the system are developed to use this shared memory, including the distributed scene graph Repo-3D [MACI98].
Frecon and Stenius developed the DIVE system [FREC98] which is based on previous distributed entity research. Instead of just generating entity updates, this system is capable of propagating nodes in a scene graph without requiring previously stored descriptions in the remote host. Nodes introduced into the scene graph are not controlled by any particular software instance, and so once created the software instance can disconnect and the scene graph nodes will remain in the memory of other running instances. Updates are handled by sending compact differences against previous objects using a multicast protocol, reducing the bandwidth used for the transmission of updates.
The current Studierstube framework described by Schmalstieg et al. is used for the implementation of various distributed AR applications [SCHM00]. Studierstube also interfaces to the previously described OpenTracker libraries [REIT01b]. The original Studierstube framework was based on Inventor and is used to implement new user interfaces such as the Personal Interaction Panel [SZAL97]. The Inventor toolkit was then extended by Hesina et al. to produce a distributed version that propagates changes in the scene graph over a network [HESI99]. Applications must use Inventor-based objects for all rendering and user interface components, and so certain operations like drawing 2D overlays on a 3D scene are difficult because Inventor does not support these. Since Studierstube is limited to distributing values that are contained within the scene graph (under the control of Inventor), any other application data will not be synchronised between instances. Schmalstieg and Hesina describe how internal application values can be stored within objects extended from Inventor base classes [SCHM02]. These application values then can be stored in the scene graph and distributed like any other graphical object. While MacIntyre and Feinerís system implements all applications using shared memory, the work by Schmalstieg et al. performs the opposite and implements everything inside the distributed scene graph. By embedding entire applications into the scene graph Schmalstieg and Hesina demonstrate how applications can be migrated between separate computers through the distributed scene graph, and processing of inputs can be distributed easily across multiple instances.
The Avocado framework by Tramberend [TRAM99] is similar in design to both Distributed Inventor [HESI99] and Repo3D [MACI98] for distributed scene graphs over a network. Avocado is based on Performer and so is designed primarily for use in complex visualisation applications. Tramberend extended the base Performer objects with a wrapper that provides storage of fields and the ability to serialise them. Private inheritance and access methods are used to force applications to work through this interface and updates are sent to the network whenever changes are made. Fields can be connected together with field connections, (similar to VRML fields and routes) and used to implement data flow style connections between objects in the system. Sensor objects provide Avocado with interfaces to tracker abstractions and service objects are used to access low-level APIs. The Scheme scripting language is embedded into the system so that parts of applications developed can be modified at run time without recompilation.
This section describes the overall design of the classes in the software architecture. Class definitions used in the software architecture can be divided into four categories Ė those for representing data values (data), those for processing input data values and then producing some kind of output values (processing), those for implementing core features that other classes can inherit or use (core), and helper code that implements interfaces to streamline development (helper). Each class can also be classified into one of the categories depicted in Figure 6‑2. Applications require classes from both high and low levels to be instantiated as objects and connected together. Each class can contain nested sub-objects of other class types or primitive C++ values such as pointers, floats, integers, and strings.
Figure 6‑2††††††† Layers of libraries forming categories of objects available to process data
Figure 6‑3††††††† Data values flow into a node for processing, producing output values
Data objects in the system are used to supply input for processing objects, which then produce an output data object that can then be propagated to other processing objects for further operations. Figure 6‑3 depicts how this input data arrives at a node and is then processed to produce new output. Objects can be connected together into a directed graph that forms a flow of data through the system. Figure 6‑4 depicts how data values initially arrive as tracker inputs, and are then processed in various stages of a virtual pipeline before reaching the user in the form of rendered output. This figure depicts categories for the objects used in various stages of the pipeline, but is only an approximate model.
The data flow model is implemented by having processing objects listen to events that are generated by data objects. When the data object changes to a new value, interested listening objects are notified of this change via callbacks. This is similar to the observer/observable pattern described by Gamma et al. [GAMM95]. Any number of processing objects can listen in on a data value, and processing objects can have any number of output values for others to listen to. The use of data flow to propagate values through various processing objects has been performed in systems such as OpenTracker [REIT01b] and Avocado [TRAM99]. The VRML concept of fields and routes allows embedded scripts to process values when they change [CARE97] [VRML97]. Based on this previous work the data flow approach seems an ideal solution, especially considering that 3D applications tend to perform data processing in sequential steps.
Figure 6‑4††††††† Expanded view of data flow model showing stages of processing
Objects in the system are represented using the C++ compilerís native internal format. It is not possible to simply take the binary data for the object and directly save it to disk or transport it across a network since it is specific to the running process only. The ability to save the state of a running system and then restart it at a later time or transfer it to another machine is desirable, and so a generic format that can represent application state is required. Serialisation is not available in C++ by default and so extra logic is provided to handle this requirement (the implementation details are discussed later). A structured XML format is used by default, with a binary format used to reduce the size of the data when required. Nested objects are processed by recursively calling the serialisation code and the results are assembled together for the top-level object.
The first use for a serialisation capability is to store persistent configurations on disk. The XML header is parsed to determine the object type, matching C++ objects are instantiated, and are configured to contain the values in the XML data. When the application is shut down these objects may be serialised back to disk so that it can resume its previous state at a later time. The serialised XML files may be used as a configuration system, and can be edited with a text editor or stored in a database. These objects can also be modified and reparsed at run time to adjust the application while in operation. This allows changing aspects of the application without having to resort to slower interpreted language support. While internal components such as network and disk interfaces cannot be serialised in this fashion, the parts of the application that a user would like to change are supported. Similarly, OpenTracker uses XML-based configurations for filter graphs [REIT01b], VR Juggler uses text files for tracker reconfiguration [JUST01], and Diverse uses compiled C++ modules switchable at run time [KELS02].
This serialisation capability may also be used to implement distributed applications. An important feature is that the system does not force the user to use this capability. In most cases, applications are implemented as single processes and interactions between objects occur using simple function call-based callbacks. The overhead of supporting the callback updates is very minimal when only local data is used. In contrast, many other systems require the application to use IPC interfaces even when operations are being performed locally, taking its toll as a large penalty on performance. Rather than use an internal architecture based around distributed shared memory [MACI96] or a scene graph [HESI99] [SCHM02], the distribution of information across multiple processes can be performed using objects in the data flow model.
Figure 6‑5 part (1) depicts two objects that are connected via callbacks, and the listener is notified when the source signals a change has been made. Figure 6‑5 part (2) depicts how objects can be inserted to implement distribution. When the source generates a new value, the Tx object serialises the new value and then transmits it over a network or other IPC mechanism. The Rx object at the destination receives the incoming data, deserialises it in place using the same class and then signals to the listeners of the object that new data is available. The listener object then receives a callback in the same way as in Figure 6‑5 part (1). This distribution mechanism is transparent to the listening objects since it is implemented using the same interfaces as any other processing object. The object store described later automatically provides network distribution when required so that the programmer does not need to implement this functionality.
The mechanism used for distribution (via callbacks and a possible network interface) is efficient because updates are only sent to those processing objects that are interested. Each object is stored on a particular server and other clients can make requests to receive updates when changes are made. For small systems, this is more efficient than broadcast protocols, although for large systems with thousands of processes each requiring a value this may not be appropriate. By using proxy processes, cached copies of values may be further distributed to others, which can assist with scaling. If a client needs to change the master value, the server must be configured to circularly listen for events from the client, or allow updates to be forced in via the network command interface (described later in this chapter). Any changes forced in by the client will be lost by the server when the next incoming value arrives from the source, so this method is only practical when the value is no longer updating.
Figure 6‑5††††††† Network distribution is implemented transparently using automatically generated serialisation callbacks and a network transmission interface
Systems containing many objects that interoperate require techniques to organise this complexity. This section introduces the concept of an object store based on a model of the Unix file system. This concept takes advantage of the ease with which users normally deal with files on a disk, and how the storage of objects can be directly mapped against the model and the benefits this provides.
The Unix operating system (and clones) implements a hierarchical file system to organise and store data [MCKU96]. File systems provide an abstraction to simplify the storage of data on a disk that is otherwise just a raw linear collection of fixed size blocks (typically 512 bytes). Files can easily exceed the block size and so higher-level abstractions are required for storage. An inode contains information about a file on a disk as well as a list of ordered pointers to blocks containing data. Each inode contains a unique identifier and is stored in a list at a fixed location on the disk.
Directory structures were developed to store mappings between human readable text names and numeric inode values. Directories are also stored using inodes and have an associated unique identifier. Since both directories and files are represented using inodes, directories can provide text names for other directory inodes and so form a hierarchical tree structure. A top-level root inode (with identifier 0) is used to represent the root directory (/) of the structure. Nodes in the tree can be accessed by specifying the name of each directory joined together using forward slash (/) characters. Path names that begin with a / character are referred to as absolute paths and are relative to the top-level root node. Other path names starting with a name are referred to as relative paths and are accessed from the current working directory. Paths may contain aliases that have special meaning - the name . (single dot) is a relative reference to the current directory, while .. (two dots) is a relative reference to the parent of the current directory. Each file and directory is named relative to its parent and the full absolute path name is not stored anywhere. This allows changes at the top level to be instantly inherited by all children.
Unix file systems implement hard links, multiple directory entries referencing a single inode value. This allows the same file to appear to exist in multiple locations but in fact is using a single set of blocks on disk. Modifications to one file will immediately affect others. Inodes store reference counts so that the disk blocks are not removed until there are no more references. A second link type named a symbolic link is used to provide a path name-based link to another file. Directory entries can store mappings between names and inodes, and also names and other path names. When the kernel encounters a symbolic link it performs a look up of the link name to find the appropriate inode and then resumes the previous look up in progress. Since symbolic links point to paths and not inodes, a destination file can be replaced and all links to it will update automatically. Hard links require each link to be changed since the inode number of the new file is different.
One problem with systems that store large collections of objects is accessing and updating them; the traditional approach being the use of global variables. Each module that needs to reference other objects must include definitions for the global variables, and suitable names must be used to avoid namespace collisions. Having global objects requires the compiler to statically declare these in advance, and hence cannot be changed at run time to suit conditions. To overcome this problem, systems such as dVS [GRIM91] and COTERIE [MACI96] implement the concept of a repository where objects can be stored for later retrieval based on a key. The Windows operating system also implements a registry, which is a hierarchical database of values stored on disk and used to configure the operation of the system from a central location. These runtime style storage systems can be modified without recompilation to store a variety of values, and do not require statically declared objects. Programmers may independently write modules and are only required to agree on the naming convention to reference the shared objects. Referencing items stored within object-oriented databases has also been implemented previously using query languages such as XMLís XPath [CONN02]. XPath allows for the searching of objects meeting some kind of criteria (similar to SQL and relational databases), but was not intended to be used for exact references like the Unix file system model.
Tinmith-evo5 integrates a number of concepts to develop a hierarchical object store. Instantiated objects in the system are created in memory (statically by the compiler, or dynamically at run time) and then a pointer reference is placed into the object store. Rather than just implementing a hash of names to retrieve object pointers, the object store is based around the Unix file system model described earlier. Path names are used to traverse a tree of directories and files, and the inode values no longer point to lists of blocks but are instead pointers to memory addresses. These memory addresses are the locations of objects and method calls can then be made just like with any C++ object. Figure 6‑6 depicts code fragments that demonstrate the storage of objects, retrieval and modification, and debugging.
Figure 6‑6††††††† Examples demonstrating usage of the hierarchical object store
On the surface, this file system approach appears to give similar results to those achieved with other systems using names to look up objects. The real advantages are gained when the Unix file system model is taken to its full extent to provide a number of interesting features. Hard links may be implemented by having multiple locations in the hierarchy point to the same object address. This allows code to use new naming conventions while still supporting older names for legacy source code. Symbolic links can be implemented by storing a path name redirection, so when the object store is traversing the internal structures it will recursively look up the linked path names. Symbolic links implement much of the same functionality of hard links, but may also be used to provide dynamic switching of objects. For example, if a system contains both GPS (at /human/body/gps) and vision tracking (at /human/body/camera), then a symbolic link can be created at /human/body/tracking that points to the currently active tracker. The true source of input devices may be concealed from the developer using symbolic links as an abstraction layer.
During the implementation, I added several optimisations to reduce memory consumption and unnecessary look ups, and this resulted in the final version diverging slightly from the Unix file system model. Instead of hard and symbolic links, I implement copy links and pointer links, which have similar operations for some purposes, but some notable differences. Copy links are similar to hard links but do not actually share the same object pointer. Instead, a copy of the object is made for the link destination, and whenever the source is changed the object store copies the new updates into the destination object using data flow. The reverse is not true however, and if the destination is modified it is not copied back. These links were implemented using a copy since the object itself actually contains its own name and parent pointer. This prevents multiple names sharing the same object pointer but makes it possible for an object to quickly find its parent without having to traverse from the root node. Pointer links are the same as Unix-based symbolic links in that they store a path name redirection in the object store.
In object-oriented languages like C++, objects may be contained inside other objects, and this is referred to as composition. Figure 6‑7 depicts how a simple object could be designed that can store a position value for a location on the Earth. In this example I use spherical (LLH) and grid-based (UTM) coordinates, with an object implemented to represent each type. These objects would also normally have methods to access the internal values and set them to desired values (not shown in this example). Since coordinates in both spherical and grid-based formats are commonly used, a Position object acts as a container for both types and keeps the values synchronised using internal data flow processing. To gain access to the internal LLH and UTM values, external code may reference these directly if declared public or otherwise use access methods if declared private.
Figure 6‑7††††††† Simplified layout of composite Position class, showing nested objects
Using the object store described previously and the example in Figure 6‑6, the Position object could be stored at the path /human/body/position. To retrieve an object pointer to this object the call Position::getStorage(ď/human/body/positionĒ) is used. Using standard C++, pointer->getLLH() or pointer->llh can be used to access the spherical LLH values. When using a file system-based object store, it is also logical to store references to the child objects at sub paths to the parent. The spherical LLH child object can therefore be accessed directly using the call CoordLLH::getStorage(ď/human/body/position/llhĒ). Both the parent and child objects are referenced in separate parts of the file system tree, but still remain joined together as a single object and so are still accessible to traditionally written code. The other advantage to this scheme is that since the file system is dynamic and can be traversed, child objects may be added or removed at run time (not just at compilation), and accessed without statically compiled names. Code can discover and access the contents of objects easily, allowing the writing of very generic code. Given a Position object, the call pointer->getNode(ďllhĒ) can be used to dynamically retrieve the LLH child object from the parent. While objects added at run time are not visible to standard C++ code, dynamic access, serialisation, and callbacks are fully supported.
Many OO-based languages implement containers to store objects based on a key: C++ implements STL hashmap, Java implements HashMap, and SmallTalk implements Dictionaries. Some systems have been implemented that use containers to implement hierarchical structures of stored objects. An alternate implementation is to use the entire path as a single key, but this is not hierarchical storage. All of these implementations are different from my object store because they only store pointers to an object but do not handle the child objects contained within. Languages such as Java and SmallTalk support the run time discovery of child objects but the use of a consistent file system approach for all levels of the hierarchy is not performed. The file system approach is even more useful in languages such as C++, where run time discovery is not normally available.
The Tinmith-evo5 architecture is implemented in C++ to allow for an efficient implementation of the software. There is very limited run time support (avoiding features such as garbage collection) to hinder performance and compilers can generate optimised code, making it ideal for machines with limited resources. The C++ language does not provide some of the features needed for the implementation of the desired software architecture, so extra code is supplied to provide these. This section describes some important implementation details for the software architecture.
C++ is a statically compiled language that provides object-oriented programming with inheritance, support for templates, and a macro pre-processor. Each object that is a part of the software architecture is required to inherit the abstract class Storable to provide interfaces that the object store and serialisation components require. A custom developed code generator TCC is used to generate internal information for each object. The Storable class uses this generated information to provide the discovery of internal objects, with the standard C++ RTTI functionality being too limited to use. A sample C++ object for an InterSense IS-300 tracker is demonstrated in Figure 6‑8, with object and method declarations depicted. Special wrapper macros are used to indicate callback functions and internal variables that should be processed. These macros pass through during C++ compilation but are used by TCC to easily find the desired values without having to write a full C++ parser. The code generation is performed automatically because it is tedious and error prone for humans to do this by hand. When internal changes are made, TCC is used to regenerate the derived code in a separate file which is hidden from the programmer. Separate definition files (such as used by CORBA and SUN RPC) are not transparent and require the programmer to keep the definitions synchronised with the implemented source code.
Figure 6‑8††††††† Edited extract from the is-300.h orientation tracker C++ definition file
Figure 6‑8 depicts statically compiled methods such as getOrientation() and getMatrix() that are used to access internal values in the object. Inheriting the Orientationable and Matrixable interfaces in the class implements polymorphism and the use of the class as a generic tracker. The internal Storable methods getVarList(), getVarType(), and getVarPointer() are used for the run time discovery of object contents, providing similar functionality to SmallTalk and Java. The getNode(ďnameĒ) method is implemented by Storable and can be used to traverse to children objects, and is also used by the object store. The use of these method calls does add small overheads for look up against internal object tables, but only has to be used when static references are not possible.
This object discovery mechanism is used to automatically implement serialisation functions. Storable implements toXml() and fromXml() methods that can write out objects in an XML format that is human and machine readable. An alternate format is with the toBinary() and fromBinary() methods that use a compact binary representation in an endian neutral format. An important limitation of serialisation is that it cannot handle process specific values such as file descriptors or graphical handles, and so these must be implemented using manually written code. To give an indication of how data is serialised into XML format, an example is depicted in Figure 6‑9. While this figure is very verbose and human readable, the data can be compressed considerably by removing white spaces and using shortened representations of the reserved words.
Figure 6‑9††††††† Complete XML serialisation of the IS-300 orientation tracker object
The data flow model described previously is implemented using pointer-based method callbacks. Instead of statically defining specific methods to be called when an event occurs, a pointer to any method defined with GEN_CALLBACK_H may be used. A single callback may listen to a number of event sources and a pointer to the modified object is passed as an argument to delimit between many objects. These callbacks are simple and efficient to perform since they involve a pointer dereference and then a function call, which is only slightly more overhead than using static function calls.
When a callback is attached to an object, it will be notified whenever the object or any of its children are changed. When an object is modified it will execute the handlers attached at that level of the object store, and then work up the hierarchy of the object store executing handlers until it finds an object that is not contained within a parent object. The execution of callbacks is automatically implemented for copy and set operations using C++ operator overloading, so the programmer does not need to be aware of this mechanism. For objects containing internal C++ values, these must be stored privately and wrapper methods written that generate callbacks. Figure 6‑10 is a code fragment depicting how callbacks can be configured and then executed. The first set of code retrieves an object pointer from the object store and then uses the setHandler method to specify the callback method. The second set of code uses access methods to modify the internal values individually. The disadvantage to updating in this way is that callbacks are propagated for each modification and there is no way to indicate to the system that changes may be performed as a single unit (the programmer never calls an update method). The faster and preferred method depicted in the third set of code is to declare a new temporary object and initialise the values to those desired. The temporary object does not generate callbacks during initialisation, the data is copied using an overloaded equals (=) operator, and only a single callback is executed for the entire copy. Temporary objects are used in tracker abstractions to read incoming data, with only the latest version being copied over. This reduces the amount of callback traffic in the system and helps to improve performance.
Figure 6‑10††††† C++ code demonstrating setup and execution of callbacks
The callback system and the object store are tightly integrated, with callbacks being propagated up the tree until a top-level container object such as /devices/trackers/gps is discovered. The path /devices/trackers is simply a set of empty paths used to contain objects and does not implement any object interfaces as such. This propagation of callbacks to parent objects is useful for when objects need to listen on areas of the object store but do not want to attach to individual objects since they may be transient. An example is an object listening on the entire scene graph for changes to distribute to other machines.
In most applications, a single non-threaded process is used as an execution container for objects to be connected together via data flow and callbacks. It may however be desired in some cases to distribute the application across separate processes running on a number of computers. Objects are placed into execution containers (Unix processes, not threads) and connected together using the network distribution technique described previously. Each execution container has its own memory and code, and may contain internal threads although these are discouraged. The communication within an execution container is performed using local callbacks, but these do not work across containers in different address spaces. The network distribution mechanism described previously is used to connect together execution containers so that they can communicate with each other.
Each execution container implements a NetServer object that listens for incoming connections. Clients connect to the server and make requests to listen to particular object paths. The NetServer object attaches itself to these objects in the object store, and when they change the NetServer will be notified. When the notify callback is executed, the NetServer object takes the updated object pointer and serialises it into XML or binary format depending on the clientís request. The client receives this stream of data and then deserialises it into an equivalent matching object created previously and stored locally. When the value is copied into the local object, other processing objects that are listening to this local object will receive a newly generated callback and the data flow process continues. With this mechanism objects can be easily separated into arbitrary execution containers.
When updates are made in large trees such as the scene graph, the amount of data generated can be quite large although there are only a small number of changes. The XML format allows differences to be sent that only contain the changed data. Each object records an incrementing serial number to keep track of the last object version sent so the server can send correct differences to the interested clients. Since the binary protocol is a fixed format it does not support varying differences. The type of protocol used also affects the choice of network transport. UDP is high speed, connectionless, has a 64kb packet length, possible loss of packets, and may arrive out of order. TCP is slower, maintains a connection, has virtually unlimited transmission sizes, and guaranteed transmission of data. UDP with binary mode may be used for absolute updates where lost packets do not need to be recovered, such as head trackers. TCP with XML is used for communicating with the server and testing for connectivity, and for data that cannot be lost, such as object differences of scene graph updates. The requesting client can specify the format to use during the connection setup, and can switch protocols if needed.
To test update latency, each of the network transports were tested over both wired and wireless networks with XML encoded objects. Table 6‑1 presents the results of three Tinmith benchmark applications running - a server generates an object containing a continuously changing time stamp of the current clock; a client on a remote machine receives this object via updates sent over the network and makes it available to others; a client running on the original server machine receives the first clientís object and compares the time stamp in the object against the current system time. This configuration has the effect of testing the total amount of time that it takes for an update to be sent round trip, and does not require the remote machine to have a synchronised clock. The time stamp values were obtained in micro- seconds using gettimeofday() calls under Linux, and the accuracy of these results is affected by unknown system call overheads, kernel timer granularity, other processes on the system, and other network traffic. The results show an obvious improvement in latency when using a wired network, and as expected UDP has the best latency. For the wireless results however, the performance of TCP was slightly better for the minimum and average cases, while the maximum time was double. The cause of this anomaly is probably a result of the operating system or bursts of traffic on the wireless network. In all configurations, the typical latency introduced is quite small and acceptable for interactive applications.
Table 6‑1†††††††† Approximate round trip delays experienced for network serialisation
Minimum 50 packets sent in XML format over 10 mbps wireless or 100 mbps ethernet
An object inside a server execution container is owned and updated by that container exclusively. The execution container makes this object available for other objects (both local and remote) to receive updates for further processing. The data flow approach can support circular flows of data, but is generally avoided unless one of the objects contains a mechanism to end processing and not continually propagate in an infinite loop. Alternatively, a client can connect in and upload a new value for the server to store, and it will remain until replaced by whatever source originally generated it in the server. Uploading values is not generally used but can be used to control internal values of an application such as user interface controls that are only periodically changed.
In most cases, execution containers do not require the use of threads to perform their processing of data flows. Data flow calculations tend to be very sequential and most libraries implement thread safety using a single lock, forcing most operations to run exclusively. Since the display depends on all calculations being completed it must be performed last and so cannot be run in parallel. While some calculations may be parallelised, the benefit is small considering the added costs of context switching and the complexity of implementing multi threaded libraries using locking. The programming model is therefore designed around the use of a single thread of control within an execution container to simplify the design of the system. While many calculations complete quickly, others such as video capture and vision tracking (as used in Tinmith-Metro) require longer periods of time. Separate execution containers are preferable, but in Tinmith-Metro the video frames must be available to the renderer and IPC is too resource intensive so a thread is used. Since the object store is not thread safe, a special communications mechanism using simple locking primitives is implemented to pass the video frame to the main thread.
The software is implemented to use standard POSIX style function calls and be compiled using the GNU C++ compiler. Systems that meet these requirements will be able to run the software, although some interfaces such as video and sound are not standardised between operating systems and require porting. The main development platform is Linux on 32-bit Intel architectures, although the software has also been compiled and tested successfully under FreeBSD on 32-bit Intel architectures and Cygwin on 32-bit Intel Windows environments. The ability to run on multiple platforms is useful when using custom wearable hardware with a specific fixed operating system.
The data flow model used by the software architecture is not supported directly by any operating system and so a suitable abstraction layer is required. Unix operating systems that are POSIX compliant generally provide a file-based interface to all devices in the system, with open(), read(), write(), ioctl(), and close() system calls. A generic set of classes are provided to interface to these calls for devices such as serial ports, disk files, TCP sockets, UDP sockets, and generic file descriptors. The global I/O manager object keeps track of all file descriptors in use and generates data flow events when they become ready for reading or writing. Non-blocking I/O is used with a select() processing loop to allow a single thread to process many I/O sources and time outs simultaneously. This is in contrast to languages such as Java where programmers are encouraged to use a thread for each blocking I/O device, increasing overheads and requiring thread synchronisation. The I/O manager developed for this software architecture is similar to the concept of a kernel used in DIVERSE [KELS02] and VR Juggler [BIER01].
Rendering to the display is performed using OpenGL graphics under X Windows. When running on a local server, the OpenGL graphics rendering is performed directly to the hardware using Direct Rendering Extensions (DRI). The X Windows server is used to provide window handling and the management of events from the keyboard and mouse.
Most hardware devices can be categorised depending on the style of input they provide - 2D, 3D, digital inputs, analogue inputs, etc. Tinmith-evo5 includes a hardware abstraction similar to those discussed previously, although there are some differences since each architecture focuses on varying types of applications. The hardware abstraction is designed for an extensive range of trackers and input devices with a variety of representation formats suitable for each type of input.
Tracking devices return a number of degrees of freedom, with either position or orientation or both depending on the technology being used. Some of these results may be absolute in that the values may be relative to the Earthís coordinate system, while others return their results relative to the coordinate system of another device. These distinctions are used to categorise trackers into four separate classes: Position, Orientation, PositionOffset, and OrientationOffset. Each of these classes represents 3DOF information, and for devices that produce less DOFs some of the values will be set to a constant value. For 6DOF trackers the result will be split across both an orientation and a position class. Each class contains a number of different formats internally, and by adjusting one the data flow model is used to recalculate the other values automatically.
The Position class is used to represent objects stored relative to the Earth [ICSM00]. There are a number of ways of storing coordinates on the Earth depending on the task. Polar coordinates (LLH latitude and longitude in degrees, height in metres) are used to accurately represent points on the Earth and do not suffer from distortions caused by curved surfaces. Cartesian coordinates (ECEF Ė XYZ in metres relative to the Earthís centre) may be used to represent these points with similar accuracy, but using the centre of the Earth as the origin may be confusing to understand. Grid coordinates (UTM Ė XYZ in metres relative to an anchor point on the surface, assuming a local flat Earth) suffer from accuracy degradation as the Earth is non-planar, but are easy to handle over short distances since the Earth appears flat. Each of these coordinate systems are stored within the Position class, and data flow is used so that when one set of values are changed the other values will be automatically recalculated. GPS trackers normally output LLH values and the OpenGL renderer uses custom UTM values described later in this section. The PositionOffset class represents relative position change from an absolute position. These values are stored using XYZ coordinates in metres and can be converted to matrices for use in the scene graph. Indoor trackers as well as 2D desktop mice are represented using PositionOffset.
Orientation values may be specified using a number of different formats relative to the UTM surface. Euler angles are combinations of three angles applied in a specified order to produce a final rotation, although can be confusing due to Gimbal lock problems and the order of combination producing different results. Aerospace angles are defined with heading, pitch, and roll angles similar to those used to represent an aircraft orientation. Matrix values with 4x4 elements are commonly used to represent transformations in the scene graph. The Orientation class represents values using all three formats, and automatically recalculates the other values using data flow when one set of values are changed. Orientation values are output by most head tracking devices since they generate absolute orientation. Similar to previously, an OrientationOffset class represents relative orientation change from an absolute orientation. These values are stored using 4x4 Matrix values only since Euler and Aerospace angles are difficult for users to correctly combine and understand.
Figure 6‑11††††† Mathematical operations possible between absolute and relative objects
The offset classes are designed for handling values relative to another coordinate system, and cannot be used by their own since they require a base value. Figure 6‑11 depicts all of the possible combinations of absolute and relative values, and are implemented as overloaded C++ operators equals (=), addition (+), and subtract (Ė). While two absolute values can produce a relative value, it is not possible to produce an absolute value using only relative values.
Incoming tracker data usually requires some processing before it can be presented to the scene graph or other data flow objects for operations. Using the data flow model, operations such as filtering, combining degrees of freedom from multiple trackers, conversions between coordinate systems, and performing the mathematical operations in Figure 6‑11 are possible. This is similar to the processing performed by OpenTracker [REIT01b]. When various position and orientation 3DOF classes are combined together, systems with many articulated parts can be described. While this combination may be performed easily using processing objects, it is difficult for humans to visualise the transformations involved, and so the demonstration section discusses the use of scene graphs to perform similar transformations.
Figure 6‑12††††† Distorted view of Tinmith-Metro showing improperly placed avatar objects when the resolution of OpenGLís internal values is exceeded
Rendering libraries such as OpenGL are designed to render in Cartesian coordinates. While polar (LLH) or Earth centred (ECEF) coordinates are more accurate, grid-based coordinates (UTM) are used since these are most easily understood by humans who can only see a small and almost flat portion of the Earth. Since UTM coordinates are zone-based, the flat approximation may be used across the surface of the Earth without any significant loss of accuracy. The UTM coordinate space is quite large and for a location in Adelaide the position is approximately 6145000 metres north and 282000 metres east of the UTM origin point for zone 54H. As a user of the system moves about the physical world, the origin of the virtual camera moves accordingly and the OpenGL matrices that perform the rendering will be recalculated. If the user is a large distance away from the UTM origin (such as in Adelaide and most other places) these values may become very large and begin to exceed the range of the internal floating point values in the transformation matrices. The accuracy of these matrices is dependent on the OpenGL implementation and the internal floating point representation used, typically 32 or 64 bits in size. While the programmer may take care to ensure the position transformation is within the accurate limits, when it is combined with other internal matrices in the OpenGL pipeline it may temporarily exceed the accuracy possible. Millimetre accurate detailed objects such as the cursors will shake and distort, and the userís avatar body will contain distorted and shifted parts, as shown in Figure 6‑12. The effects are most noticeable in the north direction since this value is an order of magnitude greater than east and so the effects are magnified accordingly.
To overcome these effects, the rendering system and the Position class work together to produce a new dynamic coordinate system with an origin that is closer to the area the user is operating in (typically within a few hundred metres). UTM coordinates represented in local coordinates are much smaller (in hundreds instead of millions of metres) and the Position class is called by the scene graph to translate LLH, UTM, and ECEF relative objects into this special coordinate system for rendering. This translation operation is completely transparent to the user and is only apparent when debugging the internals of objects. The local coordinate anchor is typically encoded into a configuration file or initialised at system start up, and may be moved to different locations when required, although this is processor intensive since every transformation matrix must be recalculated to use the new coordinate system. The local coordinate system does not require changes unless the user moves tens or hundreds of thousands of metres from the origin. Using this technique, it is possible for the system to operate over very large coordinate spaces while still handling finely detailed objects. This gives a large dynamic range of operation which is not possible using standard UTM coordinates.
Discrete events are handled differently in the data flow model due to their non-continuous nature. There are a number of types of button presses to handle, some examples being mouse buttons, keyboard buttons, and glove pinches. All inputs are described using a keyboard model, where the object stores an identifier for the last button activated with a press or release action. The identifier may be either an ASCII character code or an extended enumerated value for mouse buttons or glove fingers. Processing objects may listen for input device events and receive notification when they occur. Callbacks must process each event as they arrive and multiple events are executed as individual callbacks.
Keyboards directly map to the event model with either ASCII codes or enumerated constants representing inputs such as function keys, escape, and arrow keys. Mouse input devices are actually implemented as two separate devices in one, with the 2DOF motion represented separately using a PositionOffset object. Each mouse buttons maps to enumerated values such as Button1, Button2, and Button2. The glove input device is similarly two input devices, with the 6DOF tracking of the hands represented by PositionOffset and OrientationOffset classes. Each finger press is represented using enumerated constants such as LeftFinger1 and RightFinger4. Using processing objects, tracking and finger presses from the gloves can be converted into a virtual mouse with button clicks very easily.
Software development is performed indoors using desktop environments and the various tracking devices that are used for interactive applications may not be easily available. For these scenarios, using simulated tracking devices is desirable to speed up development time and allow testing using repeatable fixed inputs. Instead of reading from a serial port or network, a simulator generates data or reads it from a text file. Simulator objects may be provided for any part of the object flow Ė for example, IOdevice simulators are used to provide raw data to test parsers, and Position simulators are used to test the user moving in the scene graph.
A major core component of the software architecture is the rendering system. The renderer is a hierarchical scene graph with a structure similar to that of Open Inventor [STRA93] or IRIS Performer [ROHL94], with 3D geometry controlled by nested transformation nodes. The scene graph is implemented using similar object designs as any other part of the system, and so when changes are made these can be distributed across a network to share the scene graph with other applications. This design is similar to Coterie [MACI96] and its implementation of Repo-3D [MACI98] using a distributed shared memory. This is in contrast to the implementation of Studierstube [SCHM02] where the scene graph is the core of the system and applications are implemented around this.
A number of primitive objects such as spheres, cones, cylinders, polygons, and triangle meshes are supported as objects in the scene graph. Each object contains specific information (number of facets, position, and rotation), a local transformation, a set of styles defining colour and texture, and a polygon cache for OpenGL. When changes are made to the object or its parent, it is marked as being dirty and during render time a set of polygons representing the object will be calculated and stored for future use. Each object implements methods to perform rendering and the scene graph uses these to display the contents. The Group3D object does not render any polygons but can be used to encapsulate a number of children and apply transformations to them. When the transformation of a parent Group3D node is changed then all children will automatically inherit this change.
Since nodes in the scene graph inherit the same interfaces as any other data object in the system, they can be used to provide data for processing objects and store output data values. The transformation matrix in a Group3D node may be linked up to a tracker output so that as the sensor moves the objects underneath in the scene graph will move accordingly. Using a hierarchical model of the body, various tracking devices can be attached to different parts of the body to produce articulated models that match the physical world. Since the scene graph is also contained within the object store, each node can be represented using the file system notation described previously. For example, the thumb on the body of a human avatar can be referenced using the file system path /world/models/user/torso/left_arm/elbow/wrist/thumb, which is very logical and easy to understand. Scene graph objects also implement the standard serialisation interface, and so 3D models are defined using an XML style syntax similar to the X3D standard [WEBC02]. Nodes may also be instantiated from VRML and Inventor format files, although the internal hierarchies are not visible to the rest of the system. Since scene graph objects are just like any other object, it is possible for the entire object store to be written to a single XML file. Tight integration with the rest of the system means that everything is handled consistently.
An integrated part of the scene graph is a real-time constructive solid geometry engine, implementing the geometric operations described in Chapter 2 and Chapter 4 as an object named Csg3D. The Csg3D object is attached to two input selection buffers (which can be either single objects or entire hierarchies) and listens for update events. When an input object is moved or changes, the Csg3D object performs the CSG operation on the two inputs and generates an output mesh that can then be rendered in the scene graph (while the inputs are hidden). The CSG engine is capable of operating in real-time so that the user can manipulate the input objects and see the final output immediately.
The CSG engine operates on the facets that are defined by the input objects. While it might be preferable to use mathematical surface equations to represent each object, these are not easily definable for arbitrary mesh objects. Since the operation is required to work with polygons and no loss of detail, it cannot be resolved using either ray-tracing or voxel techniques. To perform a real-time CSG operation, an algorithm based on the work described by Laidlaw et al. [LAID86] is used. Each input object (or selection buffer) is broken down into a list of polygons and then the polygons in each object are subdivided by each polygon in the other object. The new subdivided meshes can contain potentially up to the square of the number of input polygons, with the complexity increasingly rapidly for highly detailed shapes. Each polygon is then tested against the other input object with the desired Boolean operation and accepted or rejected for the output. The final resulting mesh contains polygons from both inputs and is then processed to recollect back together facets that were subdivided but unmodified to reduce the polygon count. The polygons are then transferred back into a structure resembling the input hierarchies previously used and then stored, ready for rendering or further processing. While this process is computationally expensive, it still executes at interactive rates under manipulation for most objects (such as boxes), unless objects with hundreds of facets arranged at many angles are used (such as spheres). This dissertation contains a number of examples of the CSG engine used for carving, with the previous Figure 4‑24 and Figure 4‑25 from Chapter 4 depicting an object that has been carved multiple times, even splitting it into multiple parts. This CSG engine implementation is capable of performing operations on any surface that is fully enclosed.
The software architecture has been used to implement the Tinmith-Metro modelling application described previously in Chapter 5, but there are many other features that are not directly noticeable by the user that demonstrate powerful capabilities of the software architecture. This section describes the following features: an NFS server implementation, the use of the scene graph as a calculation engine, interfaces to the DIS protocol, the implementation of user interface components, and testing on miniaturised hardware.
This chapter has introduced the mapping of file system semantics to an object store for the retrieval and storage of objects in memory. As a demonstration of the capabilities of this object store, I have integrated a Network File System (NFS) server into the Tinmith-Metro application. The NFS protocol was first introduced by Sandberg et al. from Sun Microsystems [SAND85] to provide remote file system capability over a network. A client machine can mount drives located on a server and make directories and files appear to the user as though they are stored locally. NFS operates using remote procedure calls (RPC) over UDP packets and defines procedures to support primitive file system operations such as lookup, create, remove, getattr, setattr, read, write, rename, link, symlink, readlink, mkdir, rmdir, readdir, and statfs. As a user browses the file system, the operating systemís virtual file system (VFS) layer generates RPC requests to the NFS server. The NFS server processes the operation and then generates a result that is then sent back to the client machine and presented to the user.
The original NFS server implementation provided by Sun Microsystems is a user-level program that processes each RPC request and reads the local file system to generate responses. I have implemented a service inside the software architecture that implements the same RPC requests but maps these to the object storage system. When the client requests information about a file, the server traverses the object store and generates artificial information such as permissions, inode values, and sizes based on the type of the object found and its contents. Read requests on a virtual file will receive an XML representation of the object currently in memory, and write operations may be used to modify the values within an object. This NFS server implementation allows external applications to share data with Tinmith-evo5 applications without using the NetServer interface described previously. Applications implemented separately may read and edit objects using standard file system interfaces if desired, simplifying integration with Tinmith-evo5. Another use of the NFS server is to support powerful debugging using standard Unix shell scripting tools, which would be extremely difficult within a debugger. The generation of artificial file systems based on data structures is also used as a debugging and status feature in the Linux kernel, with the implementation of the proc [PROC03] and devfs [GOOC02] file systems. The NFS interface needs to be explored further in the future to better understand what extra capabilities are now possible.
When developing interactive 3D applications, each tracking device operates in a different coordinate system depending on the placement of the sensors and transmitters. Converting to a common coordinate system may be performed mathematically by applying 4x4 transformation matrices to 3D values. Performing these calculations manually can be difficult and time consuming, and so scene graphs were developed to provide an abstraction that can handle this automatically. Figure 6‑13 shows a sample VR view of the Tinmith-Metro application, with the world as well as an avatar to represent the user standing on the landscape. The parts of the userís body are defined as a collection of cylinders, but each is in coordinates relative to their adjoining parts. For example, the torso is the main cylinder, and the upper arms, upper legs, and neck are attached to this. The lower arms are attached to the upper arm, the hands are attached to the lower arms, and similarly for the legs and feet. The view frustum representing the userís field of view is attached to the head, which is relative to the torso. To make the avatar model mimic the human in the physical world, scene graph nodes are configured to listen to updates from tracking devices. For example, a GPS position tracker is attached to the torso and an IS-300 orientation tracker is attached to the head. As the GPS tracker is moved about the physical world, the entire body of the avatar will move about the virtual world. When the IS-300 is rotated, the head as well as the view frustum of the user is rotated to match. Performing this direct linkage between tracker and scene graph objects is very easy and intuitive since there is no need to deal with complex angle or matrix operations. Any transformations that need to be applied (such as a 90 degree offset or inversion) can be described in the scene graph itself, avoiding the need to implement extra external objects to process the tracker data directly.
Figure 6‑13††††† User is represented in the 3D world with a hierarchical avatar model
The thumb tracker described previously in Chapter 5 is also implemented using the scene graph. The coordinates returned by the ARToolKit libraries (described further in Chapter 7) are relative to the camera and require further processing to calculate world coordinates. By attaching the thumb tracker to a node relative to the avatarís view frustum, the scene graph can render 3D cursors at the appropriate location in world coordinates. As the user rotates their head around, these cursors will always be shown at the correct position relative to the avatarís body. An interesting feature of the scene graph is that any of the nodes may also be used as a source of information for other objects to listen to using data flow. The 2D cursors on the display in Chapter 5 are implemented by listening to the final 3D transformation of the cursors relative to the head and then projecting these to draw a 2D cursor, independent of the scene graph. In this example the scene graph is being used as a kind of calculation engine to perform most of the difficult calculations with the final value being extracted out and used separately. The scene graph allows the graphical specification and preview of transformations, and the rendering can be disabled once debugged and if the results are only exported to other objects.
The concept of a scene graph calculation engine is used extensively in an indoor position tracking system being developed [PIEK03e] [PIEK04b]. This tracker is designed to be cheap and simple to deploy, using cameras mounted on the backpack to track fiducial markers placed on the ceiling using ARToolKit, as shown in Figure 6‑14. Although most of the research performed for this dissertation is outdoor-based, being able to walk indoors and perform similar modelling operations is also desirable.
Figure 6‑14††††† Indoor tracking system with backpack, head and shoulder mounted video cameras, GPS antenna, and fiducial markers on the hands, walls and ceiling
This prototype tracking system incorporates two shoulder mounted cameras (facing forwards and backwards at 45 degree elevations) as well as the existing video overlay head camera. The video cameras capture images of the fiducial markers mounted to the ceiling. Each marker is stored as a translation from an origin point in the room, as well as an orientation from the ceiling and the various walls. With the ARToolKit returning 4x4 transformations of the markers in camera coordinates, these can be transformed into world coordinates by using the existing information known about the markers. Each room and the markers contained within are modelled in the scene graph, and by attaching the 4x4 transformation to the marker the world coordinates for the camera can be calculated. The results measured by the three cameras will each be different due to their different locations on the body, and so they must first be transformed into the same coordinates. Using a simple average without transformations is not feasible since the cameras are separated and not all tracking at the same time. Another problem is that while the shoulder cameras are rigidly mounted, the head camera is articulated on the userís neck. Since the orientation of each camera is computed using ARToolKit, a fixed transformation can be applied to the shoulder cameras to find a point on the torso of the user. For the articulated head, a transformation is applied along the direction of viewing to the centre of the head, and then a further transform is applied from the joint in the neck to reach a similar torso point as before.
Using the scene graph with graphical visualisation makes the understanding and specification of the transformations relatively simple compared to modelling it with matrices. The scene graph takes inputs from the cameras, transforms them into similar positions, and returns these back as data objects. These data objects are then input to an averaging filter object that produces a final tracker value in world coordinates. This tracker produces output using the same coordinate system as the standard GPS tracker, and so can be easily integrated with the existing tracking system. An object monitors the indoor roof tracker and GPS receiver, passing on a final computed position to the scene graph based on these two inputs.
The original DIS protocol based collaboration work performed previously [PIEK99c] has been rewritten for this new software architecture. Entity state updates arriving from the network contain a unique set of identifier values (such as site, host, and entity id) to separate it from other entities. Instead of having a separate internal list of objects, the scene graph is used to represent each entity at a path location such as /models/world/dis/S#/H#/E#, where S# is the site id, H# is the host id, and E# is the entity id. As the position and orientation values are extracted from the DIS entity state PDU packet [IEEE93], they are converted into a 6DOF matrix and then pushed directly into the scene graph. This method treats the DIS update the same as a tracking device, with the numeric id values used to directly identify the correct node in the scene graph. Each entity can be represented using a 3D model for realistic rendering and updated in real-time as packets arrive. Examples of the DIS protocol support are described further in the appendix of this dissertation.
The command entry system described in Chapter 5 is implemented as a nested series of menu objects contained within the object store. The hierarchical nature of the object store is also used to represent the arrangement of the menu nodes, and any node in the menu can be referenced using a file system path. These menus are defined separately from the implementation and are interpreted dynamically so they can be rearranged without modifying the source code. Menu nodes are defined to be either of an action or selection type and are connected together using the storage hierarchy. Action menu nodes contain a command that is executed internally in the system followed by a relative or absolute path name of the menu node to visit next. Selection nodes contain a list of child nodes to include in a menu display, and define a human readable label for presentation to the user.
Figure 6‑15††††† Partial layout of manipulation menu, with internal commands and next path
Figure 6‑15 depicts a more detailed definition of a section of the menu previously presented in Figure 5‑14, containing the commands assigned to some menu nodes and the node that will be visited next. Other nodes not showing commands are selection nodes that present the child nodes as choices to the user. Since command nodes cannot contain both a command and a list of children to select from, examples such as nudge execute the start command and then move to a child node called active which is a selection menu containing other children. When a selection menu node is chosen by the user, the menu processor moves its current traversal pointer to the node and refreshes the menu with the list of the new children available for selection. When a command menu node is selected, the command execution engine takes the text-based command string and maps it to an internal method call on the matching Tinmith object. After the execution of the action the traversal pointer is set to point to the next path specified by the menu node. The MIKE system by Olsen [OLSE86] implements a similar idea of decoupling the definition and implementation of menus.
One of the goals of the software architecture was to be able to develop applications for a wide range of computers. Currently the main demonstration applications operate on a Pentium-III 1.2 GHz laptop with an NVidia GeForce2 OpenGL video accelerator. In the future, I would like to use Tinmith-evo5 applications on smaller laptops and hand-held computers that have less processing power than I am currently using. To verify performance on slower and less capable hardware, the testing platform used is based on a Pentium-I 133 MHz embedded computer with 64 mb of memory and a Chips and Technologies 65555 2D video chipset. This older platform serves as a guide on whether these goals are achievable without requiring a major investment in porting the software to a new platform. With current hand-held computers approaching the capabilities of older processors, this is a very suitable test case that is easy to implement. As proof that this is possible, Wagner and Schmalstieg have demonstrated ARToolKit and OpenGL running on a small hand-held system [WAGN03].
Since the slower testing platform has the same processor architecture as the main platform, the software requires no changes to be compiled. Initially tested were applications that do not perform any rendering, such as the tracker driver for the ARQuake system described in the appendix. For these applications, there is no noticeable performance difference compared to a faster machine since the task of processing tracker inputs and generating packets is relatively simple. This demonstrates that the software architecture can operate within the limited processing and memory resources available for the implementation of simple tasks.
The full Tinmith-Metro application was also tested on the slower testing platform, but some changes were required in order to make the software run. The C&T 65555 chipset does not support any 3D acceleration and so the OpenGL libraries fall back to a software emulation mode that is very slow and inefficient. Generating a single frame takes many seconds and so this is not useful as a real-time AR system. Bellard has developed a small open source emulation library named TinyGL [BELL02], which provides much of the functionality of OpenGL but leaves out many of the more complex operations such as transparency, complex texture mapping, and strict adherence to the specifications. This library is capable of providing 3D rendering with textures on very old computers, although the objects in the environment cannot be as complex as is possible with hardware acceleration. TinyGL was integrated into the system and modifications were made to support the rendering of transparent textures for drawing fonts and to add some OpenGL function calls that were not implemented. The optical overlay mode of the software was also required to be used because the system is not powerful enough to handle the capture and display of video frames from the camera in real-time. With these modifications, the Tinmith-Metro software was able to run at a relatively slow rate of 2-4 frames per second at 640x480 resolution. When the application is profiled, the majority of the CPU time is spent inside the TinyGL code performing rendering. Based on these tests, OpenGL rendering is the main bottleneck affecting the performance of applications, and the software architecture itself has negligible overhead. Given the increasing number of miniaturised computers that include some 3D hardware acceleration, these performance problems will become negligible in the near future.
This chapter has described the Tinmith-evo5 software architecture, explaining the advantages of my integrated and uniform approach to building applications for virtual environments, especially mobile AR. The architecture uses a data flow methodology with an object-oriented design to allow applications to be implemented by connecting processing objects together. An object store is developed that is based on Unix file system semantics to provide a simple model for the storage and retrieval of objects in large and complex applications. Using this software architecture, a number of powerful features such as distributed programming, persistent storage, and run time configuration are possible. Components such as a scene graph, a constructive solid geometry engine, and a sensor processing interface are fully integrated to support the requirements of 3D virtual environments. The design is based on the C++ language and although the language has a number of limitations, these are overcome using a variety of techniques. The use of C++ allows the development of efficient applications that operate on a wide range of mobile computers. The applications described in this chapter and in this dissertation are implemented using this software architecture and demonstrate its usefulness for real world applications.
"Everything that can be invented has been invented."
Charles H. Duell, Commissioner, U.S. Office of Patents, 1899
This chapter presents the hardware components I have developed to operate interactive AR applications such as Tinmith-Metro in an outdoor environment. AR requires that equipment such as a HMD, computer, and tracking hardware be worn outdoors, with some early examples being the Touring Machine by Feiner et al. [FEIN97] and my initial research prototypes [THOM98] [PIEK99c]. While my research was initially based on similar applications as Feiner et al., over time I have moved into the new areas of research that are described in this dissertation. This research requires new hardware components capable of supporting the desired interactions. While commercially available components are used in many cases, these must be modified to suit the requirements of the mobile AR task. In some cases, components cannot be purchased off the shelf and so must be designed and constructed. This chapter describes the integration of existing components and the development of a custom backpack, helmet, and gloves to support the research in this dissertation.
This section describes the equipment that I currently use to perform AR outdoors. Previous generations of backpacks and associated components are discussed in the appendix of this dissertation. Table 7‑1 summarises the equipment options currently in use, including cost, placement on the body, and power consumption.
Current generation laptops are almost on par with the processing capabilities available in desktop machines. While there is still a slight delay with new technology in laptops, there is now a demand from consumers for laptops that can run the same applications as desktops. The current laptop used is a Dell Inspiron 8100 with Pentium-III 1.2 GHz processor, 512 mb of memory, and a 20 Gb hard drive. Most importantly of all, it contains an Nvidia GeForce2Go graphics chipset that is capable of rendering complex 3D texture mapped graphics with hardware accelerated OpenGL support. Using two Lithium-Ion batteries it can store approximately 110 Wh of power and operate for more than 2 hours. The laptop runs a custom compiled Linux kernel 2.4 with the RedHat Linux 7.3 distribution, which includes various libraries and tools from GNU, BSD, and the X Consortium.
Location: H=head, B=backpack, W=worn
Table 7‑1†††††††† Current backpack components with cost, location, and power consumption
The head mounted display is a Sony Glasstron PLM-700E with a maximum resolution of 800x600. As discussed previously, this display can be used for either video or optical-based augmented reality, and has one of the highest quality displays available. Since this display is no longer available, a set of IO-Glasses with PAL video resolution is used for performing demonstrations to the public.
The body position tracker used is a Trimble Ag132 GPS. As discussed previously, this device uses differential signals, advanced signal processing, and higher resolution calculations to produce 50 cm accuracy. The device outputs NMEA 0183 updates via an RS-232 serial port at 10 Hz, making it very suitable for position sensing in AR. The secondary GPS unit also supported is a Garmin 12XL GPS, which is much lower quality with 5-10 metre accuracy at 1 Hz with NMEA 0183 updates.
The head orientation tracker is an InterSense IS-300 hybrid magnetic and inertial tracker, with the sensor cube mounted on the helmet. This device produces updates via an RS-232 serial port at up to 300 Hz, but configured at the rate of 30 Hz so that the laptop processor is not saturated by the tracker data.
The video input is captured by a Point Grey Research Firefly camera, which uses a 1394 Firewire interface [IEEE95] to directly connect to the laptop. This camera is a Digital Camera (DC) compliant 1394 device that can output 640x480 resolution video at 15 frames per second in an uncompressed 24-bit RGB format. Using uncompressed RGB is more efficient than YUV formats because it can be passed directly to OpenGL and image processing algorithms with no conversions in the processor. The Firefly camera is separated into two parts via a ribbon cable - the small CCD sensor and lens are mounted onto the HMD while the processing is performed on a separate board in the helmet. An alternate camera used with the IO-Glasses for demonstrations is a Pyro 1394 web cam, supporting the same video formats as the Firefly but with a lower resolution camera sensor internally.
Other supporting components such as USB hubs, 1394 Firewire hubs, and USB to RS-232 converters are also required to connect hardware that may not share a similar interface or provide enough connector plugs. Figure 7‑1 depicts the flow of data from each component as it is propagated toward the laptop for processing.
Figure 7‑1††††††† Data bus interconnect diagram of components used for mobile outdoor AR
I have been developing backpack computers for the last five years to support AR software. The original design shown in Figure 7‑2 was used for a number of years to support my research, and was based on a hiking frame that was untidy and difficult to maintain. As part of a joint research venture between UniSA and DSTO, I worked with a team of engineers from the SES and ITD divisions at DSTO to gather requirements and analyse my previous backpack designs. The goal was to produce a new backpack using industrial manufacturing techniques to support mobile AR. The final product is of professional build quality and is shown in Figure 7‑3. This section presents how Tinmith-Endeavour evolved from the previous designs, with the lessons learned along the way and how these were used to help improve the design. The appendix contains a complete discussion of the various backpack designs I have created since the start of this dissertation.
Figure 7‑2††††††† Rear view of previous backpack design, showing tangled mess of cabling
Figure 7‑3††††††† Front and rear views of the Tinmith-Endeavour backpack in use outdoors
The technology of the components for AR research has been rapidly changing over the past few years and it is not possible to keep a static design due to constant hardware upgrades. For example, in the last two years video capture in laptops has changed from analogue PCMCIA to USB, and now to 1394 Firewire. Most input devices are now USB, although some legacy devices must still be supported with PS/2 and RS-232 ports. Merely placing all the items into a rucksack style container is not possible due to the number of cables and various sizes and weights of the devices. From my analysis, a rigid frame that the devices can attach to is the best solution, as it allows enough flexibility to accommodate future changes, even for unknown devices.
The original backpack shown in Figure 7‑2 is based on a hiking frame, containing aluminium pipes running along the sides and across. Wooden back planes and straps are used to attach components but the number of pipes to attach to is limited. Wires are routed using insulation tape to hold them together and to the backpack, and requires cutting to remove and leaves a sticky residue. The laptop is supported by a perpendicular wooden plane at the bottom and then strapped to the backpack both horizontally and vertically, and prevents access to the laptop without removing the straps. While this design works, it is time consuming when problems occur or equipment needs changing. A number of problems were identified from the previous backpack designs during the requirements gathering process. Some of the problems identified were:
∑ Hardware changes - Hardware is continuously improving and reconfiguring components was difficult because many could only fit in certain places on the hiking frame due to its shape.
Breakage - Fragile connectors and cables were breaking easily because they were not protected from external forces experienced when moving in the environment.
Travel - The backpack was difficult to transport to overseas demonstrations due to its size and fragility.
Fasteners - Insulation tape was used to fasten cables and components, which leaves a sticky residue.
Cable bundling - Bundles of cables taped together are messy and waste space otherwise available for other components.
Laptop fastening - Straps holding the laptop in place prevented it from being opened, making setup and debugging difficult.
Autonomous operation - The cabling was so complicated it required an assistant to help prepare the user for outdoor use.
Power supply - Many devices contained their own power sources with different lengths of operation, a centralised battery would simplify the operation of the backpack.
The new backpack design is worn by using an industrial breathing apparatus frame. The frame is made of aluminium with a foam padded interior and moulds to the contours of the body. The rest of the system is contained within a clear polycarbonate moulded box with a pattern of holes drilled over the surface, providing mounting points and air flow. The polycarbonate box and frame are joined together with a hinge, allowing the entire backpack to open into an A shape and be self supporting outdoors if required. The polycarbonate box depicted in Figure 7‑4 is used to mount all the components in, and a second box is mounted inside on a hinge to hold the laptop - when the backpack is in the A shape, it is possible to fold the laptop out like a table, as shown in Figure 7‑5. This design allows the backpack to be placed on the ground outdoors to operate the laptop in a traditional manner.
Figure 7‑4††††††† Design of polycarbonate housing with hinged laptop holder and internals
(Images courtesy of ITD and SES Ė Defence Science Technology Organisation)
Figure 7‑5††††††† Backpack shown in desktop configuration, permitting normal use outside
Attaching devices to the backpack is difficult since many devices are manufactured with no mounting points and little surface area to attach to or drill into. As a result, a variety of strategies are needed to fix devices securely to the backpack. With the previous designs, straps, double-sided tape, and permanent screw mountings were used. The new interior design of the backpack is shown in Figure 7‑6, with components fixed securely using Velcro and cables attached with cable ties.
Figure 7‑6††††††† Interior of backpack housing, showing internal components and cabling
A special 3M developed Velcro is used as the main means of attaching large components. This Velcro uses mushroom-shaped plastic heads and four 1cm x 1 cm pieces can securely suspend the laptop vertically. For safety, two straps run across the laptop keyboard are used to protect it from falling if the Velcro were to fail. Components inside the backpack also use Velcro but do not require safety straps because they are lightweight and attached cables would provide support if the device detaches during use. Using the Velcro enables easy rearranging of components as hardware requirements change and small surface areas can support quite heavy objects.
Cables are run along the surface of the polycarbonate in busses, with cable ties through the plastic grid holes to secure them. This still does not truly solve the problem of easy configuration since they must be destructively cut open when new cables are added or removed. Since they are cheap, they are used in preference to insulator tape which does not run well through holes and leaves a sticky residue. It would be desirable to use some kind of reusable cable ties that can hold large bundles of wires easily.
Many of the components used require their own battery, and so using a single dense power source means that all devices run for the same amount of time. In the past, heavy lead acid 12V batteries were used but these have been replaced with 12V Nickel Metal Hydride (NiMH) batteries rated at 100 Wh. This battery powers all of the components except the laptop for approximately 1.5 hours (varying depending on the equipment in use) and may be swapped by an assistant while mobile. Linear voltage regulators are used to run low current devices at 5 and 9 volts, as well as the Sony Glasstron which uses a non-standard 8.4V. The Glasstron uses 1.2A of current at 60% efficiency due to the linear regulators, but the additional cost and weight of a DC-DC converter was not deemed necessary and instead spent on extra NiMH batteries.
To distribute power, a specially designed breakout box shown in Figure 7‑7 is used that has 8 four pin connectors, each with GND, +5V, +9V, and +12V available. Devices may be easily plugged in since one standard type of connector is used throughout the system (except for the Sony Glasstron). Power breakout boxes may be chained if more sockets are needed. A master power switch (with 5 Amp fuse and indicator light) is mounted onto the front shoulder strap to allow the user to easily turn the power on and off without having to remove the backpack.
Figure 7‑7††††††† Power supply breakout box, with +5V, +9V, and +12V at each connector
Figure 7‑8 depicts how the various devices in the system acquire their power. Some devices are connected directly to the breakout box while others receive it over their data bus connections.
Figure 7‑8††††††† Power bus interconnect diagram of components used for mobile outdoor AR
Figure 7‑9††††††† Two USB ports, glove connector, and cables mounted onto shoulder straps
To make the system easy to use while outdoors, a number of connectors are available on the shoulder straps to attach devices to, as shown in Figure 7‑9. Four USB sockets are available on the shoulder straps, allowing the user to plug in devices such as mice and keyboards for debugging while mobile. Special Lemo connectors are used to attach the 1394 video cable and IS-300 sensing cube, permitting the separation of the HMD from the backpack. 8-pin sockets that attach to leads ending in Mini-DIN connectors are used for the glove interface and are easy to plug in, even while wearing the gloves. Since the 50-pin Sony Glasstron cable cannot be cut for a connector, the entire unit is attached with Velcro so it can be separated.
Internally, cables have been shortened where possible to approximate lengths or tied into tight looms and cable tied to the plastic grid on the backpack. The backplane allows many separate bundles and so different subsystems are separated to allow changes without affecting the rest of the cables. One risk of using tight cable ties is that they could potentially damage fragile cables (such as the Glasstron 50-pin cable) if used incorrectly.
The Sony Glasstron HMD is a very fragile device and has a limited surface area for attaching extra components such as trackers and cameras. Rather than attempting to weigh down the small hinge on the front of the HMD, the head band of the Glasstron is attached to a welding helmet that has a semi-rigid plastic frame permitting attachments. The IS-300 sensor cube is mounted on the top of the helmet shield with plastic mushroom Velcro, while the Firefly camera processing board is hidden underneath the shield. The small CCD sensor for the Firefly camera is mounted onto the HMD using a custom manufactured bracket with screws to adjust the angle, and a flexible ribbon cable connecting it to the processing board. The helmet design is depicted in Figure 7‑10, and photos were shown previously in Figure 1‑1 and Figure 7‑3. As mentioned previously, for demonstration purposes an IO-Glasses HMD with Pyro 1394 camera is used and includes sufficient mounting space so that an extra welding helmet is not required.
Figure 7‑10††††† Design of brackets to attach Sony Glasstron and Firefly camera to a helmet
(Image courtesy of ITD and SES Ė Defence Science Technology Organisation)
Since this research is being conducted in Australia, presentations at conferences usually require travel on airplanes. Airlines are known to handle baggage very roughly and transporting delicate computer equipment can be difficult. A special aluminium suitcase was designed to transport the backpack, containing foam blocks that have been carved out to fit the profile of the backpack. This foam is soft and protects the equipment from shocks while being transported, reducing possible damage to the backpack and improving reliability for demonstrations.
While the foam suitcase adequately protects the backpack, the vibrations and other shocks experienced during transport still manage to separate connectors and Velcro, requiring small touch up repairs on arrival. Devices such as the laptop, GPS, IS-300, and HMD are too fragile to be transported in this way and so are packed into a separate carry bag as hand luggage. This bag has much thinner padding and is designed to fit into the overhead compartment of an aircraft, with the assumption that the person carrying it will properly handle the equipment.
The new Tinmith Endeavour backpack is an marked improvement over my previous backpack designs. Although this design is better, there are still a number of areas where it could be improved that I have discovered during development:
∑ Improve backpack frame design - The current breathing apparatus frame comes in one generic size and is not designed to adjust for the different sizes of people. This affects the weight distribution and for tall people means that the weight rests entirely on the shoulders and none on the waist. This fatigues the user very quickly and makes the backpack uncomfortable. By replacing the breathing apparatus frame with an adjustable version this will improve the ergonomics of the device and enable much longer operation runs.
Add more ports to the shoulder straps - The USB ports on the shoulder straps allow easy plugging in of equipment by the user while they are wearing the backpack. At the time, 1394 connectors could not be purchased and so the camera cable was split with Lemo connectors and run directly to the hub. With the new availability of OEM 1394 sockets, these can now be embedded into the shoulder straps easily.
With recent changes in some hardware devices, it is also possible to streamline the backpack design further, reducing its weight and power consumption:
∑ The InterSense InertiaCube2 can now be processed in the laptop, with the InertiaCube plugging directly into the serial port. This removes the need for the InterSense IS-300 processor that uses a large volume of the backpack and large amounts of current.
The use of new HMDs such as IO-Glasses means that no 8.4V conversion for the Glasstron is needed, removing the need for inefficient power conversion and heat dissipation.
The embedding of RS-232 to USB chipsets into devices such as the glove controller so that devices can draw power from the USB port. This removes the need for large RS-232 cables and some of the power cables. If all legacy serial devices can be removed it is then possible to remove the USB to RS-232 converter, saving more space and power.
As the components in the backpack are miniaturised over time, a redesign of the polycarbonate casing will eventually be desired since unused space will accumulate.
As discussed in Chapter 5, the userís hands are the primary input device for the system. Finger presses as well as the position of the hands provide the necessary inputs for the user interface software. This section discusses how the gloves were constructed, the electronic detection of finger pinches, and the implementation of position tracking.
Gloves have been used as input devices in a large number of VR systems; they allow the use of the hands to interact with the environment using natural pinch and grab style gestures. There are a number of commercially available gloves on the market and these perform sensing in two different ways: the FakeSpace PinchGlove [FAKE01] contains electrical contact sensors at each fingertip to measure finger pinches, while the VTi CyberGlove [VIRT01] uses bend sensors to measure finger positions. While full motion of the fingers may seem more accurate, errors in the results make it difficult for software to determine exactly when the userís fingers are pressed together. Since my user interface requires only simple finger press inputs, a design similar to the PinchGlove is the most desirable as it detects accurate contact information. Although these gloves were initially investigated for use in my user interface, they were disregarded because the controller unit was too large and consumed too much electrical power. As discussed in Chapter 5, I also wanted to experiment with the placement of the metallic pads in various locations. For the purposes of my research, I have created a number of sets of custom pinch gloves that allow flexibility in working with different pad layouts. These gloves have conductive pads that can be placed at any desired location, and use a small low powered controller device suitable for use with a mobile computer.
The glove design is based on typical fabric gloves available in many supermarkets and hardware stores, with wires and connectors glued to the surface. The gloves are quite simple in operation and rely on the completion of an electric circuit - conductive surfaces on the fingers, thumbs, and palms detect when various pinch motions occur. Initially, a partially conductive aluminium tape was used as the surface, but this did not conduct well and was difficult to attach to the wires. Further improvements used metallic wire wrapped and glued around the fingertips, giving improved conduction and pinch detection but being more fragile. The latest design is based around 3M copper adhesive tape, which gives the best conduction and is also extremely robust. The three glove designs with aluminium tape, wire, and copper tape are shown in Figure 7‑11.
Figure 7‑11††††† Designs of various versions of the glove and attached fiducial markers
Shown are aluminium tape, wire wound, and copper tape designs
Wires from the metallic surfaces are attached to the glove controller box mounted inside the backpack via connectors on the shoulder straps. The controller box contains a Basic Stamp BS2 microcontroller [PARA01] that uses very little power and is fully functional in a small 24 pin IC sized package. The circuit depicted in Figure 7‑12 connects the BS2 to the gloves with a minimal number of components. The internal software applies voltage to each finger and polls for which pad the voltage is detected on (thumb, palm, or none). If this value is different from the last loop, a single byte packet is sent to the serial port on the laptop for processing, containing the current status of the changed finger. Since the microcontroller is dedicated to the task, there is no overhead for the laptop and the polling can operate in a hard real-time loop at 30 Hz. As the design of the gloves changes, the controller can be upgraded by downloading new firmware through the serial port.
Figure 7‑12††††† Circuit schematic for the low power glove controller
The gloves are used to capture the 3D position of the hands so the user can intuitively control the outdoor AR system. As described in the background chapter, tracking in an outdoor environment suffers from a number of known problems such as power consumption, size, weight, and support infrastructure. The outdoor environment also contains harsh conditions that cannot be controlled by the user. Some examples of these problems are:
∑ Accelerometers cannot be used since they drift over time and the registration of the cursor would be inadequate for accurate modelling tasks.
Infrared-based systems are unreliable when used outside due to the large amounts of infrared light given off by the sun.
Active magnetic tracking relies on using large non-portable control units, consumes large amounts of electric power, and would be affected by magnetic fields from the equipment carried on the backpack.
One solution demonstrated outdoors was the WearTrack system [FOXL00] by Foxlin and Harrington that utilised ultrasonic technology. This system tracked the position of the userís hand relative to their head (with no orientation), and was used for a number of interaction tasks. To implement this system, an ultrasonic tracker with its associated weight, size, and electric power penalties must be added to the wearable computer system.
The solution created for this dissertation uses optically-based vision tracking. This approach allows me to leverage the existing helmet mounted camera that is currently used for the live AR video overlay. The ARToolKit libraries [KATO99] are employed to perform full six degree of freedom tracking of the fiducial markers placed on the thumbs of the gloves. Currently only the position values are used, as the accuracy of the rotation values is not adequate. Implementing vision tracking with the gloves simply requires the attachment of small and lightweight paper-based fiducial markers, with no extra devices or batteries required. The paper markers used in this implementation are 2cm x 2cm squares and are glued to the thumbs of the glove. Another advantage of my vision-based tracking implementation is that achieving accurate registration is relatively simple using the video from the AR overlay.
Figure 7‑13††††† Example use of the fiducial marker tracking used for a 3D cursor
It was initially proposed to put the fiducial markers on the back of the userís hands, but it was quickly realised that the hands are large and fill much of the field of view of the camera. A user would not be able keep the markers in the field of view and manipulate an object to the edge of the display. The problem of accurately calculating the userís finger tip position based on fiducial markers on the wrists was also a concern. It was decided that the index fingertips would not obscure the userís view, allow much finer motor control, and provide extended movement. The ends of the index fingers were dismissed during simple trials however, as they moved during menu option selections and would affect operations in progress. I did however notice that the thumb only moved slightly during the pinch motion, and the users studied tended to bring their fingers to meet their thumb as opposed to moving the thumb to meet the fingers. The targets were then placed at the tips of the thumb in an orientation that is fully visible to the camera when the gloves are used for interaction and menu operations, as shown in Figure 7‑13.
The ARToolKit libraries [KATO99] were developed to support the tracking of simple fiducial markers, allowing applications to appear to place 3D objects onto these markers when viewed on a display device Ė an example is the ARToolKit simpleTest demonstration. While the ARToolKit generates 6DOF matrices for each fiducial marker, these matrices are expressed using a special distorted camera frustum model and not usually in true orthogonal world coordinates. If the calibration model for the camera is an accurate model, it is possible to treat these camera coordinates directly as world coordinates. If the calibration model for the camera is not perfect (the ARToolKit calibration process does not always generate good results) then extra errors are introduced during the conversion and it is unusable for tracking. I have developed a method of correcting the calibration values so that the ARToolKit may be used for tracking and inserting results into a scene graph [PIEK02f].
In applications that use ARToolKit as intended (such as simpleTest), the video is first captured by libARvideo. Next, recognition of fiducial markers and calculation of camera-space transformations is performed in libAR, which is then used to render the final scene using the camera calibration frustum in libARgsub. When a pattern is recognised, a matrix transformation is returned from arGetTransMat(), which defines the markerís position and orientation (6DOF) relative to the camera, in the cameraís calibrated coordinate system. The camera calibration data is used by this function to modify the results to compensate for the cameraís unique properties. Due to distortions in the camera and errors in the calibration process, the coordinate system is not the typical orthogonal coordinate system normally used in 3D graphics. If this camera-space matrix is used to draw an object with libARgsub, the view frustum used in OpenGL will be the same as that of the camera model, and so the image will appear to be rendered at the correct location. An interesting side effect of these transformations is that no matter how poor the camera calibration is, the 3D object overlaid on the fiducial marker will always be correct since the incorrect calibration model used in arGetTransMat() is reversed when drawing using the camera as the view frustum in libARgsub. When the matrix calculated in arGetTransMat() is put into a scene graph however, errors occur in the output for two reasons. The first is that the camera calibration is not being used to render the display and compensate for any camera distortions. Secondly, if the calibration is not good enough and did not model the camera accurately, further errors are introduced.
After calibration of a camera, ARToolKit generates information for the camera similar to that depicted on the left in Figure 7‑14. The values that define the coordinate system of the camera (and also found to introduce the most errors) were the X and Y values for the centre pixel of the camera and the other values in the matrix (highlighted with boxes). In the default supplied calibration file, the Y axis is 48 pixels from the centre of the camera but under testing I found most cameras had centres that were reasonably in the middle. This was an error introduced during the calibration process that requires correction before use, and forms the basis of my calibration technique.
Figure 7‑14††††† ARToolKit default camera_para.dat file, with error x=2.5, y=48.0
The model is then corrected so the axes are orthogonal and usable as a tracker
The values generated by the ARToolKit calibration procedure (as depicted in Figure 7‑14) are stored in a binary file that can be easily modified. The technique is to adjust the highlighted values but leave the focal point and scale values untouched because they are reasonably correct and errors are less noticeable. The highlighted values are replaced with the coordinates exactly at the centre of the display, with 320x240 being used for a 640x480 resolution camera. These changes are based on the assumption that most cameras are straight and that any offsets in this value are caused by errors in the calibration process and not in the camera itself. Figure 7‑15 depicts the effects of these changes graphically, where the original distorted camera is now orthogonal and suitable for use as a tracking device in a scene graph. This technique is only useful for correcting errors caused by the ARToolKit calibration with cameras that have the centre of the image at approximately the axis of the camera. In distorted cameras, this technique could produce results that are worse than the uncorrected version, and so should be used with care and the results carefully monitored to ensure that it is being used in an appropriate fashion.
Figure 7‑15††††† Graphical depictions showing original and new orthogonal camera model
Although vision tracking may be CPU intensive, the ARToolKit runs quite efficiently on the laptop and works alongside the rest of the software with no noticeable problems. The 1394 camera mounted on the userís head captures the video for the video overlay and also passes it to the ARToolKit subsystem for further processing. Dynamic lighting conditions may cause problems for the tracking since the camera automatically adjusts its settings for the overall image. Sometimes the resulting image may be too bright or lacking enough contrast to produce the thresholded binary image for the ARToolKit library to extract the fiducial markers, and so the tracking fails. I intend on experimenting further with different cameras and filters to help improve the ability to handle the bright and varying lighting conditions outdoors. In dark twilight conditions, the tracker works quite reliably due to the low light gathering capabilities of the camera, and a small light attached to the HMD may be used in cases of complete darkness.
Although there are some problems, the tracker is usually able to acquire the fiducial markers and provide tracking information to the rest of the system. When used for 3D positioning the results from the vision tracker is quite good and the registration is within the area of the target. However, detecting 3D rotation is more error prone since large changes in orientation only give small changes in perspective on the image, resulting in very noisy output in the order of 20-30 degrees. As a result, I currently only use ARToolKit for position tracking, providing a cursor for use on the HMD. Chapter 5 described how by combining two cursors it is possible to derive a high quality rotation using their relative positions.
Using ARToolKit as a general purpose tracker with some adjustments has allowed me to integrate hand tracking into my outdoor modelling applications that may otherwise have not been possible given the conditions in the outdoor environment. Since the user interface described in Chapter 5 has evolved to use only 2D position information, much simpler tracking may be performed using algorithms such as blob detection. Coloured balls with non-specular surfaces mounted onto the thumbs are much easier to detect even under poor lighting, and would improve interactive performance and tracking quality for the user.
This chapter has presented the hardware components used to perform mobile outdoor AR and to support the applications developed for this dissertation. The components used are mostly based on commercially available devices since specific equipment designed for mobile AR is not available. A new backpack named Tinmith-Endeavour was developed to provide a flexible platform for the carrying of the required components outside. This new design takes advantage of lessons learned over many years of backpack design and is expandable to handle changes in the future. To provide input for the user interface described in this dissertation, a new input device based on pinch detecting gloves and fiducial markers was developed. Both the backpack and gloves have been tested extensively and iteratively refined as part of this dissertation research.
"The reward for work well done is the opportunity to do more work"
Jonas Salk, MD
This dissertation makes a number of significant and original contributions in the area of interactive 3D modelling for outdoor augmented reality environments. The contributions described in this dissertation can be summarised as the development of augmented reality working planes, construction at a distance techniques, user interface technology, and the software and hardware needed for implementation. These contributions have been demonstrated with real world examples and provide the capability to perform modelling of 3D geometry in outdoor environments. With these contributions there are a wide range of future possibilities for new research and applications that may be used in scientific and commercial settings.
Seven unique contributions to the area of computer science are described in the following sections. These contributions form a solution to the problems associated with interactive 3D modelling in outdoor augmented reality worlds. The introduction to this dissertation contained a set of six research questions and three research goals. The seven contributions answer each of these questions, providing a practical and demonstrated solution for each one. Each of the goals outlined were used as principles during the development of these contributions and guided their development.
The ability of a human to perceive depth information attenuates rapidly as the distance increases. Most existing techniques for virtual environments are designed to operate within armís reach and so different techniques are required for interacting with objects at large distances. Outdoor objects such as buildings are very large compared to the size of a human and are beyond their depth perception, even if a part of the building is within armís reach. By thinking of distance as sideways motion from a different view point, it becomes possible to avoid problems with inaccurate depth perception. The augmented reality working planes technique is developed based on existing concepts used in CAD modelling systems. By creating an AR working plane at a distance from the user, vertices may be projected against this surface from any distance with no reliance on the depth perception of the user. AR working planes may also be defined and used to manipulate existing objects, providing a complete set of modelling interactions. AR working planes have an accuracy which attenuates at a constant rate that is better than human depth perception, and is limited only by the resolution of the pixels on the HMD and the tracking devices in use. The concept of AR working planes encompasses a number of ways to create the planes, and may be made relative to the various coordinate systems the user is familiar with. By taking advantage of the alignment of landmarks that can accurately position a user, AR working planes may be created that are aligned with physical world objects. The flexibility of AR working planes may be used to perform a wide variety of modelling operations and forms a basis for the next contribution. A further advantage to the use of AR working planes is that only 2D input devices are required, which makes the implementation much easier using currently available mobile hardware.
A naÔve way to perform outdoor modelling is to create each vertex by hand and then connect them together to form polygons and objects. This method is time consuming and does not take advantage of the many properties of outdoor objects that can be used to minimise the amount of data entry required. Humans are quite capable of understanding the design of objects around them and this knowledge is used to break down the modelling of a building into a set of simple and much higher-level steps. For example, instead of specifying vertices directly, the user may project walls aligned with the object and the system can calculate the vertices and polygons automatically based on the intersection points. This dissertation presented a collection of techniques named construction at a distance that provides the ability to perform various modelling operations. By iteratively combining these techniques, complex shapes may be formed. Using an AR preview the user can construct the model in steps and compare it against the physical world, iteratively refining the parts that need the most work according to the judgement of the user. The construction at a distance techniques have been demonstrated with examples performing the modelling of many complex outdoor shapes.
There are only a small number of user interfaces for mobile AR and many of these are quite simple and use existing 2D desktop metaphors. With AR focusing on the use of the body as an input device to view information, little research has been performed into performing higher-level interactions such as those required for 3D modelling. A further complication is that tracking technology for mobile computers suffers from problems not normally experienced indoors, and so this must be taken into consideration with any interface design. Command entry is required to select amongst the number of different AR working planes and construction at a distance techniques available. Furthermore, these techniques also require pointer-based interactions to create vertices and perform manipulation operations. This dissertation describes a user interface designed specifically for mobile AR applications, based on the userís hands. By using the mapping of finger pinches to commands, the user may execute commands without using the cursor, leaving the hands available for pointing-based interactions. Keeping command and pointing actions separate allows both actions to be performed simultaneously if desired. To complete the AR user interface, a wide range of information is presented to the user so they understand the state of the application and the environment around them. By using external VR style views, the user is able to see information not normally visible from the immersive AR view. The design of this user interface was iteratively tested in small informal user studies to find design flaws and then make improvements.
There are a small number of input devices that are suitable for use with mobile computers because of limitations imposed by the outdoor environment. Most 3D tracking technology is not suitable for mobile computing but is also not required since AR working planes only requires a 2D input for a cursor. Existing desktop input devices such as trackballs and joysticks may be difficult to operate since there are numerous levels of indirection applied between the user and the task. Since operations are performed against AR working planes, the direct motion of the hands is used as an input device. Instead of manipulating a cursor indirectly, the user can simply reach out and point directly at the plane to perform operations. This dissertation presents a vision-based tracking system to support the user interface with intuitive hand-based control. Simple metallic sensors are used to detect pinching motions, and are mapped directly to the menu system. Fiducial markers mounted on the thumb are tracked using the HMD camera providing video overlay, and operates in a wide variety of lighting conditions. This hand tracker is a simple and yet very effective input device for mobile outdoor AR, requiring no major extra equipment not already included in the system.
This dissertation presents applications that have been developed using the other previously discussed contributions. The Tinmith-Metro modelling application demonstrates the use of AR working planes and construction at a distance techniques, controlled by the user interface and vision tracked gloves. This application implements all of the examples presented in this dissertation, with the goal being to produce a tool that may be used to create and capture the geometry of outdoor structures. The examples are used to demonstrate the capabilities of the techniques described in this dissertation.
Software toolkits for 3D virtual environments are not sufficiently developed to the point where applications may be developed by simply connecting together various components. In 2D desktop environments, simple toolkits were built and then used to perform research into increasingly higher-level tools. Current 2D toolkits are extensive and do not require the programmer to perform very simple operations since a best practice has been developed for many types of tasks. In contrast, 3D toolkits are much less mature and only simple tasks such as tracker abstraction have reached a best practice. The developer of 3D applications is currently required to implement many of the features that an application needs. Existing software was not adequate for the implementation of the contributions of this dissertation, and so a number of new solutions were developed to current problems. This dissertation contributes to the existing body of work by developing a software architecture with a number of novel features such as an object-oriented design based on data flow, a file system approach for an object store, and a streamlined and high performance implementation. This software architecture forms a basis for the applications developed for this thesis, and made the implementation much simpler than would have been possible by attempting to use a collection of existing tools.
Most of the equipment used for the development of AR systems is based on commercially available components, but these must be modified since the components are not normally designed for mobile and outdoor requirements. In other cases, components that can not be purchased must be designed and constructed. Since the desired task is mobile outdoor AR, all the equipment required must be worn by the user. Integrating a number of components into a system that can be worn outdoors is non-trivial, and must take into account restrictions such as weight, size, power consumption, mounting, and capabilities. This dissertation contributes a design for a mobile backpack computer that uses a flexible internal architecture to carry a number of devices while also being easy to change. Since hardware is constantly evolving, the ability to accommodate these changes is important when performing research with mobile AR systems.
With the availability of 3D modelling techniques for use in mobile AR environments, a wide range of possibilities for future work are available. I would initially like to explore possible applications where the 3D modelling techniques can be used to enhance existing work. As mentioned in Chapter 2, there are a number of techniques currently used to capture the physical world but they have some limitations, and my contributions address some of these. It would be interesting to explore how the modelling techniques may be integrated with existing capture techniques to provide real-time feedback using AR and allow interactive changes. Chapter 4 discussed how the modelling techniques may be used to create geometry for objects that do not exist yet. This capability would be useful for landscape designers and architects to plan the construction of new developments while on site. When I have discussed my research with various people, I have received much interest from people in areas such as architecture, construction, forestry, gaming, surveying, and visual arts who are interested in trying the technology for their particular applications.
There are a number of areas I would like to perform further research in to improve the contributions made in this dissertation. Hardware is continuously improving and this increases the features that can be implemented. It would be interesting to combine the construction at a distance techniques with existing image-based reconstruction algorithms, so that the system can automate simple tasks while still giving the user control over the process. As discussed previously, the use of speech recognition technology would be a very useful addition to the user interface in Chapter 5. Using the distributed software architecture described in Chapter 6, applications can be developed that support users on multiple systems (both indoors and outdoors) to collaborate on various modelling tasks. It would be interesting to explore collaboration tasks further to find out the types of interactions that are useful when working in these environments. With the availability of a reference platform, the development of user studies to test future improvements is now possible, and verifying which techniques perform the best in this environment would be useful Ė the current design is based on knowledge from the VR area that may not fully apply. For the technology to progress towards mainstream use, it will require all the components to be better integrated and miniaturised. I would like to explore the use of smaller computers such as hand helds and tablet PCs as replacements for the laptop, with the goal being to reduce the current backpack to something more manageable and that may one day fit into the userís pocket.
When this dissertation was written, the goal was to create the necessary user interfaces and techniques required to perform 3D modelling. While this goal has been achieved, the modelling application now needs to be used to solve real world problems in order to be useful. To allow the ideas in this thesis to mature into applications that can be used in the real world, I have formulated a scenario which is both useful and can be used to thoroughly test out future developments. The scenario is of a disaster recovery system, to be used by rescue workers when areas of cities have been destroyed by an event such as an earthquake. A current problem with these cases is that workers may not know what the landscape looked like previously, and therefore they may not know where to dig and search. Using previously captured models overlaid onto the landscape, the previous environment is made obvious to the wearer. The AR equipped rescuers can then direct other traditional rescuers to places of interest, even in conditions of dust or darkness. Another important problem with rescue efforts is coordination amongst workers. Trying to communicate spatial information about the environment to another person on a radio is quite difficult, especially when there is confusion and panic. Using AR, the user is able to mark up the environment around them and perform tasks such as highlighting damaged walls and unsafe areas, modifying building models in 3D to reflect changes, and adding virtual post-it notes and warning signs. These changes can be shared with other users over a wireless network, and also communicated back to the operations managers at headquarters to increase their situational awareness. These operations managers are responsible for controlling the entire effort, and giving them the ability to see information gathered in real-time would help them better understand the situation and use resources effectively. Apart from just gathering information though, the operations managers also have the ability to modify the information and add markers to the environment to guide the mobile users. This could be used to indicate places to visit or avoid based on the combined knowledge of all the participating users. For this scenario, I envisage the various participants will be using numerous types of technology depending on what is appropriate for the task. For example, outdoor users may wear full AR systems or carry hand held computers. Indoor users may use desktop monitors, large wall displays, or projector tables with tangible interfaces. I think that making all of these different technologies work together to solve a difficult problem is the next big step, with many benefits to society as a result.
Research in the area of outdoor AR environments is still in its infancy. This dissertation has provided a number of contributions to the area but there is still much research to be performed before AR applications will become mainstream. This is an exciting time to be working in the field, with many currently untouched problems that remain to be solved and so many exciting possible applications. I hope that in the near future it may be possible to realise a mobile AR system that can be used both indoors and outdoors, fits into the pocket, works all the time without reliability problems, and provides a real sense of presence for the virtual objects overlaid onto the physical world.
"Men have become the tools of their tools"
Henry David Thoreau (1817-1862)
During the research for this dissertation, wearable computers have evolved from very simple and slow 2D graphics to highly detailed 3D rendering systems capable of video overlay, texture mapped triangles, and the ability to render millions of triangles per second for realistic AR output. As discussed in the previous chapter, this evolution process is ongoing as various devices are improved by their respective manufacturers. This appendix discusses the numerous mobile outdoor AR systems that I have constructed at key points in their development, broken down into the equipment that was used, how it was mounted onto the user, and the software that was developed for the platform. Since these three categories were not all changed at the same time (the same software may have been used with multiple types of equipment), platforms are described at various important milestones of development.
The first prototype system I worked on the development of was named Map-In-The-Hat [THOM98] [PIEK99a], with the objective being to develop a mobile navigation system that could guide users towards waypoints in unfamiliar terrain. This system could also be referred to as the Tinmith-I prototype since further designs were named after this, although it was never referred to in any literature using this name. Although the computer used was designed for mounting on a belt, there were too many components to carry and so a ruck sack was used instead. While this system was used for several informal studies, the wearable computer had faulty video hardware that caused it to run very slow, and the GPS did not reliably receive satellite signals.
Figure A‑1†††††† Phoenix-II wearable computer with batteries, cables, and belt mounting
Figure A‑2†††††† Map-in-the-Hat prototype inside ruck sack, with antenna, cables, and HMD
Figure A‑3†††††† Screen shots of Map-in-the-Hat indicating a waypoint on the display
An initial collection of equipment was supplied by DSTO and was used for this initial evaluation. This initial equipment included the following:
∑ Phoenix 486 belt mounted wearable computer (16 mb memory, 500 mb hard drive, 640x480x4bpp VGA output @ 2 Hz - see Figure A‑1)
Precision Navigation TCM2 magnetic compass (15 Hz updates)
Trimble SVeeSix GPS with radio-based differential receiver (1 Hz updates at 5-10 metres accuracy)
Sony Glasstron PLM-100 NTSC resolution HMD with optical overlay
Phoenix forearm keyboard for data entry
12V lead acid batteries rated at 14 Wh
A simple ruck sack was used to carry all the components on the userís back, as shown in Figure A‑2. This ruck sack did not organise the components inside and so was difficult to work with. Some issues associated with this ruck sack design are:
∑ Components are placed arbitrarily into the sack
Equipment moves around due to non-rigid mounting
Cables and connectors break easily due to movement inside backpack
Not possible to get easy access to components or wires
The software for this initial prototype was quite primitive and was not implemented with a software architecture. The application was a simple X11 program written for Linux that processed the compass and GPS inputs to draw a steering gadget onto the display. The optical overlay capability of the HMD was used to produce the final AR view of the world. The steering information was a diamond that overlaid the destination target along with bearing and distance values, as shown in Figure A‑3. This prototype was useful for discovering problems with the numerous components being integrated, and the knowledge gained was used for the development of future systems.
After the experience from Map-in-the-Hat a new system was designed from the ground up for the purpose of performing the mobile navigation task much more effectively. Instead of just providing simple navigation cues, this software was designed to support both 2D maps and 3D immersive wire frames with optical overlay [THOM99]. This version is implemented using a flexible software architecture [PIEK99b] designed to be extended for other tasks in the future. Later systems use this software architecture for the development of other applications. This version is named Tinmith-2, and there was no version one because this was reserved for the previous Map-in-the-Hat system. The name Tinmith was originally coined based on an abbreviation for ďThis Is Not Map In The HatĒ, although is never referred to in this way.
Figure A‑4†††††† Tinmith-2 hiking frame with some equipment attached
The equipment for this version was similar to the last, except the wearable computer and GPS were both replaced to improve reliability. The laptop had the added benefit of providing an LCD screen and keyboard to enable simple debugging while outdoors. The following equipment was added:
∑ Toshiba CDS320 Pentium-200 laptop (64 mb memory, 2 Gb hard drive, 800x600x16bpp 2D VGA with C&T 65555)
Garmin GPS 12XL 12 channel hand-held receiver with radio-based differential receiver (1 Hz updates at 5-10 metres accuracy)
The carrying sack was replaced with a rigid frame (see Figure A‑4) that allowed much more stable attachment of the components to the backpack. This frame also permitted much greater access to the equipment and made debugging much easier for development. Some properties associated with this mounting are:
∑ Hiking frame with aluminium rails to attach components
Small amount of reasonably light equipment
Easy access to all equipment and wires, improving visibility and debugging
Objects held with packing tape and gravity assisted to hold in correct position
Laptop contained within a cardboard box strapped to hiking frame with packing tape
HMD controller not mountable and placed in the pocket of the user
Design not robust enough since the tape would gradually unattach from the frame
Figure A‑5†††††† 2D top down map overlaid on physical world (using offline AR overlay)
Figure A‑6†††††† 2D top down map overlay with current location relative to nearby buildings
This application performed a similar navigation task as Map-in-the-Hat, but instead used a top down view with a steering arrow (see Figure A‑5 and Figure A‑6) to indicate the direction to walk in while at the same time showing a line drawn map of the area with other waypoints [PIEK99b]. This arrow was much more efficient to use since it was not limited to the field of view of the HMD; the previous steering diamond could not indicate to the user the orientation of an object that was to the side of or behind the user. Using the forearm keyboard the user may mark new waypoints and control the information displayed. An immersive 3D wire frame display (see Figure A‑7) was also developed so that models could be overlaid onto buildings, and this was used to visualise simple extensions to buildings in the physical world [THOM99]. Since the video hardware supported only 2D acceleration, wire frame rendering and optical overlay was used to minimise load on the CPU. This software also contained an internal architecture that allowed various software modules to perform different tasks and then share data over an internal bus. By separating the software into modules it can be easily distributed across multiple machines, supporting distributed processing and monitoring by outside computers on a network. This distribution capability was useful when there were not enough serial ports on the Toshiba laptop, and the Phoenix-2 was used to provide an extra RS-232 serial port processed by a software module and then sent to the main laptop.
Figure A‑7†††††† 3D wireframe overlay of building, with small extension made to the left
For the Tinmith-3 prototype the existing software was extended so that it was able to share information with other wearables and indoor computer systems. Using the DIS protocol [IEEE93], various software applications may render views of both physical and simulated entities, and provide situational awareness to wearable and fixed users [PIEK99c]. The backpack was reconstructed to use rigid mountings and straps instead of the fragile packing tape, greatly improving its reliability.
The equipment for this prototype was the same as the previous Tinmith-2, except the Phoenix-2 wearable computer providing the extra serial port was removed. The following components were added:
∑ Quatech 4-port PCMCIA serial adaptor
Lucent WaveLAN PCMCIA wireless network card
Figure A‑8†††††† Tinmith-3 backpack with HMD, head tracker, and forearm keyboard
The previous backpack using packing tape was rebuilt to use a more permanent and fixed design that was much more reliable, and is shown in Figure A‑8. Some properties of this new fixed design are:
∑ Same hiking frame as previous
Wooden panels hold devices that cannot mount directly to the frame, fixed with screws and straps
Panels held using metal brackets allowing some limited rearrangements
Frame is an awkward curved shape to mount to
HMD controller not mountable and placed in the pocket of the user
Laptop cannot be opened and used since it is tightly strapped up
Much more rigid, safer, and reliable design
The same software architecture from Tinmith-2 was used, except various extensions were added to capture data from DIS protocol-based simulation software and present it to the display module, with tracking information broadcast back to other DIS simulation software. Figure A‑9 depicts the layout of all the modules in the system, with the lsapdis and tracker modules providing the extra interfaces for this support. Tinmith-2 contained the same modules and layout except for these two extensions.
Figure A‑9†††††† Software interconnect diagram for Tinmith-2 to Tinmith-4 prototypes
Figure A‑10†††† View of ModSAF tool with simulated entities and a wearable user
To demonstrate the system, the ModSAF tool (see Figure A‑10) is used to generate a set of simulated helicopters that fly across a virtual landscape according to a set of rules created by the simulation manager. The display for ModSAF can show the locations of entities it is simulating, as well as any entities generated by other computers, from a top down 2D control panel. The wearable user located outdoors (see Figure A‑11) generates DIS packets that are then displayed as another entity in ModSAF. For a VR style view, the MetaVR application is capable of rendering DIS entities onto a 3D landscape, with views such as Figure A‑12 showing an avatar of the wearable user observing a ModSAF simulated helicopter. On the AR HMD of the user, both of the previously discussed 2D top down and 3D immersive views may be used to display points and labels representing the entities simulated by ModSAF or generated by other wearable computers. The 2D view shown in Figure A‑13 is the wearableís top down HMD view of the simulated entities in the previous figures.
Figure A‑11†††† Wearable user in outdoor environment generates DIS packets
Figure A‑12†††† MetaVR view of avatar for wearable user and helicopter for ModSAF entity
Figure A‑13†††† DIS entities overlaid in yellow on HMD with a top down view
The Tinmith-4 prototype was extended to develop two new applications. The first was an architectural visualisation application, where filled triangles can be rendered to the display to present to the user more realistic looking models. The game Quake was also modified to produce a new AR version named ARQuake [THOM00] [PIEK02d], using the Tinmith-4 software to provide position and orientation information about the user. These were the last applications developed using the current software architecture before it was replaced with a rewritten version.
Same as the previous Tinmith-3 prototype
Same as the previous Tinmith-3 prototype
The architectural visualisation application was developed by rewriting the previous display module with a new 3D renderer, capable of drawing filled depth sorted triangles instead of just simple wire frames. Numerous optimisations were performed to make the rendering possible on the hardware available, and the complexity of the models was limited to a few hundred polygons to keep reasonable frame rates. Models were converted from AutoCAD DXF files and loaded in, allowing architects to preview the models designed on desktop software with outdoor AR systems, as shown in Figure A‑14.
Figure A‑14†††† Visualising artificial CAD building extensions overlaid on physical world
The ARQuake application was developed using the GPL released Quake source code from ID Software [IDSO01], and is a first person shoot-em up game so it has a view point similar to a VR system. When operated on an optical overlay HMD, objects in the game appear over the physical world, as shown in Figure A‑15. Instead of using a keyboard or mouse to control the game, it was modified to use the position and orientation sensors on the body as an input device. An extra software module was written using the Tinmith architecture that generated UDP packets for ARQuake based on the tracker values available. Using a custom haptic feedback gun, the user may attack monsters using a highly intuitive trigger input that most users are familiar with. Since the game does not support individually aimed weapons, the gun is not tracked and the user aims with their head.
Figure A‑15†††† ARQuake implemented using optical AR with virtual monsters shown
Although the previous software architecture was quite powerful, it was optimised for the navigation task and the use of older equipment. With the arrival of hardware accelerated OpenGL and faster CPUs, I wanted to start doing research into complex 3D modelling tasks involving input gloves, vision tracking, and live video overlay. The goal was to produce an interactive modelling system that used intuitive hand gestures to control the environment, with an artistic impression shown in Figure A‑16. The capabilities desired were out of the scope of the original software architecture and so a new one was designed to be the platform for all my future AR application development. This prototype was used to implement my first demonstrations of street furniture and infinite planes modelling in AR [PIEK01b] [PIEK02c].
Figure A‑16†††† Mock up demonstrating how a modelling system could be used outdoors
For this update almost all of the equipment was replaced since the previous equipment was becoming quite outdated. The equipment added was as follows:
∑ Gateway Solo Pentium-II 450 laptop (64 mb memory, 6 Gb hard drive, 1024x768x24bpp OpenGL with ATI Rage Mach64)
CPIA chipset-based USB camera (352x288 frames at 5 Hz)
Custom designed pinch gloves with metallic pads and low power microcontroller interface
Glasstron PLM-700E 800x600 resolution HMD with opaque settings for video AR
LCD television rendering live video overlay previews for bystanders to observe during demonstrations
InterSense IS-300 hybrid orientation sensor (100+ Hz updates)
Trimble OEM Ag132 GPS with Omnistar satellite differential receiver (10 Hz updates at 50 cm accuracy)
12V lead acid batteries rated at 85 Wh
Similar mounting techniques as previous were used for the newer components, and due to the increased complexity of the design this backpack began to become unwieldy, as seen in Figure A‑17 and Figure A‑18. This was the last prototype to use the current backpack before the switch to the new Tinmith-Endeavour design. Some properties of the current design are:
∑ Component count larger due to increased functionality (TV output, cameras, gloves)
Cabling complexity increased due to extra equipment
Connectors fitted to waist holders for attaching devices while mobile
Not all devices fit the wooden panels and curvature of backpack frame
Difficult to add extra devices without moving all existing equipment around
Sony Glasstron was still not mounted, held with a strap around the neck
Figure A‑17†††† Side view of original Tinmith-evo5 backpack, with cabling problems
Figure A‑18†††† Close up view of messy cable bundles and miscellaneous input devices
This version uses a completely rewritten set of software designed to perform the modelling tasks envisaged [PIEK01c] [PIEK02a] [PIEK03f]. The implementation was described previously in Chapter 6, and is written in C++ and uses a much more efficient model for processing data. Using an object-oriented approach, each object contains data that can be listened on for changes by other objects in the system. Since all the components of the application are written using this software architecture, everything is tightly integrated and consistent. A user interface based on cursors mapped to the userís thumbs as well as a menu system was implemented to support complex modelling tasks such as CSG and 3D manipulation. The modelling tasks implemented were simple versions of the infinite planes and street furniture techniques described previously. This first prototype (with video overlay AR output shown in Figure A‑19) was mostly used for experiments in testing out initial modelling techniques and gathering information for the development of later improved versions.
Figure A‑19†††† Screen shots of the first Tinmith-Metro release in use outdoors
As part of informal user experiments described in Chapter 5, I wanted to test the usefulness of the user interface in an indoor VR environment [PIEK02b] [PIEK02e]. Since the interface is relatively generic it may be used in VR systems with only slight tracker changes. The hardware used was similar to typical VR systems, with a Polhemus magnetic tracker and opaque HMD used indoors, as shown in Figure A‑20.
This system did not use a backpack computer and instead used indoor-based hardware to render to the VR display. The components used were as follows:
∑ Dell Inspiron 8100 laptop with Pentium-III 1.2 Ghz (512 mb memory, 40 Gb hard drive, 1600x1400x24bpp OpenGL with NVIDIA GeForce2Go)
Polhemus magnetic tracker with 3 sensors
Daeyang Cyvisor 800x600 resolution opaque HMD
Power supplied from 240 volt mains supply
Custom designed pinch gloves with metallic pads and low power microcontroller interface (no vision tracking)
Since the equipment was used indoors and the user has a limited working range, everything was mounted to various parts of the room. Some properties of this mounting are as follows:
∑ One Polhemus magnetic sensor was mounted on the HMD to track head motion
Two Polhemus magnetic sensors were mounted onto the gloves of the user, replacing the vision tracking system
User is tethered to the system via a cable bundle attached to the ceiling
Reliable and easy to configure since the environment is fixed
Figure A‑20 VR immersive system used to control the Tinmith-Metro user interface
The same modelling software previously described was modified to produce a VR version for use indoors, with a small example world shown in Figure A‑20. The video overlay was disabled, software reconfigured to use the Polhemus trackers, and extra features added to support the VR nature of the system. The same interface involving a cursor and menus was used to control the application, and users experienced with the outdoor system could use exactly the same interaction techniques to control the indoor system.
For this prototype, all of the lessons learned from previous versions were used to redesign both the hardware and the user interface techniques. Although using similar equipment to the previous prototype, a new backpack frame was designed with DSTO for the specific purpose of being used for outdoor AR. The user interface was redesigned and simplified to make modelling tasks for the user easier, and a number of new construction at a distance techniques were added to supplement the previous techniques. Many of the techniques discussed in this dissertation are implemented in this prototype.
Figure A‑21†††† Side and front views of the Tinmith-Endeavour backpack in use outdoors
Along with the new backpack design, a newer and much more powerful laptop was introduced that is capable of rendering live video overlay texture maps for AR at interactive frame rates. By using newer and higher resolution 1394 Firewire cameras, the quality of the video is improved dramatically from previous prototypes and produces much more compelling demonstrations outdoors. The new components used are as follows:
∑ Dell Inspiron 8100 laptop with Pentium-III 1.2 Ghz (512 mb memory, 40 Gb hard drive, 1600x1400x24bpp OpenGL with NVIDIA GeForce2Go)
Point Grey Research Firefly 1394 camera (640x480 frames at 15 Hz)
Trimble Ag132 GPS with Omnistar or radio differential receiver (10 Hz updates at 50 cm accuracy)
Custom designed pinch gloves with wire wound pads and low power microcontroller interface
This prototype introduced the new backpack design from DSTO [PIEK02h], which was discussed previously in Chapter 7 and shown in Figure A‑21. Due to the flexible design of the backpack, components can be mounted in a number of configurations and easily changed. The following features are available with this backpack design:
∑ Using Velcro attachments, components can be easily rearranged
Cabled fixed internally with cable ties to prevent movement
Hinged design allows laptop to be used on a horizontal surface while outdoors
Laptop screen held open to permit bystanders to see live AR video overlay
Connectors and switches mounted on shoulder straps for easy access
New helmet design to mount cameras and trackers onto HMD
The software architecture for this system is similar to the first Tinmith-evo5 prototype, except that improvements were made to improve the implementation of internal objects, speed up performance, and extend the serialisation mechanism to use it for distributed indoor/outdoor collaboration tasks. The user interface was improved based on the informal user studies discussed in previous chapters [PIEK03d], making it more intuitive and efficient to control the system with. While the previous prototype only implemented a small number of outdoor modelling techniques, this new version implements many of the construction at a distance techniques described in this dissertation [PIEK01b] [PIEK02g] [PIEK03c]. This version contains the most up to date demonstration of the techniques in this dissertation.
Figure A‑22†††† Screen capture of the latest Tinmith-Metro release in use outdoors
The previous Tinmith-evo5 prototype two used for outdoor AR modelling tasks is also able to run the modified ARQuake game discussed previously. For this version, the software was updated to use the latest Tinmith-evo5 software architecture and configuration system, supporting the latest equipment with improved accuracy and performance. The main noticeable improvement was the use of the GLquake source code with OpenGL acceleration and a video-based AR overlay modification to greatly improve the quality of the output [PIEK03a].
Same as the previous Tinmith-evo5 prototype two, with an extra input device for ARQuake. The haptic feedback gun was replaced with a much simpler USB-based version that draws less power and looks more like a toy, as shown in Figure A‑23.
Figure A‑23†††† USB mouse embedded into a childrenís bubble blowing toy
Figure A‑24†††† Monsters overlaid on the physical world with video overlay ARQuake
Same as the previous Tinmith-evo5 prototype two
The ARQuake software described previously was rewritten to use the new Tinmith-evo5 software architecture, which is both more efficient and supports more types of tracking devices. The type of AR implemented was also changed from optical to video overlay, similar to the latest Tinmith-evo5 software. OpenGL stencil buffers were used to overlay a video texture onto the areas that were black in the original optical version. The final result is excellent image quality with vivid colours and sharp edges well suited for demonstrations, as shown in Figure A‑24.
This appendix has presented the history and evolving nature of the hardware and software developed as part of my research into outdoor AR. While the quality of the first AR systems was initially quite primitive with limited functionality, the latest AR systems are capable of performing complex 3D modelling tasks with displays that are much more compelling for the user.
"Reality is merely an illusion, albeit a very persistent one."
Albert Einstein (1879-1955)
This dissertation has resulted in the production of a large quantity of pictures, videos, and other support information that is not included with this thesis. Some of this material is contained on a CD-ROM produced at the time of the publication of this dissertation, while other up to date information is available on the Internet.
The attached CD-ROM contains electronic copies of the following material:
∑ Copy of this dissertation, both as a whole and separated by chapter (PDF)
Archive of all conference and journal papers published to date (PDF)
Videos demonstrating the modelling applications in use outdoors (MPEG1)
Videos demonstrating applications discussed in the evolutions appendix (MPEG1)
The documents in PDF format require the use of Adobe Acrobat Reader or Ghostscript to view or print the files. Acrobat Reader is available free from http://www.adobe.com and Ghostscript is included with most free operating systems such as Linux, FreeBSD, and Cygwin. The videos are encoded in MPEG1 format, and should be viewable using most video playback software available in the last five years. MPEG1 files are known to playback on all versions of Windows 98 upwards, most versions of QuickTime for the Macintosh, and using open source video players available for Linux and FreeBSD.
The research performed in this dissertation is part of an ongoing project and is under continuous development. For the latest information, pictures, and videos, please visit http://www.tinmith.net and http://wearables.unisa.edu.au. The author can be contacted at firstname.lastname@example.org or email@example.com.
"Wise men make proverbs, but fools repeat them."
Samuel Palmer (1805-1880)
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