2013 Lecture3: AR Tracking
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2013 Lecture3: AR Tracking

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2013 COSC 426 Lecture 3 on AR Tracking. Taught by Mark Billinghurst from the HIT Lab NZ at the University of Canterbury. Taught on July 26th, 2013.

2013 COSC 426 Lecture 3 on AR Tracking. Taught by Mark Billinghurst from the HIT Lab NZ at the University of Canterbury. Taught on July 26th, 2013.

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  • 1. COSC 426: Augmented Reality Mark Billinghurst mark.billinghurst@hitlabnz.org July 26th 2013 Lecture 3: AR Tracking
  • 2. Key Points from Lecture 2
  • 3. “The product is no longer the basis of value.The experience is.” Venkat Ramaswamy The Future of Competition.
  • 4. experiences services products components Value Sony CSL © 2004 Gilmore + Pine: Experience Economy Function Emotion
  • 5. Interaction Design is All About You   Users should be involved throughout the Design Process   Consider all the needs of the user
  • 6. Interaction Design Process
  • 7. experiences applications tools components Building Compelling AR Experiences Tracking, Display Authoring Interaction Usability
  • 8. Optical see-through head-mounted display Virtual images from monitors Real World Optical Combiners
  • 9. Video see-through HMD Video cameras Monitors Graphics Combiner Video
  • 10. Video Monitor AR Video cameras Monitor Graphics Combiner Video Stereo glasses
  • 11. AR Tracking and Registration
  • 12.   Registration   Positioning virtual object wrt real world   Tracking   Continually locating the users viewpoint -  Position (x,y,z) -  Orientation (r,p,y)
  • 13. Tracking
  • 14. Tracking Requirements   Augmented Reality Information Display   World Stabilized   Body Stabilized   Head Stabilized Increasing Tracking Requirements Head Stabilized Body Stabilized World Stabilized
  • 15. Tracking Technologies  Active •  Mechanical, Magnetic, Ultrasonic •  GPS, Wifi, cell location  Passive •  Inertial sensors (compass, accelerometer, gyro) •  Computer Vision •  Marker based, Natural feature tracking  Hybrid Tracking •  Combined sensors (eg Vision + Inertial)
  • 16. AR Tracking Taxonomy e.g. AR Toolkit Low Accuracy at 15-60 Hz e.g. IVRD High Accuracy & High Speed Hybrid Tracking Limited Range e.g. HiBall Many Fiducials in space/time but no GPS Extended Range Indoor Environment e.g. WLVA Not Hybridized GPS or Camera or Compass Low Accuracy & Not Robust e.g. BARS Hybrid Tracking GPS and Camera and Compass High Accuracy & Robust Outdoor Environment AR TRACKING
  • 17. Tracking Types Magnetic Tracker Inertial Tracker Ultrasonic Tracker Optical Tracker Marker-Based Tracking Markerless Tracking Specialized Tracking Edge-Based Tracking Template-Based Tracking Interest Point Tracking Mechanical Tracker
  • 18. Mechanical Tracker   Idea: mechanical arms with joint sensors   ++: high accuracy, haptic feedback   -- : cumbersome, expensive Microscribe
  • 19. Magnetic Tracker   Idea: difference between a magnetic transmitter and a receiver   ++: 6DOF, robust   -- : wired, sensible to metal, noisy, expensive Flock of Birds (Ascension)
  • 20. Magnetic Tracking Error
  • 21. Ultrasonics Tracker   Idea: Time of Flight or Phase-Coherence Sound Waves   ++: Small, Cheap   -- : 3DOF, Line of Sight, Low resolution, Affected Environment Conditon (pressure, temperature) Ultrasonic Logitech IS600
  • 22. Inertial Tracker   Idea: measuring linear and angular orientation rates (accelerometer/gyroscope)   ++: no transmitter, cheap, small, high frequency, wireless   -- : drift, hysteris only 3DOF IS300 (Intersense) Wii Remote
  • 23. Mobile Sensors   Inertial compass   Earth’s magnetic field   Measures absolute orientation   Accelerometers   Measures acceleration about axis   Used for tilt, relative rotation   Can drift over time
  • 24. Global Positioning System (GPS)   Created by US in 1978   Currently 29 satellites   Satellites send position + time   GPS Receiver positioning   4 satellites need to be visible   Differential time of arrival   Triangulation   Accuracy   5-30m+, blocked by weather, buildings etc
  • 25. Problems with GPS   Takes time to get satellite fix   Satellites moving around   Earths atmosphere affects signal   Assumes consistent speed (the speed of light).   Delay depends where you are on Earth   Weather effects   Signal reflection   Multi-path reflection off buildings   Signal blocking   Trees, buildings, mountains   Satellites send out bad data   Misreport their own position
  • 26. Accurate to < 5cm close to base station (22m/100 km) Expensive - $20-40,000 USD
  • 27. Assisted-GPS (A-GPS)   Use external location server to send GPS signal   GPS receivers on cell towers, etc   Sends precise satellite position (Ephemeris)   Speeds up GPS Tracking   Makes it faster to search for satellites   Provides navigation data (don’t decode on phone)   Other benefits   Provides support for indoor positioning   Can use cheaper GPS hardware   Uses less battery power on device
  • 28. Assisted GPS
  • 29. Cell Tower Triangulation   Calculate phone position from signal strength   < 50 m in cities   > 1 km in rural
  • 30. WiFi Positioning   Estimate location by using WiFi access points   Can use know locations of WiFi access points   Triangulate through signal strength   Eg. PlaceEngine (www.placeengine.com)   Client software for PC and mobiles   SDK returns position   Accuracy   5 – 100m (depends on WiFi density)
  • 31. WiFi Hotspots in New York
  • 32. Indoor WiFi Location Sensing   Indoor Location   Asset, people tracking   Aeroscout   http://aeroscout.com/   WiFi + RFID   Ekahau   http://www.ekahau.com/   WiFi + LED tracking
  • 33. Integrated Systems   Combine GPS, Cell tower, WiFi signals   Skyhook (www.skyhookwireless.com)   Core Engine   Database of known locations   700 million Wi-Fi access points and cellular towers.
  • 34. Comparative Accuracies   Study testing iPhone 3GS cf. low cost GPS   A-GPS   8 m error   WiFi   74 m error   Cell Tower Positioning   600 m error Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi, and Cellular Positioning In GIScience on July 15, 2009 at 8:11 pm By Paul A Zandbergen Transactions in GIS, Volume 13 Issue s1, Pages 5 - 25
  • 35. Optical Tracking
  • 36. Optical Tracker   Idea: Image Processing and Computer Vision   Specialized   Infrared, Retro-Reflective, Stereoscopic   Monocular Based Vision Tracking ART Hi-Ball
  • 37. Outside-In vs. Inside-Out Tracking
  • 38. Optical Tracking Technologies   Scalable active trackers   InterSense IS-900, 3rd Tech HiBall   Passive optical computer vision   Line of sight, may require landmarks   Can be brittle.   Computer vision is computationally-intensive 3rd Tech, Inc.
  • 39. HiBall Tracking System (3rd Tech)   Inside-Out Tracker   $50K USD   Scalable over large area   Fast update (2000Hz)   Latency Less than 1 ms.   Accurate   Position 0.4mm RMS   Orientation 0.02° RMS
  • 40. Starting simple: Marker tracking   Has been done for more than 10 years   A square marker provides 4 corners   Enough for pose estimation!   Several open source solutions exist   Fairly simple to implement   Standard computer vision methods
  • 41. Marker Based Tracking: ARToolKit http://artoolkit.sourceforge.net/
  • 42. Tracking Range with Pattern Size Rule of thumb – range = 10 x pattern width
  • 43. Tracking Error with Range
  • 44. Tracking Error with Angle
  • 45. Tracking challenges in ARToolKit False positives and inter-marker confusion (image by M. Fiala) Image noise (e.g. poor lens, block coding / compression, neon tube) Unfocused camera, motion blur Dark/unevenly lit scene, vignetting Jittering (Photoshop illustration) Occlusion (image by M. Fiala)
  • 46. Limitations of ARToolKit   Partial occlusions cause tracking failure   Affected by lighting and shadows   Tracking range depends on marker size   Performance depends on number of markers   cf artTag, ARToolKitPlus   Pose accuracy depends on distance to marker   Pose accuracy depends on angle to marker
  • 47. Tracking, Tracking, Tracking
  • 48. Other Marker Tracking Libraries   arTag   http://www.artag.net/   ARToolKitPlus [Discontinued]   http://studierstube.icg.tu-graz.ac.at/handheld_ar/ artoolkitplus.php   stbTracker   http://studierstube.icg.tu-graz.ac.at/handheld_ar/ stbtracker.php   MXRToolKit   http://sourceforge.net/projects/mxrtoolkit/
  • 49. Markerless Tracking
  • 50. Markerless Tracking Magnetic Tracker Inertial Tracker Ultrasonic Tracker Optical Tracker Marker-Based Tracking Markerless Tracking Specialized Tracking Edge-Based Tracking Template-Based Tracking Interest Point Tracking   No more Markers! Markerless Tracking
  • 51. Natural feature tracking   Tracking from features of the surrounding environment   Corners, edges, blobs, ...   Generally more difficult than marker tracking   Markers are designed for their purpose   The natural environment is not…   Less well-established methods   Usually much slower than marker tracking
  • 52. Natural Feature Tracking   Use Natural Cues of Real Elements   Edges   Surface Texture   Interest Points   Model or Model-Free   ++: no visual pollution Contours Features Points Surfaces
  • 53. Texture Tracking
  • 54. Edge Based Tracking   RAPiD [Drummond et al. 02]   Initialization, Control Points, Pose Prediction (Global Method)
  • 55. Line Based Tracking   Visual Servoing [Comport et al. 2004]
  • 56. Model Based Tracking   Track from 3D model   Eg OpenTL - www.opentl.org   General purpose library for model based visual tracking
  • 57. Marker vs. natural feature tracking   Marker tracking   + Can require no image database to be stored   + Markers can be an eye-catcher   + Tracking is less demanding   - The environment must be instrumented with markers   - Markers usually work only when fully in view   Natural feature tracking   - A database of keypoints must be stored/downloaded   + Natural feature targets might catch the attention less   + Natural feature targets are potentially everywhere   + Natural feature targets work also if partially in view
  • 58. Hybrid Tracking
  • 59. Sensor tracking   Used by many “AR browsers”   GPS, Compass, Accelerometer, (Gyroscope)   Not sufficient alone (drift, interference)
  • 60. Outdoor Hybrid Tracking   Combines   computer vision -  natural feature tracking   inertial gyroscope sensors   Both correct for each other   Inertial gyro - provides frame to frame prediction of camera orientation   Computer vision - correct for gyro drift
  • 61. Combining Sensors and Vision   Sensors -  Produce noisy output (= jittering augmentations) -  Are not sufficiently accurate (= wrongly placed augmentations) -  Gives us first information on where we are in the world, and what we are looking at   Vision -  Is more accurate (= stable and correct augmentations) -  Requires choosing the correct keypoint database to track from -  Requires registering our local coordinate frame (online- generated model) to the global one (world)
  • 62. Outdoor AR Tracking System You, Neumann, Azuma outdoor AR system (1999)
  • 63. Robust Outdoor Tracking   Hybrid Tracking   Computer Vision, GPS, inertial   Going Out   Reitmayer & Drummond (Univ. Cambridge)
  • 64. Handheld Display
  • 65. Registration
  • 66. Spatial Registration
  • 67. The Registration Problem   Virtual and Real must stay properly aligned   If not:   Breaks the illusion that the two coexist   Prevents acceptance of many serious applications
  • 68. Sources of registration errors   Static errors   Optical distortions   Mechanical misalignments   Tracker errors   Incorrect viewing parameters   Dynamic errors   System delays (largest source of error) -  1 ms delay = 1/3 mm registration error
  • 69. Reducing static errors   Distortion compensation   Manual adjustments   View-based or direct measurements   Camera calibration (video)
  • 70. View Based Calibration (Azuma 94)
  • 71. Dynamic errors   Total Delay = 50 + 2 + 33 + 17 = 102 ms   1 ms delay = 1/3 mm = 33mm error Tracking Calculate Viewpoint Simulation Render Scene Draw to Display x,y,z r,p,y Application Loop 20 Hz = 50ms 500 Hz = 2ms 30 Hz = 33ms 60 Hz = 17ms
  • 72. Reducing dynamic errors (1)   Reduce system lag   Faster components/system modules   Reduce apparent lag   Image deflection   Image warping
  • 73. Reducing System Lag Tracking Calculate Viewpoint Simulation Render Scene Draw to Display x,y,z r,p,y Application Loop Faster Tracker Faster CPU Faster GPU Faster Display
  • 74. Reducing Apparent Lag Tracking Update x,y,z r,p,y Virtual Display Physical Display (640x480) 1280 x 960 Last known position Virtual Display Physical Display (640x480) 1280 x 960 Latest position Tracking Calculate Viewpoint Simulation Render Scene Draw to Display x,y,z r,p,y Application Loop
  • 75. Reducing dynamic errors (2)   Match input streams (video)   Delay video of real world to match system lag   Predictive Tracking   Inertial sensors helpful Azuma / Bishop 1994
  • 76. Predictive Tracking Time Position Past Future Can predict up to 80 ms in future (Holloway) Now
  • 77. Predictive Tracking (Azuma 94)
  • 78. Wrap-up   Tracking and Registration are key problems   Registration error   Measures against static error   Measures against dynamic error   AR typically requires multiple tracking technologies   Research Areas: Hybrid Markerless Techniques, Deformable Surface, Mobile, Outdoors
  • 79. Project List   Mobile   Hybrid Tracking for Outdoor AR   City Scale AR Visualization   Outdoor AR Authoring Tool   Outdoor AR collaborative game   AR interaction for Google Glass   Non-Mobile   AR Face Painting   AR Authoring Tool   Tangible AR puppeteer studio   Gesture based interaction with AR content
  • 80. More Information •  Mark Billinghurst –  mark.billinghurst@hitlabnz.org •  Websites –  www.hitlabnz.org