Comp4010 Lecture4 AR Tracking and Interaction
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The document discusses augmented reality (AR), covering definitions, technologies, tracking methods, and challenges in implementation. It highlights the necessity of real-time interaction and the integration of virtual images with the real world using various tracking techniques such as optical, inertial, and marker-based tracking. Additionally, it addresses the evolution of simultaneous localization and mapping (SLAM) systems and their applications in AR and robotics.
![Edge Based Tracking
• Example: RAPiD [Drummond et al. 02]
• Initialization, Control Points, Pose Prediction (Global Method)](https://image.slidesharecdn.com/comp4010-lecture4-final-2021-210817203146/85/Comp4010-Lecture4-AR-Tracking-and-Interaction-67-320.jpg){kind=tracking}
![Line Based Tracking
• Visual Servoing [Comport et al. 2004]](https://image.slidesharecdn.com/comp4010-lecture4-final-2021-210817203146/85/Comp4010-Lecture4-AR-Tracking-and-Interaction-69-320.jpg){kind=tracking}
Comp4010 Lecture4 AR Tracking and Interaction
- 1. AR TRACKING AND INTERACTION COMP 4010 Lecture Four Mark Billinghurst August 17th 2021 [email protected]
- 2. REVIEW
- 3. Augmented Reality Definition • Combines Real and Virtual Images • Both can be seen at the same time • Interactive in real-time • The virtual content can be interacted with • Registered in 3D • Virtual objects appear fixed in space
- 4. Augmented RealityTechnology • Combines Real and Virtual Images • Needs: Display technology • Interactive in real-time • Needs: Input and interaction technology • Registered in 3D • Needs: Viewpoint tracking technology
- 5. Example: MagicLeap ML-1 AR Display •Display • Multi-layered Waveguide display •Tracking • Inside out SLAM tracking •Input • 6DOF wand, gesture input
- 6. AR Display Technologies • Classification (Bimber/Raskar 2005) • Head attached • Head mounted display/projector • Body attached • Handheld display/projector • Spatial • Spatially aligned projector/monitor
- 7. Bimber, O., & Raskar, R. (2005). Spatial augmented reality: merging real and virtual worlds. CRC press. DisplayTaxonomy
- 8. Types of Head Mounted Displays Occluded See-thru Multiplexed
- 9. Optical see-through Head-Mounted Display Virtual images from monitors Real World Optical Combiners
- 10. Optical Design – Curved Mirror ▪ Reflect off free-space curved mirror
- 12. Example: Varjo XR-1 • Wide field of view • 87 degrees • High resolution • 1920 x 1080 pixel/eye • 1440 x 1600 pixel insert • Low latency stereo cameras • 2 x 12 megapixel • < 20 ms delay • Integrated Eye Tracking
- 13. Varjo XR-1 Image Quality
- 14. Multiplexed Display Virtual Image ‘inset’ into Real World
- 16. SpatialAugmented Reality • Project onto irregular surfaces • Geometric Registration • Projector blending, High dynamic range • Book: Bimber, Rasker “Spatial Augmented Reality”
- 17. Video MonitorAR Video cameras Monitor Graphics Combiner Video Stereo glasses
- 18. Magic Mirror AR Experience • See AR overlay of an image of yourself
- 19. AR RequiresTracking and Registration • Registration • Positioning virtual object wrt real world • Fixing virtual object on real object when view is fixed • Calibration • Offline measurements • Measure camera relative to head mounted display • Tracking • Continually locating the user’s viewpoint when view moving • Position (x,y,z), Orientation (r,p,y)
- 20. Sources of Registration Errors •Static errors • Optical distortions (in HMD) • Mechanical misalignments • Tracker errors • Incorrect viewing parameters •Dynamic errors • System delays (largest source of error) • 1 ms delay = 1/3 mm registration error
- 21. 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
- 22. Reducing dynamic errors (1) •Reduce system lag •Faster components/system modules •Reduce apparent lag •Image deflection •Image warping
- 23. Reducing dynamic errors (2) • Match video + graphics input streams (video AR) • Delay video of real world to match system lag • User doesn’t notice • Predictive Tracking • Inertial sensors helpful Azuma / Bishop 1994
- 24. 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)
- 26. OPTICAL TRACKING
- 28. Why Optical Tracking for AR? • Many AR devices have cameras • Mobile phone/tablet, Video see-through display • Provides precise alignment between video and AR overlay • Using features in video to generate pixel perfect alignment • Real world has many visual features that can be tracked from • Computer Vision is a well established discipline • Over 40 years of research to draw on • Old non real time algorithms can be run in real time on todays devices
- 29. Common AR Optical Tracking Types • Marker Tracking • Tracking known artificial markers/images • e.g. ARToolKit square markers • Markerless Tracking • Tracking from known features in real world • e.g. Vuforia image tracking • Unprepared Tracking • Tracking in unknown environment • e.g. SLAM tracking
- 30. Visual Tracking Approaches • Marker based tracking with artificial features • Make a model before tracking • Model based tracking with natural features • Acquire a model before tracking • Simultaneous localization and mapping • Build a model while tracking it
- 31. Marker Tracking • Available for more than 20 years • Several open-source solutions exist • ARToolKit, ARTag, ATK+, etc • Fairly simple to implement • Standard computer vision methods • A rectangle provides 4 corner points • Enough for pose estimation!
- 32. Demo: ARToolKit
- 33. Key Problem: Finding Camera Position • Need camera pose relative to marker to render AR graphics Known image Image in Camera view Overlay AR content
- 34. Goal: Find Camera Pose • Knowing: • Position of key points in on-screen video image • Camera properties (focal length, image distortion)
- 35. Coordinates for Marker Tracking
- 36. Coordinates for Marker Tracking Marker Camera •Final Goal •Rotation & Translation 1: Camera Ideal Screen •Perspective model •Obtained from Camera Calibration 2: Ideal Screen Observed Screen •Nonlinear function (barrel shape) •Obtained from Camera Calibration 3: Marker Observed Screen •Correspondence of 4 vertices •Real time image processing
- 37. Marker Tracking – General Principle 1. Capturing image with known camera 2. Search for quadrilaterals 3. Pose estimation from homography 4. Pose refinement Minimize nonlinear projection error 5. Use final pose 37 1 3 2 4 5 Image: Daniel Wagner
- 38. Marker Based Tracking: ARToolKit https://github.com/artoolkit
- 39. MarkerTracking – Fiducial Detection • Threshold the whole image to black and white • Search scanline by scanline for edges (white to black) • Follow edge until either • Back to starting pixel • Image border • Check for size • Reject fiducials early that are too small (or too large)
- 40. MarkerTracking – Rectangle Fitting • Start with an arbitrary point “x” on the contour • The point with maximum distance must be a corner c0 • Create a diagonal through the center • Find points c1 & c2 with maximum distance left and right of diag. • New diagonal from c1 to c2 • Find point c3 right of diagonal with maximum distance
- 41. MarkerTracking – Pattern checking • Calculate homography using the 4 corner points • “Direct Linear Transform” algorithm • Maps normalized coordinates to marker coordinates (simple perspective projection, no camera model) • Extract pattern by sampling and check • Id (implicit encoding) • Template (normalized cross correlation)
- 42. Marker tracking – Pose estimation • Calculates marker pose relative to the camera • Initial estimation directly from homography • Very fast, but coarse with error • Jitters a lot… • Iterative Refinement using Gauss-Newton method • 6 parameters (3 for position, 3 for rotation) to refine • At each iteration we optimize on the error • Iterate
- 43. Outcome: Camera Transform • Transformation from Marker to Camera • Rotation and Translation TCM : 4x4 transformation matrix from marker coord. to camera coord.
- 44. Tracking challenges inARToolKit 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. But - You can’t cover world with ARToolKit Markers!
- 47. 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! èMarkerless Tracking Mechanica l Tracker
- 49. Visual Tracking Approaches • Marker based tracking with artificial features • Make a model before tracking • Model based tracking with natural features • Acquire a model before tracking • Simultaneous localization and mapping • Build a model while tracking it
- 50. 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
- 51. Natural Features • Detect salient interest points in image • Must be easily found • Location in image should remain stable when viewpoint changes • Requires textured surfaces • Alternative: can use edge features (less discriminative) • Match interest points to tracking model database • Database filled with results of 3D reconstruction • Matching entire (sub-)images is too costly • Typically interest points are compiled into “descriptors” Tracking 51 Image: Gerhard Reitmayr Image: Martin Hirzer
- 52. TextureTracking
- 53. Demo: Vuforia Texture Tracking https://www.youtube.com/watch?v=1Qf5Qew5zSU
- 54. Tracking by Keypoint Detection • This is what most trackers do… • Targets are detected every frame • Popular because tracking and detection are solved simultaneously Keypoint detection Descriptor creation and matching Outlier Removal Pose estimation and refinement Camera Image Pose Recognition
- 55. Detection and Tracking Detection Incremental tracking Tracking target detected Tracking target lost Tracking target not detected Incremental tracking ok Start + Recognize target type + Detect target + Initialize camera pose + Fast + Robust to blur, lighting changes + Robust to tilt • Tracking and detection are complementary approaches. • After successful detection, the target is tracked incrementally. • If the target is lost, the detection is activated again
- 56. What is a Keypoint? • It depends on the detector you use! • For high performance use the FAST corner detector • Apply FAST to all pixels of your image • Obtain a set of keypoints for your image • Describe the keypoints Rosten, E., & Drummond, T. (2006, May). Machine learning for high-speed corner detection. In European conference on computer vision (pp. 430-443). Springer Berlin Heidelberg.
- 57. FAST Corner Keypoint Detection
- 58. Example:FAST Corner Detection https://www.youtube.com/watch?v=fevfxfHnpeY
- 59. Descriptors • Describe the Keypoint features • Can use SIFT • Estimate the dominant keypoint orientation using gradients • Compensate for detected orientation • Describe the keypoints in terms of the gradients surrounding it Wagner D., Reitmayr G., Mulloni A., Drummond T., Schmalstieg D., Real-Time Detection and Tracking for Augmented Reality on Mobile Phones. IEEE Transactions on Visualization and Computer Graphics, May/June, 2010
- 60. Database Creation • Offline step – create database of known features • Searching for corners in a static image • For robustness look at corners on multiple scales • Some corners are more descriptive at larger or smaller scales • We don’t know how far users will be from our image • Build a database file with all descriptors and their position on the original image
- 61. Real-time Tracking • Search for known keypoints in the video • Create the descriptors • Match the descriptors from the live video against those in the database • Brute force is not an option • Need the speed-up of special data structures Keypoint detection Descriptor creation and matching Outlier Removal Pose estimation and refinement Camera Image Pose Recognition
- 62. NFT – Outlier removal • Removing outlier features • Several removal techniques • Simple geometric tests • Is the keypoint rotation invariant? • Do keypoints remain relative to each other? • Homography-based tests Rotation Invariant
- 63. NFT – Pose refinement • Pose from homography makes good starting point • Use Gauss-Newton iteration • Try to minimize the re-projection error of the keypoints • Typically, 2-4 iterations are enough..
- 64. NFT – Real-time tracking • Search for keypoints in the video image • Create the descriptors • Match the descriptors from the live video against those in the database • Remove the keypoints that are outliers • Use the remaining keypoints to calculate the pose of the camera Keypoint detection Descriptor creation and matching Outlier Removal Pose estimation and refinement Camera Image Pose Recognition
- 65. Example Target Image Feature Detection AR Overlay
- 67. Edge Based Tracking • Example: RAPiD [Drummond et al. 02] • Initialization, Control Points, Pose Prediction (Global Method)
- 68. Demo: Edge Based Tracking
- 69. Line Based Tracking • Visual Servoing [Comport et al. 2004]
- 70. 3D Model BasedTracking • Tracking from 3D object shape • Align detected features to 3D object model • Examples • SnapChat Face tracking • Mechanical part tracking • Vehicle tracking • Etc..
- 71. Typical Model Based Tracking Algorithm
- 72. Example: Vuforia Model Tracker • Uses pre-captured 3D model for tracking • On-screen guide to line up model
- 74. Taxonomy of Model Based Tracking Lowney, M., & Raj, A. S. (2016). Model based tracking for augmented reality on mobile devices.
- 75. Marker vs.Natural FeatureTracking • Marker tracking • Usually requires no database to be stored • Markers can be an eye-catcher • Tracking is less demanding • The environment must be instrumented • 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
- 76. Visual Tracking Approaches • Marker based tracking with artificial features • Make a model before tracking • Model based tracking with natural features • Acquire a model before tracking • Simultaneous localization and mapping • Build a model while tracking it
- 78. Tracking from an Unknown Environment • What to do when you don’t know any features? • Very important problem in mobile robotics - Where am I? • SLAM • Simultaneously Localize And Map the environment • Goal: to recover both camera pose and map structure while initially knowing neither. • Mapping: • Building a map of the environment which the robot is in • Localisation: • Navigating this environment using the map while keeping track of the robot’s relative position and orientation
- 79. Parallel Tracking and Mapping Tracking Mapping New keyframes Map updates + Estimate camera pose + For every frame + Extend map + Improve map + Slow updates rate Parallel tracking and mapping uses two concurrent threads, one for tracking and one for mapping, which run at different speeds
- 80. Parallel Tracking and Mapping Video stream New frames Map updates Tracking Mapping Tracked local pose FAST SLOW Simultaneous localization and mapping (SLAM) in small workspaces Klein/Drummond, U. Cambridge
- 81. Visual SLAM • Early SLAM systems (1986 - ) • Computer visions and sensors (e.g. IMU, laser, etc.) • One of the most important algorithms in Robotics • Visual SLAM • Using cameras only, such as stereo view • MonoSLAM (single camera) developed in 2007 (Davidson)
- 83. How SLAMWorks • Three main steps 1. Tracking a set of points through successive camera frames 2. Using these tracks to triangulate their 3D position 3. Simultaneously use the estimated point locations to calculate the camera pose which could have observed them • By observing a sufficient number of points can solve for both structure and motion (camera path and scene structure).
- 84. Evolution of SLAM Systems • MonoSLAM (Davidson, 2007) • Real time SLAM from single camera • PTAM (Klein, 2009) • First SLAM implementation on mobile phone • FAB-MAP (Cummins, 2008) • Probabilistic Localization and Mapping • DTAM (Newcombe, 2011) • 3D surface reconstruction from every pixel in image • KinectFusion (Izadi, 2011) • Realtime dense surface mapping and tracking using RGB-D
- 85. Demo:MonoSLAM
- 86. LSD-SLAM (Engel 2014) • A novel, direct monocular SLAM technique • Uses image intensities both for tracking and mapping. • The camera is tracked using direct image alignment, while • Geometry is estimated as semi-dense depth maps • Supports very large-scale tracking • Runs in real time on CPU and smartphone
- 87. Demo:LSD-SLAM
- 88. Direct Method vs. Feature Based • Direct uses all information in image, cf feature based approach that only use small patches around corners and edges
- 89. Applications of SLAM Systems • Many possible applications • Augmented Reality camera tracking • Mobile robot localisation • Real world navigation aid • 3D scene reconstruction • 3D Object reconstruction • Etc.. • Assumptions • Camera moves through an unchanging scene • So not suitable for person tracking, gesture recognition • Both involve non-rigidly deforming objects and a non-static map
- 90. Hybrid Tracking Interfaces • Combine multiple tracking technologies together • Active-Passive: Magnetic, Vision • Active-Inertial: Vison, inertial • Passoive-Inertial: Compass, inertial
- 91. Combining Sensors andVision • Sensors • Produces 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)
- 92. OutdoorARTracking System You, Neumann,Azuma outdoor AR system (1999)
- 93. Types of Sensor Fusion • Complementary • Combining sensors with different degrees of freedom • Sensors must be synchronized (or requires inter-/extrapolation) • E.g., combine position-only and orientation-only sensor • E.g., orthogonal 1D sensors in gyro or magnetometer are complementary • Competitive • Different sensor types measure the same degree of freedom • Redundant sensor fusion • Use worse sensor only if better sensor is unavailable • E.g., GPS + pedometer • Statistical sensor fusion www.augmentedrealitybook.org Tracking 93
- 94. Example: Outdoor Hybrid Tracking • Combines • computer vision • inertial gyroscope sensors • Both correct for each other • Inertial gyro • provides frame to frame prediction of camera orientation, fast sensing • drifts over time • Computer vision • Natural feature tracking, corrects for gyro drift • Slower, less accurate
- 95. Robust OutdoorTracking • HybridTracking • ComputerVision, GPS, inertial • Going Out • Reitmayr & Drummond (Univ. Cambridge) Reitmayr, G., & Drummond, T. W. (2006). Going out: robust model-based tracking for outdoor augmented reaity. In Mixed and Augmented Reality, 2006. ISMAR 2006. IEEE/ACM International Symposium on (pp. 109-118). IEEE.
- 96. Handheld Display
- 97. Demo: Going Out Hybrid Tracking
- 98. ARKit – Visual Inertial Odometry • Uses both computer vision + inertial sensing • Tracking position twice • Computer Vision – feature tracking, 2D plane tracking • Inertial sensing – using the phone IMU • Output combined via Kalman filter • Determine which output is most accurate • Pass pose to ARKit SDK • Each system compliments the other • Computer vision – needs visual features • IMU - drifts over time, doesn’t need features
- 99. ARKit –Visual Inertial Odometry • Slow camera • Fast IMU • If camera drops out IMU takes over • Camera corrects IMU errors
- 101. Conclusions • Tracking and Registration are key problems • Registration error • Measures against static error • Measures against dynamic error • AR typically requires multiple tracking technologies • Computer vision most popular • Research Areas: • SLAM systems, Deformable models, Mobile outdoor tracking
- 102. More Information Fua, P., & Lepetit, V. (2007). Vision based 3D tracking and pose estimation for mixed reality. In Emerging technologies of augmented reality: Interfaces and design (pp. 1-22). IGI Global.
- 103. 3: AR INTERACTION
- 104. Augmented Reality technology • Combines Real and Virtual Images • Needs: Display technology • Interactive in real-time • Needs: Input and interaction technology • Registered in 3D • Needs: Viewpoint tracking technology
- 105. How Do You Design an Interface for This?
- 106. AR Interaction • Designing AR Systems = Interface Design • Using different input and output technologies • Objective is a high quality of user experience • Ease of use and learning • Performance and satisfaction
- 107. Typical Interface Design Path 1/ Prototype Demonstration 2/ Adoption of Interaction Techniques from other interface metaphors 3/ Development of new interface metaphors appropriate to the medium 4/ Development of formal theoretical models for predicting and modeling user actions Desktop WIMP Virtual Reality Augmented Reality
- 108. Interacting with AR Content • You can see spatially registered AR.. how can you interact with it?
- 109. Different Types of AR Interaction • Browsing Interfaces • simple (conceptually!), unobtrusive • 3D AR Interfaces • expressive, creative, require attention • Tangible Interfaces • Embedded into conventional environments • Tangible AR • Combines TUI input + AR display
- 110. AR Interfaces as Data Browsers • 2D/3D virtual objects are registered in 3D • “VR in Real World” • Interaction • 2D/3D virtual viewpoint control • Applications • Visualization, training
- 111. AR Information Browsers • Information is registered to real-world context • Hand held AR displays • Interaction • Manipulation of a window into information space • Applications • Context-aware information displays Rekimoto, et al. 1997
- 112. NaviCam Demo (1997)
- 113. Navicam Architecture
- 114. Current AR Information Browsers • Mobile AR • GPS + compass • Many Applications • Wikitude • Yelp • Google maps • …
- 115. Example: Google Maps AR Mode • AR Navigation Aid • GPS + compass, 2D/3D object placement
- 117. Advantages and Disadvantages • Important class of AR interfaces • Wearable computers • AR simulation, training • Limited interactivity • Modification of virtual content is difficult Rekimoto, et al. 1997
- 118. 3D AR Interfaces • Virtual objects displayed in 3D physical space and manipulated • HMDs and 6DOF head-tracking • 6DOF hand trackers for input • Interaction • Viewpoint control • Traditional 3D user interface interaction: manipulation, selection, etc. Kiyokawa, et al. 2000
- 119. AR 3D Interaction (2000)
- 122. Advantages and Disadvantages • Important class of AR interfaces • Entertainment, design, training • Advantages • User can interact with 3D virtual object everywhere in space • Natural, familiar interaction • Disadvantages • Usually no tactile feedback • User has to use different devices for virtual and physical objects Oshima, et al. 2000