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Parallel Tracking and Mapping for Small AR Workspaces Vision Seminar

Parallel Tracking and Mapping for Small AR Workspaces Vision Seminar. 2008. 9. 4 (Thu) Young Ki Baik Computer Vision Lab. References. Parallel Tracking and Mapping for Small AR Workspaces Georg Klein, David Murray, ISMAR 2007 Visual Odometry David Nister et. al. , CVPR 2004. Outline.

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Parallel Tracking and Mapping for Small AR Workspaces Vision Seminar

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  1. Parallel Tracking and Mapping for Small AR WorkspacesVision Seminar 2008. 9. 4 (Thu) Young Ki Baik Computer Vision Lab.

  2. References • Parallel Tracking and Mapping for Small AR Workspaces • Georg Klein, David Murray, ISMAR 2007 • Visual Odometry • David Nister et. al. , CVPR 2004

  3. Outline • What is AR? • Previous works • Proposed methods • Demo • Conclusion

  4. What is AR? • AR: Augment Reality ugumentreality(AR) uses the real scene as the background, and makes applications with putting 3D objects to the background. Since the cost of augment reality is not as expensive asfull 3D virtual reality(VR), AR has been very popular research topic. A Full 3D (X) Expensive? (X) Full 3D? (O) Expensive? (O)

  5. Demonstration

  6. The Aim • AR with a hand-held camera • Visual Tracking provides registration

  7. The Aim • AR with a hand-held camera • Visual Tracking provides registration • Track without prior model of world

  8. The Aim • AR with a hand-held camera • Visual Tracking provides registration • Track without prior model of world • Challenges • Speed • Accuracy • Robustness • Interaction with real world

  9. Existing attempts : SLAM • SLAM : Simultaneous Localization and Mapping can use many different types of sensor to acquire observation data used in building the map such as laser rangefinders, sonar sensors and cameras. • Well-established in robotics (using a rich array of sensors) • Demonstrated with a single hand-held camera by Davison at 2003 (Mono-SLAM). • Mono-SLAM was applied to AR system at 2004.

  10. Existing attempts : Model based tracking • Model-based tracking is • More robust • More accurate • Proposed by Lepetit et. al. at ISMAR 2003

  11. Frame by Frame SLAM • Why? is SLAM fundamentally harder? Time One frame Find features Many DOF Update camera pose and entire map Draw graphics

  12. Frame by Frame SLAM • SLAM • Updating entire map every frame is so expensive!!! • Needs “sparse map of high-quality features” - A. Davison • Proposed approach • Use dense map(of low quality features) • Don’t update the map every frame : Keyframes • Split the tracking and mapping into two threads

  13. Parallel Tracking and Mapping • Proposed method - Split the tracking and mapping into two threads Time Thread #2 Mapping Update map One frame Thread #1 Tracking Find features Simple & easy Update camera pose only Draw graphics

  14. Parallel Tracking and Mapping Tracking thread: • Responsible estimation of camera pose and rendering augmented graphics • Must run at 30 Hz • Make as robust and accurate as possible Mapping thread: • Responsible for providing the map • Can take lots of time per key frame • Make as rich and accurate as possible

  15. Tracking thread • Overall flow Map Pre-process frame Project points Project points Measure points Measure points Update Camera Pose Update Camera Pose Coarse stage Fine stage Draw Graphics

  16. Pre-process frame • Make for pyramid levels 80x60 160x120 640x480 320x240

  17. Pre-process frame • Make for pyramid levels • Detect Fast corners • E. Rosten et al (ECCV 2006) 80x60 160x120 640x480 320x240

  18. Project Points • Use motion model to update camera pose • Constant velocity model Estimatedcurrent Pt+1 Previous posPt ∇t’ Previous pos Pt-1 ∇t Vt =(Pt – Pt-1)/∇t Pt+1=Pt+∇t’(Vt)

  19. Project Points • Choose subset to measure • ~ 50 biggest features for coarse stage • 1000 randomly selected for fine stage ~50 1000 80x60 160x120 640x480 320x240

  20. Measure Points • Generate 8x8 matching template (warped from source key-frame:map) • Search a fixed radius around projected position • Use Zero-mean SSD • Only search at Fast corner points

  21. Update caemra pose • 6-DOF problem • Obtain by SFM (Three-point algorithm) ?

  22. Dray graphics • What can we draw in an unknown scene? • Assume single plane visible at start • Run VR simulation on the plane

  23. Mapping thread • Overall flow Stereo Initialization Tracker Wait for new key frame Add new map points Optimize map Map maintenance

  24. Stereo Initialization • Use five-point-pose algorithm • D. Nister et. al. 2006 • Requires a pair of frames and feature correspondences • Provides initial map • User input required: • Two clicks for two key-frames • Smooth motion for feature correspondence

  25. Wait for new key frame • Key frames are only added if : • There is a sufficient baseline to the other key frame • Tracking quality is good • Key frame (4 level pyramid images and its corners) • When a key frame is added : • The mapping thread stops whatever it is doing • All points in the map are measured in the key frame • New map points are found and added to the map

  26. Add new map points • Want as many map points as possible • Check all maximal FAST corners in the key frame : • Check score • Check if already in map • Epipolar search in a neighboring key frame • Triangulate matches and add to map • Repeat in four image pyramid levels

  27. Optimize map • Use batch SFM method: Bundle Adjustment • Adjusts map point positions and key frame poses • Minimize reprojection error of all points in all keyframes (or use only last N key frames)

  28. Map maintenance • When camera is not exploring, mapping thread has idle time • Data association in bundle adjustment is reversible • Re-attempt outlier measurements • Try measure new map features in all old key frames

  29. Comparison to EKF-SLAM • More Accurate • More robust • Faster tracking < SLAM based AR Proposed AR

  30. System and Results • Environment • Desktop PC (Intel Core 2 Duo 2.66 GHz) • OS : Linux • Language : C++ • Tracking speed

  31. System and Results • Mapping scalability and speed • Practical limit • 150 key frames • 6000 points • Bundle adjustment timing

  32. Demonstration

  33. Remaining problem • Outlier management • Still brittle in some scenario • Repeated texture • Passive stereo initialization • Occlusion problem • Relocation problem

  34. Conclusion • Conclusion • Parallel tracking and mapping process are presented using multi-thread. • Contribution • Visual odometry system was well presented. • Overcome computation by multi-thread • Opinion • The proposed algorithm can be applied to our research • Navigation system • 3D tracking system

  35. Q & A

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