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Registration for Robotics

Registration for Robotics. Patrick Mihelich JD Chen James Bowman Helen Oleynikova Freiburg TORO group: Giorgio Grisetti Cyrill Stachness Rainer Kummerle. Kurt Konolige Willow Garage Stanford University. Outline. Applications Overview of Registration

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Registration for Robotics

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  1. Registration for Robotics Patrick Mihelich JD Chen James Bowman Helen Oleynikova Freiburg TORO group: Giorgio Grisetti CyrillStachness Rainer Kummerle Kurt Konolige Willow Garage Stanford University

  2. Outline • Applications • Overview of Registration • Front end and image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  3. Applications of Registration • Photo tourism [Snavely, Seitz, Szeliski 2006]

  4. Applications of Registration • Object Modeling [Lovi et al. 2010] • [Newcombe and Davison 2010]

  5. Applications of Registration • Face Tracking [WATSON: Morency 2003]

  6. Applications of Registration • Visual Odometry [Willow Garage]

  7. Applications of Registration • Visual SLAM [Willow Garage]

  8. Outline • Applications • Overview of Registration • Front end, image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  9. Registration Elements Incremental Global

  10. Mapping Systems MonoSLAM[Davison 2003] Parallel Tracking and Mapping [Klein and Murray 2007] View-based Maps [Konolige et al. 2009]

  11. Map Representations Covariance Matrix over points p0 p1 p2 … p0 p1 p2 … Extended Kalman Filter update

  12. MonoSLAM

  13. Map Representations Information matrix over views and world points c0 c1 c2 … p0 p1 p2 … c0c1c2… p0 p1 p2 … Nonlinear least squares update HDx = -g

  14. Parallel Tracking and Mapping

  15. Map Representations Information matrix over views c0 c1 c2 … c0c1c2… Nonlinear least squares update HDx = -g

  16. View-based Maps

  17. Comparison of Mapping Methods

  18. Outline • Applications • Overview of Registration • Front end and image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  19. Interest points • Adaptive threshold • Gridding for spatial diversity • FAST [Rosten and Drummond 2006] • Harris • SIFT [Lowe 1999] • SUSAN, CenSure, MSER, …

  20. Feature matching • Tracking with motion model • Windowed brute-force • KD tree nearest-neighbor • SIFT / SURF features • Planar patches • Random-tree signatures [Calonder et al. 2009] • … 930 features 354 matched 183 inliers

  21. Outlier detection and pose estimation • RANSAC to estimate pose • 3 points for stereo (3D-3D) • 3-point PnP for monocular (2D-3D) • Outlier rejection • Refinement • Least-squares • Reprojectionerror • Scavenging 930 features 354 matched 183 inliers

  22. Outline • Applications • Overview of Registration • Front end and image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  23. Visual Odometry – Sliding Window • 2-view pose estimates are unreliable • Triangulation is narrow-baseline • Multi-view estimates are more accurate • Triangulation is wide-baseline • More points, wider baseline => more accuracy • Keyframes • Many close-together views don’t add much • Too far apart leads to low inlier count • Sliding window of views • Track points as long as possible • ~20 – 40 keyframes in window

  24. Visual Odometry – Bundle Adjustment Measurement model: For Gaussian distributions ( is covariance) [Gauss-Newton / Normal] H Sparseness of H c0 c1 c2 … p0 p1 p2 … c0c1c2… p0 p1 p2 … 20 camera views 5000 features H is (20x6 + 5000x3)2

  25. Visual Odometry examples[courtesy Andrew Comport, INRIA] • Outdoor sequence in Versailles • 1 m stereo baseline, narrow FOV • ~400 m sequence • Average frame distance: 0.6 m • Max frame distance: 1.1 m

  26. Visual Odometry examples[courtesy Andrew Comport, INRIA] • Indoor Willow Garage sequence • 10 cm stereo, wide FOV • ~100 m sequence • Average frame distance: 0.3 m

  27. Outline • Applications • Overview of Registration • Front end and image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  28. Place Recognition test • K-nearest neighbor feature matching across database of images • KD tree [Lowe 1999, Eade and Drummond 2008, Williams et al. 2007] • Bag-of-words • visual vocabulary [Stewenius and Nister 2006, Cummins and Newman 2008] DB test DB

  29. Place Recognition: Vocabulary Trees[Nister and Stewenius CVPR06] • “Bag of words” retrieval • Vocab tree created offline • For recognition: • Image keypoints extracted • Tree encodes approximate NN search • Inverted index of images at leaves [Cummins and Newman ICRA07 Cullmer et al. ACRA08 Fraundorfer et al. IROS07] [Image from Nister and Stewenius CVPR06]

  30. Place Recognition: Vocabulary Trees Performance on Indoor dataset

  31. View-based Maps [Konolige et al. 2009] Performance on Indoor dataset

  32. View-based Maps [Konolige et al. 2009] Performance on Indoor dataset

  33. Trajectory synthesis

  34. Outline • Applications • Overview of Registration • Front end and image matching • Visual Odometry • Place recognition • Global SBA • Extensions

  35. Comparison of Mapping Methods

  36. Fast SBA[Konolige BMVC 2010, Jeong et al. CVPR 2010] • Take advantage of sparse secondary structure of Hessian • Use fast linear solvers • Davis’ CHOLMOD • Block preconditioned conjugate gradient

  37. Fast SBA in VSLAM

  38. Fast SBA in Reconstruction

  39. Open-Source in ROS • sbapackage for fast SBA • frame_common and posestfor front-end and pose estimation • vocabulary_treefor place recognition • vslam_systemfor Visual Odometry and VSLAM

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