1 / 39

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

ryu
Télécharger la présentation

Registration for Robotics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related