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Probabilistic Algorithms for Mobile Robot Mapping

Probabilistic Algorithms for Mobile Robot Mapping. Sebastian Thrun Carnegie Mellon & Stanford Wolfram Burgard University of Freiburg and Dieter Fox University of Washington. Based on the paper A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping

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Probabilistic Algorithms for Mobile Robot Mapping

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  1. Probabilistic Algorithms forMobile Robot Mapping Sebastian Thrun Carnegie Mellon & Stanford Wolfram Burgard University of Freiburg and Dieter Fox University of Washington

  2. Based on the paper A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping Best paper award at 2000 IEEE International Conference on Robotics and Automation (~1,100 submissions) Sponsored by DARPA (TMR-J.Blitch, MARS-D.Gage, MICA-S.Heise) and NSF (ITR(2), CAREER-E.Glinert, IIS-V.Lumelsky) Other contributors: Yufeng Liu, Rosemary Emery, Deepayan Charkrabarti, Frank Dellaert, Michael Montemerlo, Reid Simmons, Hugh Durrant-Whyte, Somajyoti Majnuder, Nick Roy, Joelle Pineau, …

  3. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  4. Museum Tour-Guide Robots With: Greg Armstrong, Michael Beetz, Maren Benewitz, Wolfram Burgard, Armin Cremers, Frank Dellaert, Dieter Fox, Dirk Haenel, Chuck Rosenberg, Nicholas Roy, Jamie Schulte, Dirk Schulz

  5. The Nursebot Initiative With: Greg Armstrong, Greg Baltus, Jacqueline Dunbar-Jacob, Jennifer Goetz, Sara Kiesler, Judith Matthews, Colleen McCarthy, Michael Montemerlo, Joelle Pineau, Martha Pollack, Nicholas Roy, Jamie Schulte

  6. Mapping: The Problem • Concurrent Mapping and Localization (CML) • Simultaneous Localization and Mapping (SLAM)

  7. Mapping: The Problem • Continuous variables • High-dimensional (eg, 1,000,000+ dimensions) • Multiple sources of noise • Simulation not acceptable

  8. Milestone Approaches Mataric 1990 Elfes/Moravec 1986 Kuipers et al 1991 Lu/Milios/Gutmann 1997

  9. 3D Mapping Moravec et al, 2000 Konolige et al, 2001 Teller et al, 2000

  10. Every state-of-the-art mapping algorithm is probabilistic. Take-Home Message Mapping is the holy grail in mobile robotics.

  11. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  12. Bayes Filters x = state t = time z = measurement u = control  = constant Special cases: HMMs DBNs POMDPs Kalman filters Condensation ...

  13. Bayes Filters in Localization [Simmons/Koenig 95] [Kaelbling et al 96] [Burgard, Fox, et al 96]

  14. s = robot pose m = map t = time  = constant z = measurement u = control Localization: Mapping? Bayes Filters for Mapping

  15. Kalman Filters (SLAM) [Smith, Self, Cheeseman, 1990]

  16. Underwater Mapping with SLAMCourtesy of Hugh Durrant-Whyte, Univ of Sydney

  17. Large-Scale SLAM MappingCourtesy of John Leonard, MIT

  18. SLAM: Limitations • Linear • Scaling: O(N4) in number of features in map • Can’t solve data association problem

  19. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  20. E-Step: Localization M-Step: Mapping with known poses Unknown Data Association: EM [Dempster et al, 77] [Thrun et al, 1998] [Shatkay/Kaelbling 1997]

  21. 16 landmarks 15 landmarks 27 landmarks 17 landmarks CMU’s Wean Hall (80 x 25 meters)

  22. EM Mapping, Example (width 45 m)

  23. EM Mapping: Limitations • Local Minima • Not Real-Time

  24. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  25. The Goal Kalman filters: real-time No data association EM: data association Not real-time ?

  26. + Incremental ML Real-Time Approximation (ICRA paper)

  27. Incremental ML: Not A Good Idea mismatch path robot

  28. + Real-Time Approximation Our ICRA Paper 

  29. Real-Time Approximation Yellow flashes: artificially distorted map (30 deg, 50 cm)

  30. Importance of Posterior Pose Estimate With pose posterior Without pose posterior

  31. Online Mapping with PosteriorCourtesy of Kurt Konolige, SRI, DARPA-TMR [Gutmann & Konolige, 00]

  32. Accuracy: “The Tech” Museum, San Jose 2D Map, learned CAD map

  33. Multi-Robot Mapping Cascaded architecture • Every module maximizes likelihood • Pre-aligned scans are passed up in hierarchy map … … map Pre-aligned scans Aligned map map map map

  34. Multi-Robot Exploration DARPA TMR Texas 7/99 (July. Texas. No air conditioning. Req to dress up. Rattlesnakes) DARPA TMR Maryland 7/00

  35. 3D Volumetric Mapping

  36. 3D Structure Mapping

  37. 3D Texture Mapping

  38. Fine-Grained Structure:Can We Do Better?

  39. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  40. Multi-Planar 3D Mapping Idea: Exploit fact that buildings posses many planar surfaces • Compact models • High Accuracy • Objects instead of pixels

  41. 3D Multi-Plane Mapping Problem Entails five problems • Generative model with priors: Not all of the world is planar • Parameter estimation: Location and angle of planar surfaces unknown • Outlier identification: Not all measurements correspond to planar surfaces (other objects, noise) • Correspondence: Different measurements correspond to different planar surfaces • Model selection: Number of planar surfaces unknown

  42. Expected Log-Likelihood Function [Liu et al, ICML-01]

  43. * * * * * * EM To The Rescue! Game Over!

  44. Results With EM (95% of data explained by 7 surfaces) Without EM error With: Deepayan Chakrabarti, Rosemary Emery, Yufeng Liu, Wolfram Burgard, ICML-01

  45. The Obvious Next Step EM for concurrent localization EM for object mapping 

  46. Underwater Mapping (with University of Sydney) With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding

  47. This Talk Motivation SLAM (Kalman filters) Expectation Maximization Real Time Hybrid 3D Mapping with EM Open Problems

  48. Take-Home Message Mapping is the holy grail in mobile robotics. Every state-of-the-art mapping algorithm is probabilistic. Sebastian has one cool animation!

  49. Open Problems • 2D Indoor mapping and exploration • 3D mapping (real-time, multi-robot) • Object mapping (desks, chairs, doors, …) • Outdoors, underwater, planetary • Dynamic environments (people, retail stores) • Full posterior with data association (real-time, optimal)

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