ijcai distinguished 01

Information about ijcai distinguished 01

Published on September 27, 2007

Author: Yuan

Source: authorstream.com

Content

Probabilistic Algorithms for Mobile Robot Mapping:  Sebastian Thrun Carnegie Mellon & Stanford Wolfram Burgard University of Freiburg and Dieter Fox University of Washington Probabilistic Algorithms for Mobile Robot Mapping Slide2:  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, … Museum Tour-Guide Robots:  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 The Nursebot Initiative:  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 Mapping: The Problem:  Mapping: The Problem Concurrent Mapping and Localization (CML) Simultaneous Localization and Mapping (SLAM) Mapping: The Problem:  Mapping: The Problem Continuous variables High-dimensional (eg, 1,000,000+ dimensions) Multiple sources of noise Simulation not acceptable Milestone Approaches:  Milestone Approaches Mataric 1990 Kuipers et al 1991 Elfes/Moravec 1986 Lu/Milios/Gutmann 1997 3D Mapping:  3D Mapping Konolige et al, 2001 Teller et al, 2000 Moravec et al, 2000 Take-Home Message:  Take-Home Message Mapping is the holy grail in mobile robotics. Bayes Filters:  Bayes Filters Special cases: HMMs DBNs POMDPs Kalman filters Condensation ... x = state t = time z = measurement u = control  = constant Bayes Filters in Localization:  Bayes Filters in Localization [Simmons/Koenig 95] [Kaelbling et al 96] [Burgard, Fox, et al 96] Bayes Filters for Mapping:  Bayes Filters for Mapping s = robot pose m = map t = time  = constant z = measurement u = control Kalman Filters (SLAM):  Kalman Filters (SLAM) [Smith, Self, Cheeseman, 1990] Underwater Mapping with SLAM Courtesy of Hugh Durrant-Whyte, Univ of Sydney:  Underwater Mapping with SLAM Courtesy of Hugh Durrant-Whyte, Univ of Sydney Large-Scale SLAM Mapping Courtesy of John Leonard, MIT:  Large-Scale SLAM Mapping Courtesy of John Leonard, MIT SLAM: Limitations:  SLAM: Limitations Linear Scaling: O(N4) in number of features in map Can’t solve data association problem Unknown Data Association: EM:  Unknown Data Association: EM [Dempster et al, 77] [Thrun et al, 1998] [Shatkay/Kaelbling 1997] CMU’s Wean Hall (80 x 25 meters):  CMU’s Wean Hall (80 x 25 meters) EM Mapping, Example (width 45 m):  EM Mapping, Example (width 45 m) EM Mapping: Limitations:  EM Mapping: Limitations Local Minima Not Real-Time The Goal:  The Goal ? Real-Time Approximation (ICRA paper):  Real-Time Approximation (ICRA paper) Incremental ML: Not A Good Idea:  Incremental ML: Not A Good Idea path robot mismatch Real-Time Approximation:  Real-Time Approximation Our ICRA Paper  Real-Time Approximation:  Real-Time Approximation Yellow flashes: artificially distorted map (30 deg, 50 cm) Importance of Posterior Pose Estimate:  Importance of Posterior Pose Estimate Without pose posterior With pose posterior Online Mapping with Posterior Courtesy of Kurt Konolige, SRI, DARPA-TMR:  Online Mapping with Posterior Courtesy of Kurt Konolige, SRI, DARPA-TMR [Gutmann & Konolige, 00] Accuracy: “The Tech” Museum, San Jose:  CAD map Accuracy: “The Tech” Museum, San Jose 2D Map, learned Multi-Robot Mapping:  Multi-Robot Mapping Every module maximizes likelihood Pre-aligned scans are passed up in hierarchy map map map Cascaded architecture map map … … Aligned map Pre-aligned scans Multi-Robot Exploration:  Multi-Robot Exploration DARPA TMR Maryland 7/00 DARPA TMR Texas 7/99 (July. Texas. No air conditioning. Req to dress up. Rattlesnakes) 3D Volumetric Mapping:  3D Volumetric Mapping 3D Structure Mapping:  3D Structure Mapping 3D Texture Mapping:  3D Texture Mapping Fine-Grained Structure: Can We Do Better?:  Fine-Grained Structure: Can We Do Better? Multi-Planar 3D Mapping:  Multi-Planar 3D Mapping Idea: Exploit fact that buildings posses many planar surfaces Compact models High Accuracy Objects instead of pixels 3D Multi-Plane Mapping Problem:  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 Expected Log-Likelihood Function:  Expected Log-Likelihood Function [Liu et al, ICML-01] EM To The Rescue!:  EM To The Rescue! Results:  Results With EM (95% of data explained by 7 surfaces) Without EM With: Deepayan Chakrabarti, Rosemary Emery, Yufeng Liu, Wolfram Burgard, ICML-01 error The Obvious Next Step:  The Obvious Next Step  Underwater Mapping (with University of Sydney):  Underwater Mapping (with University of Sydney) With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding Take-Home Message:  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! Open Problems:  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) Open Problems, con’t:  Open Problems, con’t Mapping, localization Control/Planning under uncertainty Integration of symbolic making Human robot interaction Literature Pointers: “Robotic Mapping” at www.thrun.org “Probabilistic Robotics” AI Magazine 21(4) Slide52:  www.appliedautonomy.com

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