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Building Accurate Maps using Rao-Blackwellized Particle Filters Cyrill Stachniss University of Freiburg, Germany Thanks and partial slide courtesy of Mike Montemerlo and Dirk Haehnel Research Lab for Autonomous Intelligent Systems (cid:1) Headed by Prof. Dr. Wolfram Burgard (cid:1) 1 academic advisor & 1 post-doc (cid:1) 14 Ph.D. students Key Projects (cid:1) 1 SFB/TR-8 (cid:1) 3 European projects (cid:1) 1 DFG graduate school (cid:1) 1 BMBF project (cid:1) 3 projects funded by industry Fields of Research (cid:1) Mobile robots (cid:1) State and model estimation (cid:1) Adaptive techniques and learning (cid:1) Multi-robot coordination (cid:1) Decision-theoretic approaches (cid:1) Scene understanding (cid:1) Manipulation (cid:1) Autonomous cars (cid:1) Humanoid robots (cid:1) Flying vehicles (cid:1) … Probabilistic Robotics What is this Talk About? SLAM localization mapping integrated approaches active localization exploration path planning A Typical Robot… proximity senor wheels with encoders (provide odometry) Maps are Important for Robots (cid:1) Maps model the environment of the robot (cid:1) Localization is impossible without a map (“Where am I?”) (cid:1) Efficient motion planning requires maps (“How to reach a target location?”) (cid:1) Reasoning about the state of the world (cid:1) … Maps are important for efficiently solving standard robotic problems What is “SLAM” ? (cid:2) Estimate the pose and the map of a mobile robot at the same time poses map observations & movements Courtesy of Dirk Haehnel Mapping using Raw Odometry (cid:2) Why is SLAM hard? Chicken-or-egg problem: (cid:2) a map is needed to localize the robot and (cid:2) a pose estimate is needed to build a map Courtesy of Dirk Haehnel Particle Filters Who knows how a particle filter works ? Explain Particle Filters Skip Explanation Brief Introduction to Particle Filters What is a particle filter? (cid:2) It is a Bayes filter (cid:2) Particle filters are a way to efficiently represent non-Gaussian distribution Basic principle (cid:2) Set of state hypotheses (“particles”) (cid:2) Informally speaking: “survival-of-the-fittest”

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Localization is impossible without a map. (“Where am I?”) Mapping using Raw Odometry. ▫ Why is SLAM Particle filters are a way to efficiently represent.

Building Accurate Maps using Rao-Blackwellized Particle Filters PDF

89 Pages·2009·5.58 MB·English

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Localization is impossible without a map. (“Where am I?”) Mapping using Raw Odometry. ▫ Why is SLAM Particle filters are a way to efficiently represent.

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Author:	Unknown
Publication Year:	2009
Pages:	89
Language:	English
File Size:	5.58
Format:	PDF
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