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Probabilistic Robotics Overview

Probabilistic Robotics Overview. BSc course Kunstmatige Intelligentie 2009 http://www.science.uva.nl/~arnoud/education/ProbabilisticRobotics/ Arnoud Visser Intelligent Systems Lab Amsterdam Informatics Institute Universiteit van Amsterdam A.Visser@uva.nl. Probabilistic Robotics.

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Probabilistic Robotics Overview

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  1. Probabilistic RoboticsOverview BSc course Kunstmatige Intelligentie 2009 http://www.science.uva.nl/~arnoud/education/ProbabilisticRobotics/ Arnoud Visser Intelligent Systems Lab Amsterdam Informatics Institute Universiteit van Amsterdam A.Visser@uva.nl

  2. Probabilistic Robotics Probabilistic robotics is a subfield of robotics concerned with the on the algorithms to couple the perception and control part. It relies on statistical techniques for representing information and making decisions. By doing so, it accommodates the uncertainty that arises in most contemporary robotics applications. Probabilistic Robotics Course at the Universiteit van Amsterdam

  3. Structure of the course • Studio Class Room (hoor-, werkcolleges & prakticum ) on Monday • Studio Class Room (hoor-, werkcolleges & prakticum) on Wednesday • Studio Class Room (zelfstudie) on Friday

  4. Goals for the Course • Insight in the mathematical foundation of the techniques and algorithms applied in the field • Experience with the derivation of models from clear problem descriptions • Practical experience with applying the techniques to “real robots” in a virtual world

  5. Literature • Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, The MIT Press, 2005. • http://www.probabilistic-robotics.org/

  6. Grading • 1/2 exam grades, 1/2 assignments grade • Exam grade: 1/2 midterm exam, 2/2 final exam • Exams will be “open-book” Probabilistic Robotics Course at the Universiteit van Amsterdam

  7. Some practical issues • Try to keep up with reading the chapters • Ask questions whenever something in the lectureor the book is not clear to you • Slides will become available online: http://staff.science.uva.nl/~arnoud/education/ProbabilisticRobotics Probabilistic Robotics Course at the Universiteit van Amsterdam

  8. Assignments • Exercises from the book • Matlab-exercises • UsarSim-exercises Probabilistic Robotics Course at the Universiteit van Amsterdam

  9. Topics covered in the course • Robot Motion and Perception • Localization • Mapping • Exploration Probabilistic Robotics Course at the Universiteit van Amsterdam

  10. The Book • Part I: The Basics • Introduction • State Estimation & Recursive Filters • Robot Motion • Robot Perception • Part II: Localization • Markov and Gaussian • Grid And Monte Carlo • Part III: Mapping • Occupancy Grid Mapping • Simultaneous Localization and Mapping • Advanced SLAM algorithms • Part IV: Planning and Control • Approximate POMDP Techniques • Exploration Probabilistic Robotics Course at the Universiteit van Amsterdam

  11. Sebastian Thrun • Director of the Stanford AI Lab • Winner of the DARPA Grand Challenge 2005 • Primary author of CARMEN – The Robot Navigation Toolkit • Builder of the interactive museum tour-guide robot Rhino - Minerva Probabilistic Robotics Course at the Universiteit van Amsterdam

  12. Wolfram Burgard • Head of the research lab for Autonomous Intelligent Systems at the Universität Freiburg • Supervisor of Sebastian Thrun • Initiator of the interactive museum tour-guide robot Rhino / Minerva • Advisor in the NurseBot project Probabilistic Robotics Course at the Universiteit van Amsterdam

  13. Dieter Fox • Director of the Robotics and State Estimation Lab at the University of Washington • Student of Sebastian Thrun • Programmer of the interactive museum tour-guide robot Rhino / Minerva • RoboCup Aibo League veteran Probabilistic Robotics Course at the Universiteit van Amsterdam

  14. Common background: Museum Tour-guides Minerva, Washington, 1998 Rhino, Bonn, 1997 Probabilistic Robotics Course at the Universiteit van Amsterdam

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