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Introduction to Mobile Robotics - SS 2010

Introduction to Mobile Robotics (engl.) - Autonomous Mobile Systems

This course will introduce basic concepts and techniques used within the field of mobile robotics. We analyze the fundamental challenges for autonomous intelligent systems and present the state of the art solutions. Among other topics, we will discuss:
  • Sensors,
  • Kinematics,
  • Path planning,
  • Vehicle localization,
  • Map building,
  • SLAM,
  • Exploration of unknown terrain


  • Lecturers: PD Dr. Cyrill Stachniss, Prof. Dr. Wolfram Burgard, Prof. Dr. Maren Bennewitz (Juniorprof.), Dr. Giorgio Grisetti, Dr. Kai Arras
  • Co-organizers: Maximilian Beinhofer, Henrik Kretzschmar, Lionel Ott
  • Lectures: Tuesday 11:15-13:00, Friday 11:15-12:00, Room: SR 00-034, Building 051
  • Lectures/Exercises: Friday 12:00-13:00, Room: SR 00-034, Building 051
  • Oral exams: 22.09.10 starting at 9:00 and 23.09.10 starting at 9:00. See the list of the Pruefungsamt for further details.

  • Exercises

    1. Exercise sheet 1 – Setup (PDF)
    2. Exercise sheet 2 – Linear Algebra (PDF) (revised)
    3. Exercise sheet 3 – Locomotion, Bayes Rule (PDF)
    4. Exercise sheet 4 – Bayes Filter, Motion Model (PDF)
    5. Exercise sheet 5 – Sensor Models (PDF)
    6. Exercise sheet 6 – Mapping with Known Poses (PDF)
    7. Exercise sheet 7 – Particle Filter (PDF, pf_framework)
    8. Exercise sheet 8 – Extended Kalman Filter (PDF, kf_framework)
    9. Exercise sheet 9 – Line Fitting (PDF, line fitting framework)
    10. Exercise sheet 10 – SLAM: Basics (PDF)
    11. Exercise sheet 11 – EKF, Rao-Blackwellization, Path Planning (PDF)
    12. Exercise sheet 12 – ICP (PDF, icp_framework)



    Slides

    1. Introduction PDF (20.04.2010)
    2. Robotics Labs in Freiburg (20.04.2010)
    3. Robot Control Paradigms PDF
    4. Linear Algebra PDF (23.04.2010, 27.04.2010)
    5. Wheeled Locomotion PDF (30.04.2010)
    6. Proximity Sensors PDF (04.05.2010)
    7. Probabilistic Robotics PDF revised (04.05.2010 until slide 13, 07.05.2010 until slide 32)
    8. Probabilistic Motion Models PDF
    9. Probabilistic Sensor Models PDF
    10. Mapping with Known Poses PDF
    11. Particle Filter PDF (08.06.2010)
    12. Kalman Filter PDF (11.06.2010)
    13. Error Propagation PDF (15.06.2010)
    14. LSQ Estimation, Geometric Feature Extraction PDF (15.06.2010)
    15. EKF Localization PDF (18.06.2010)
    16. SLAM: Simultaneous Localization and Mapping PDF (22.06.2010)
    17. SLAM: Landmark-based FastSLAM PDF (25.06.2010)
    18. SLAM: Grid-based FastSLAM PDF (29.06.2010)
    19. Path Planning PDF (02.07.2010)
    20. ICP: Iterative Closest Point Algorithm PDF, octave files (06.07.2010, 09.07.2010)
    21. Information Gain-Based Exploration PDF (16.07.2010)
    22. Summary PDF (20.07.2010, 23.07.2010)



    Additional Material

    1. Octave cheat sheet
    2. Basic Probabilities Rules PDF
    3. Explanation and derivation of the particle filters equations for mobile robot localization and for mapping with grid maps (PDF)



    Recordings

    There will be no recordings this year, but you may consult the 2009 recordings in case you miss a lecture.
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