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Introduction to Mobile Robotics - SS 2007

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




Exercises

Note: there is a FAQ (frequently asked questions) for the exercises/lab assignments.
  1. Exercise week 1 - Parsing .log-files and visualization of poses and laser scans (ZIP, PDF)
  2. Exercise week 2 - Differential Drive, Conditional Probabilities (PDF)
  3. Exercise week 3 - Odometry Data vs. True Trajectory (ZIP, PDF) corrected 7.5.07!
  4. Exercise week 4 - Probabilistic Motion and Sensor Models (PDF) corrected 16.5.07, see FAQ!
  5. Exercise week 5 - Extended Kalman Filter I (PDF)
  6. Exercise week 6 - Extended Kalman Filter II (ZIP, PDF) corrected 14.06.07!
  7. Exercise week 7 - Extended Kalman Filter III (PDF)
  8. Exercise week 8 - Landmark Mapping With Known Poses (PDF, ZIP)
  9. Exercise week 9 - Grip Mapping With Known Poses (PDF) updated 25.6.07 (minor change in formulation)
  10. Exercise week 10 - Monte Carlo Localization I (PDF, ZIP) updated 3.7.07
  11. Exercise week 11 - Monte Carlo Localization II (PDF, ZIP) updated 10.07.07 (removed old 2a)
  12. Exercise week 12 - Monte Carlo Localization III (PDF, ZIP)
Programming assignments have to be submitted electronically. Written assignments (such as proofs, calculations, etc) have to be handed in paper form at the class on fridays.



Slides

  1. Introduction (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  2. Locomotion (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  3. Sensors (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  4. Bayes Filters (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  5. Probabilistic Motion Models (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  6. Probabilistic Sensor Models (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  7. Bayes Filter Implementations (Particle Filters) [UPDATED PDF SLIDES] (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  8. Bayes Filter Implementations (Discrete Filters) (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  9. Bayes Filter Implementations (Gaussian Filters) (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  10. Mapping with Known Poses [UPDATED PDF SLIDES] (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  11. Landmark-based FastSLAM (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  12. Grid-based FastSLAM (PDF, PDF: 4 on 1)
  13. EKF-based SLAM (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  14. Iterative Closest Point Algorithm (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  15. Path Planning and Collision Avoidance (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  16. Coordinated Multi-Robot Exploration (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  17. Improved Multi-Robot Exploration (PDF)
  18. Information Gain-based Exploration Using Rao-Blackwellized Particle Filters (PS, PDF, PS: 4 on 1, PDF: 4 on 1)
  19. Architectures (PS, PDF, PS: 4 on 1, PDF: 4 on 1)



Additional Material

  1. Explanation and derivation of the particle filters equations for mobile robot localization and for mapping with grid maps (PDF)



Recordings (new filenames!)

  1. Locomotion (24.04.07)
  2. Bayes (1) (24.04.07)
  3. Bayes (2) (27.04.07)
  4. Bayes (3) (30.04.07)
  5. Probabilistic Motion Models (1) (30.04.07)
  6. Probabilistic Motion Models (2) (04.05.07)
  7. Sensor Models (1) (07.05.07)
  8. Sensor Models (2) (11.05.07)
  9. Bayes Filter Implementations: Gaussian Filters (1) (11.05.07)
  10. Bayes Filter Implementations: Gaussian Filters (2) (14.05.07)
  11. Bayes Filter Implementations: Gaussian Filters (3) (14.05.07)
  12. Bayes Filter Implementations: Gaussian Filters (4) (18.05.07)
  13. Bayes Filter Implementations: Gaussian Filters (5) (18.05.07)
  14. Bayes Filter Implementations: Gaussian Filters (6) (18.05.07)
  15. Bayes Filter Implementations: Discrete Filters (1) (21.05.07)
  16. Bayes Filter Implementations: Discrete Filters (2) (21.05.07)
  17. Bayes Filter Implementations: Particle Filters (1) (21.05.07)
  18. Bayes Filter Implementations: Particle Filters (2) (25.05.07)
  19. Mapping with Known Poses (1) (04.06.07)
  20. Mapping with Known Poses (2) (04.06.07)
  21. SLAM Mapping (1) (08.06.07)
  22. Landmark-Based FastSLAM (1) (11.06.07)
  23. Landmark-Based FastSLAM (2) (11.06.07)
  24. Landmark-Based FastSLAM (3) (11.06.07)
  25. Landmark-Based FastSLAM (4) (15.06.07)
  26. Path Planning (1) (18.06.07)
  27. Path Planning (2) (18.06.07)
  28. Multi Robot Exploration (1) (22.06.07)
  29. Supervised Learning of Places from Range Data using AdaBoost (1) (25.06.07)
  30. ... (29.06.07)
  31. 3D-Mapping (02.07.07)
  32. ... (06.07.07)
  33. Line and Circle Extraction, First-Order Error Propagation (09.07.07)
  34. Improved Multi Robot Exploration (1) (13.07.07)
  35. Information-Gain based Exploration (17.07.07)
  36. ... (17.07.07)
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