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

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• Lecturers: Prof. Dr. Wolfram Burgard, Dr. Cyrill Stachniss, Prof. Dr. Maren Bennewitz (Juniorprof.) Dr. Kai Arras, Dr. Giorgio Grisetti
• Co-organizers: Daniel Meyer-Delius, Boris Lau
• Lectures: Tuesday 11:15-13:00, Friday 11:15-12:00, Room: SR 01-016, Building 101
• Lectures/Exercises: Friday 12:00-13:00, Room: SR 01-016, Building 101
• !!! EXAM: THE SCHEDULE FOR THURSDAY THE 17th OF SEPTEMBER HAS BEED CHANGED. PLEASE CHECK THE NEW SCHEDULE!!! (HERE)
• Exam: The exam will be oral and will take place the 9th, 14th and 17th of September.

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Exercises

Results will be discussed next Friday. FAQ (frequently asked questions) for the exercises/lab assignments.
1. Exercise sheet 1 – Setup (PDF)
2. Exercise sheet 2 – Locomotion, Bayes (PDF)
3. Exercise sheet 3 – State Estimation (UPDATED!!!) (PDF)
4. Exercise sheet 4 – Motion and Sensor Models (PDF, log1.dat.gz, log2.dat.gz)
5. Exercise sheet 5 – Particle Filter and Monte Carlo Localization (PDF)
6. Exercise sheet 6 – Particle Filter and Monte Carlo Localization, Kalman Filter (PDF)
7. Exercise sheet 7 – Kalman Filter (PDF, log3.log.gz)
8. Exercise sheet 8 – Error Propagation, EKF, Landmarks (PDF, Octave code, UPDATED Log-file)
9. Exercise sheet 9 – Mapping with Known Poses, Landmark-based SLAM (PDF, Octave code, Log-file, Octave code for drawing a covariance ellipse)
10. Exercise sheet 10 – EKF SLAM, FastSLAM, Data Association (PDF)
11. Exercise sheet 11 – Path Planning (PDF, Octave)
12. Exercise sheet 12 – ICP / SVD (PDF, Octave)
13. Exercise sheet 13 – Multi-Robot Exploration / Entropy and Information Gain (PDF)




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Slides

1. Introduction PDF
2. Locomotion PDF
3. Sensors PDF
4. Probabilities and Bayes PDF
5. Probabilistic Motion Models PDF
6. Probabilistic Sensor Models PDF
7. Bayes Filter - Discrete Filters PDF
8. Bayes Filter - Particle Filter and Monte Carlo Localization PDF
9. Bayes Filter - Kalman Filter PDF
10. Bayes Filter - Extended Kalman Filter PDF
11. Error Propagation PDF
12. Error Propagation (UPDATED!!!) PDF
13. LSQ Estimation and Feature Extraction PDF
14. Mapping with Known Poses PDF
15. SLAM: Simultaneous Localization and Mapping PDF
16. SLAM: Simultaneous Localization and Mapping (UPDATED!!!) PDF
17. SLAM: Grid-based FastSLAM PDF
18. Path Planning and Collision Avoidance PDF
19. Iterative Closest Point Algorithm PDF
20. Elevation Maps PDF
21. Multi-Robot Exploration PDF
22. Information Gain-based Exploration PDF
23. Summary PDF




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Recordings

1. Wheeled Locomotion (1) (24.04.09)
2. Wheeled Locomotion (2) (28.04.09)
3. Proximity Sensors (28.04.09)
4. Probabilistic Robotics (1) (28.04.09). See the Bayes' rule example with corrected numbers here.
5. Probabilistic Robotics (2) (05.05.09)
6. Probabilistic Robotics (3) (08.05.09)
7. Probabilistic Motion Models (1) (08.05.09)
8. Probabilistic Motion Models (2) (12.05.09)
9. Probabilistic Sensor Models (1) (12.05.09)
10. Probabilistic Sensor Models (2) (15.05.09)
11. Probabilistic Sensor Models (3) (19.05.09)
12. Bayes Filter - Discrete Filters (19.05.09)
13. Bayes Filter - Particle Filter and Monte Carlo Localization (1) (22.05.09)
14. Bayes Filter - Particle Filter and Monte Carlo Localization (2) (26.05.09)
15. Bayes Filter - Kalman Filter (1) (26.05.09)
16. Bayes Filter - Kalman Filter (2) (29.05.09)
17. Bayes Filter - Extended Kalman Filter (9.06.09)
18. Error Propagation (1) (9.06.09)
19. Error Propagation (2) / LSQ Estimation and Feature Extraction (12.06.09)
20. Mapping with Known Poses (1) (16.06.09)
21. Mapping with Known Poses (2) (16.06.09)
22. SLAM: Simultaneous Localization and Mapping (1) (19.06.09)
23. SLAM: Simultaneous Localization and Mapping (2) (23.06.09)
24. SLAM: Simultaneous Localization and Mapping (3) (23.06.09)
25. SLAM: Grid-based FastSLAM (1) (26.06.09)
26. SLAM: Grid-based FastSLAM (2) [part 1, part 2] (30.06.09)
27. Path Planning and Collision Avoidance (1) (30.06.09)
28. Path Planning and Collision Avoidance (2) (03.07.09)
29. Iterative Closest Point Algorithm (10.07.09)
30. Elevation Maps (14.07.09)
31. Multi-Robot Exploration (1) (14.07.09)
32. Multi-Robot Exploration (2) (14.07.09)
33. Information Gain-based Exploration (1) (14.07.09)
34. Information Gain-based Exploration (2) (17.07.09)
35. Summary (1) (21.07.09)
36. Summary (2) (24.07.09)




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Additional Material

1. Bayes' Rule Example PDF
2. Basic Probabilities Rules PDF