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

Solving and submitting the exercise sheets is recommended but not mandatory to be admitted to the final exam. There are no bonus points.

The exercises should be solved in groups of two students. Submit a single tar/zip file with all codes, scripts and a single pdf with all answers (programs and figures). In general, assignments will be published on Tuesday and have to be submitted the following Monday before class. Submit programming exercises via email to mobilerobotics@informatik.uni-freiburg.de.

Join this forum for discussing questions on exercises and lectures
https://groups.google.com/forum/?fromgroups#!forum/ais_introtorobotics13

  • Exercise sheet 1 – Setup (Octave Tutorial) (PDF), Octave cheat sheet
  • Exercise sheet 2 – Linear Algebra, Locomotion, and Sensing (PDF, laserscan.dat)
  • Exercise sheet 3 – Locomotion, Bayes Rule, Bayes Filter (PDF)
  • Exercise sheet 4 – Sampling, Motion Model, Bayes Filter (PDF)
  • Exercise sheet 5 – Mapping with Known Poses (PDF)
  • Exercise sheet 6 – Extended Kalman Filter (PDF, ekf_framework)
  • Exercise sheet 7 – Velocity Motion Model, Particle Filter (PDF, files)
  • Exercise sheet 8 – Particle Filter (PDF, pf_framework)
  • Exercise sheet 9 – SLAM Basics (PDF)



Slides

  • Linear Algebra PDF
  • Robot Control Paradigms PDF
  • Wheeled Locomotion PDF
  • Sensors PDF
  • Probabilities and Bayes PDF
  • Probabilistic Motion Models PDF
  • Probabilistic Sensor Models PDF
  • Mapping with Known Poses PDF
  • Kalman Filter PDF
  • Extended Kalman Filter PDF
  • Discrete Filters PDF
  • Particle Filter, MCL PDF
  • SLAM: Simultaneous Localization and Mapping PDF
  • SLAM: Landmark-based FastSLAM PDF
  • SLAM: Grid-based FastSLAM PDF
  • SLAM: Graph-based SLAM PDF
  • Techniques for 3D Mapping PDF
  • Iterative Closest Points Algorithm PDF
  • Path Planning and Collision Avoidance PDF
  • Multi-Robot Exploration PDF
  • Information-Driven Exploration PDF
  • Summary PDF



Recordings

  1. Introduction
  2. Linear Algebra
  3. Paradigms and Wheeled Locomotion Part 1
  4. Wheeled Locomotion Part 2 and Sensors
  5. Probabilities and Bayes - Part 1
  6. Probabilities and Bayes - Part 2
  7. Motion Models - Part 1
  8. Motion Models - Part 2
  9. Sensor Models
  10. Mapping with Known Poses - Part 1
  11. Mapping with Known Poses - Part 2 and Kalman Filter - Part 1
  12. Kalman Filter - Part 2
  13. Extended Kalman Filter - Part 1
  14. Extended Kalman Filter - Part 2
  15. Discrete Filters and Particle Filter, MCL - Part 1
  16. Particle Filter - Part 2
  17. SLAM/EKF SLAM - Part 1
  18. SLAM/EKF SLAM - Part 2
  19. Landmark-Based FastSLAM and Grid-Based FastSLAM - Part 1
  20. Grid-Based FastSLAM - Part 2



Additional Material

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