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Introduction to Mobile Robotics - SS 2021

Course content (engl.)

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:
  • Kinematics
  • Sensors
  • Vehicle localization
  • Map building
  • SLAM
  • Path planning

Organization

  • Lecturer: Prof. Dr. Wolfram Burgard,
  • Co-organizers: Dr. Daniel Büscher, Dr. Lukas Luft Shengchao Yan Johannes Meyer
  • Introduction session: There will be an intro lecture on Tuesday, April 20, 13:00 via Zoom.
  • The Q&A-sessions take place Tuesdays at 13:00 via Zoom (the first one on April 27).
  • The exercises take place Thursdays at 13:00 via Zoom (the first one on April 22).
  • Zoom link: here, meeting ID: 686 2914 8672, the passcode is announced in ILIAS.
  • All quiz questions can be found here, same password.
  • No on-site lectures will be provided this term. Please work over the lecture recordings (see below) in your own time.
  • For questions and discussions: ILIAS Forum. For access you might need to register to the ILIAS Course first.
  • Exam: The exam is WRITTEN for most students, except for bachelor students of computer science with Prüfungsordnung 2012.
    1. WRITTEN: Thu 23-09-2021, 13:00 - 16:00. Estimated start: 14:00, duration: 90 minutes.
    2. ORAL: September 20 and 24, 2021.

Lectures

Lecture Dates Topic Slides Recordings
00 20-04-2021 Introduction PDF MP4
01 27-04-2021 Linear Algebra PDF MP4
MP4
02 04-05-2021 Robot Control Paradigms PDF MP4
03 04-05-2021 Wheeled Locomotion PDF MP4
MP4
04 04-05-2021 Proximity Sensors PDF MP4
05 11-05-2021 Probabilistic Robotics PDF MP4
MP4
MP4
06 18-05-2021 Probabilistic Motion Models PDF MP4
MP4
07 25-05-2021 Probabilistic Sensor Models PDF MP4
MP4
08 08-06-2021 Bayes Filter - Discrete Filters PDF MP4
09 08-06-2021 Bayes Filter - Particle Filter and MCL PDF MP4
MP4
10 15-06-2021 Bayes Filter - Kalman Filter PDF MP4
11 15-06-2021 Bayes Filter - Extended Kalman Filter PDF MP4
12 22-06-2021 Grid Maps and Mapping With Known Poses PDF MP4
MP4
13 29-06-2021 SLAM - Simultaneous Localization and Mapping PDF MP4
14 29-06-2021 SLAM - Landmark-based FastSLAM PDF MP4
15 29-06-2021 SLAM - Grid-based FastSLAM PDF MP4
MP4
16 29-06-2021 SLAM - Graph-based SLAM PDF MP4
MP4
17 06-07-2021 Techniques for 3D Mapping PDF MP4
18 06-07-2021 Iterative Closest Point Algorithm PDF MP4
19 13-07-2021 Path and Motion Planning PDF MP4
MP4
MP4
20 20-07-2021 Multi-Robot Exploration PDF MP4
MP4
21 20-07-2021 Information Driven Exploration PDF MP4
22 20-07-2021 Summary PDF MP4

Exercises

Each exercise session consists of two parts: a short recap of the lecture and the discussion of the exercise sheets.

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

Exercise sheets will be published about one week before the discussion session. We strongly encourage you to solve the exercise sheets beforehand to benefit from the discussions in class.

Send a mail to mobilerobotics@informatik.uni-freiburg.de for an appointment with one of the teaching assistants.

Sheet Discussion Topic Exercise Sheet Exercise Material Solutions
00 22-04-2021 (Thu) Setup Python PDF myfirstscript.py
01 29-04-2021 (Thu) Linear Algebra PDF laserscan.dat PDF
02 06-05-2021 (Thu) Locomotion, Differential Drive PDF PDF
03 20-05-2021 (Thu) Bayes Rule PDF PDF
04 01-06-2021 (Tue) Sampling, Motion Models PDF PDF
05 10-06-2021 (Thu) Sensor Models PDF PDF
06 17-06-2021 (Thu) Discrete Filter, Particle Filter PDF pf_framework.tar.gz PDF (alternate solution for q1
07 24-06-2021 (Thu) Extended Kalman Filter PDF kf_framework.tar.gz PDF
08 01-07-2021 (Thu) Mapping with Known Poses PDF PDF
09 08-07-2021 (Thu) FastSLAM PDF fastSLAM_framework.tar.gz
fastSLAM_algorithm.pdf
PDF
10 15-07-2021 (Thu) Iterative Closest Point Algorithm PDF icp_framework.tar.gz PDF
11 22-07-2021 (Thu) Path Planning PDF planning_framework.tar.gz PDF

Additional Material

  • Notes on one dimensional Gaussians (PDF)
  • Notes on multi dimensional Gaussians (PDF)
  • Python cheat sheet (PDF)
  • Matrix cookbook (PDF)
Benutzerspezifische Werkzeuge