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Introduction to Mobile Robotics - SS 2017

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:
  • Kinematics
  • Sensors
  • Vehicle localization
  • Map building
  • SLAM
  • Path planning
  • Exploration of unknown terrain


Lectures


MP4
Lecture Dates Topic Slides Recordings
00 26-04-2017 Introduction PDF MP4
01 26-04-2017
28-04-2017
Linear Algebra PDF MP4_ss16
MP4
02 03-05-2017 Robot Control Paradigms PDF MP4
03 03-05-2017
05-05-2017
Wheeled Locomotion PDF MP4
MP4
04 05-05-2017 Proximity Sensors PDF MP4
05 10-05-2017
12-05-2017
Probabilistic Robotics PDF MP4
MP4
06 17-05-2017
17-05-2017
19-05-2017
Probabilistic Motion Models PDF MP4_ss16
MP4_ss16
MP4_ss16
07 24-05-2017
26-05-2017
Probabilistic Sensor Models PDF MP4_ss16
08 26-05-2017 Bayes Filter - Discrete Filters PDF MP4
09 31-05-2017
31-05-2017
Bayes Filter - Particle Filter and MCL PDF MP4_ss16
MP4_ss16
10 02-06-2017 Bayes Filter - Kalman Filter PDF MP4_ss16
11 14-06-2017 Bayes Filter - Extended Kalman Filter PDF MP4
12 14-06-2017
16-06-2017
21-06-2017
21-06-2017
Grid Maps and Mapping With Known Poses PDF MP4
MP4
MP4
MP4
13 23-06-2017 SLAM - Simultaneous Localization and Mapping MP4

Exercises

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 on Fridays and will be discussed in class one week later. We strongly encourage you to solve the exercise sheets beforehand to benefit from the discussions in class.

Join this Google group forum for discussing questions on lectures and exercises or send a mail to mobilerobotics@informatik.uni-freiburg.de for an appointment with one of the teaching assistants.

Sheet Due date Topic Exercise Sheet Exercise Material Solutions
01 28-04-2017 Setup Python PDF func.py
02 05-05-2017 Linear Algebra PDF laserscan.dat PDF
03 12-05-2017 Locomotion, Differential Drive PDF PDF
04 19-05-2017 Bayes Rule PDF PDF
05 24-05-2017 Sampling, Motion Models PDF PDF
06 02-06-2017 Sensor Models PDF PDF
07 16-06-2017 Discrete Filter, Particle Filter PDF pf_framework.tar.gz PDF
08 23-06-2017 Extended Kalman Filter PDF kf_framework.tar.gz PDF
09 30-06-2017 Mapping with Known Poses PDF


Additional Material

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