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Introduction to Mobile Robotics - SS 2019

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

  • Lecturer: Prof. Dr. Wolfram Burgard, Dr. Michael Tangermann, Dr. Daniel Büscher, Lukas Luft
  • Co-organizers: Marina Kollmitz, Iman Nematollahi,
  • Lecture: Wed 14.00-16.00 / Fri 16.00-17.00, Building: 101, Room: HS 00-036 (Schicksaal)
  • Exercises: Fri 17.00-18.00, Building: 101, Room: SR 00-010/014
  • Q&A session: Wed, August 28, 14.00-16.00, Building: 101, Room: HS 00-036 (Schicksaal)
  • For questions,discussions regarding the lectures/tutorials an ILIAS forum is available: ILIAS Forum
  • Exam: The exam is ORAL for bachelor students of Computer Science (except Prüfungsordnung 2018) and WRITTEN for everyone else.
    1. WRITTEN: Mon 02-09-2019, 14:00 - 16:00, Building: G.-Köhler-Allee 101, Room: 00-010/014
    2. ORAL: TBA
  • Post-exam review: Thu 17-10-2019 and Thu 24-10-2019, 10:00-11:00, Building: 080, Room: Seminar room ground floor

Lectures

Lecture Dates Topic Slides Recordings
00 24-04-2019 Introduction PDF MP4
01 24-04-2019
26-04-2019
Linear Algebra PDF MP4
MP4
02 26-04-2019 Robot Control Paradigms PDF MP4
03 03-05-2019 Wheeled Locomotion PDF MP4
MP4
04 08-05-2019 Proximity Sensors PDF MP4
05 08-05-2019
10-05-2019
15-05-2019
Probabilistic Robotics PDF MP4
MP4
MP4
06 17-05-2019
22-05-2019
Probabilistic Motion Models PDF MP4
MP4
07 22-05-2019
24-05-2019
Probabilistic Sensor Models PDF MP4
MP4
08 29-05-2018 Bayes Filter - Discrete Filters PDF MP4
09 31-05-2019
05-06-2019
Bayes Filter - Particle Filter and MCL PDF MP4
MP4
10 07-06-2019 Bayes Filter - Kalman Filter PDF MP4
11 19-06-2019 Bayes Filter - Extended Kalman Filter PDF MP4
12 21-06-2019
26-06-2018
Grid Maps and Mapping With Known Poses PDF MP4
MP4
13 28-06-2019 SLAM - Simultaneous Localization and Mapping PDF MP4
14 03-07-2019 SLAM - Landmark-based FastSLAM PDF MP4
15 05-07-2019
10-07-2019
SLAM - Grid-based FastSLAM PDF MP4
MP4
16 10-07-2019 SLAM - Graph-based SLAM PDF MP4
MP4
17 12-07-2019 Techniques for 3D Mapping PDF MP4
18 17-07-2019 Iterative Closest Point Algorithm PDF MP4_ss16
19 17-07-2019
19-07-2019
Path and Motion Planning PDF MP4_ss16
MP4
MP4
20 24-07-2019 Multi-Robot Exploration PDF MP4_ss16
21 24-07-2019 Information Driven Exploration PDF MP4_ss16

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.

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
00 n/a Setup Python PDF
01 03-05-2019 Linear Algebra PDF laserscan.dat PDF
02 10-05-2019 Locomotion, Differential Drive PDF PDF
03 17-05-2019 Bayes Rule PDF PDF
04 24-05-2019 Sampling, Motion Models PDF PDF
05 31-05-2019 Sensor Models PDF PDF
06 07-06-2019 Discrete Filter, Particle Filter PDF pf_framework.tar.gz PDF
07 21-06-2019 Extended Kalman Filter PDF kf_framework.tar.gz PDF
08 28-06-2019 Mapping with Known Poses PDF PDF
09 05-07-2019 Simultaneous Localization and Mapping PDF PDF
10 12-07-2019 FastSLAM PDF fastSLAM_framework.tar.gz
fastSLAM_algorithm.pdf
PDF
11 19-07-2019 Iterative Closest Point Algorithm PDF icp_framework.tar.gz PDF
12 26-07-2019 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)
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