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Introduction to Mobile Robotics - SS 2023

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: Dr. Tim Welschehold , Dr. Daniel Büscher, Dr. Lukas Luft
  • Co-organizers: Adriana Gómez, Chenguang Huang, Kshitij Sirohi,
  • The lectures take place Tuesdays at 14:15-16:00 (room 101 00 026) and Thursdays at 14:15-15:00 (room 101 01 009/13).
  • The exercises take place Thursdays 15:00-16:00 (room 101 01 009/13).
  • For questions and discussions: ILIAS Forum.
  • The exam will be written on Tuesday, September 5, starting 13:30. Duration: 90 minutes.
  • The exam location is at G.-Köhler-Allee 101 For room information, please check ILIAS forum.
  • Post-exam review: Thu 21-09-2023, Fri 22-09-2023 and Thu 19-10-2023, 14:00 - 15:00, building 080 (back entrance), ground floor seminar room
  • Retake exam: oral, individual time slots in Februar and March 2024, in building 80, upper floor, small seminar room.

Lectures

Lecture Dates Topic Slides
00 18-04-2023 Introduction PDF
01 20-04-2023 Transformations (Linear Algebra) PDF
02 25-04-2023 Robot Control Paradigms PDF
03 25-04-2023 Wheeled Locomotion PDF
04 27-04-2023 Proximity Sensors PDF
05 02-05-2023 Probabilistic Robotics PDF
06 16-05-2023 Probabilistic Motion Models PDF
07 23-05-2023 Probabilistic Sensor Models PDF
08 13-06-2023 Bayes Filter - Discrete Filters PDF
09 13-06-2023 Bayes Filter - Particle Filter and MCL PDF
10 20-06-2023 Bayes Filter - Kalman Filter PDF
11 20-06-2023 Bayes Filter - Extended Kalman Filter PDF
12 27-06-2023 Grid Maps and Mapping With Known Poses PDF
13 04-07-2023 SLAM - Simultaneous Localization and Mapping PDF
14 04-07-2023 SLAM - Landmark-based FastSLAM PDF
15 11-07-2023 SLAM - Grid-based FastSLAM PDF
16 11-07-2023 SLAM - Graph-based SLAM PDF
17 18-07-2023 Iterative Closest Point Algorithm PDF

Exercises

Solving the exercise sheets is not mandatory to be admitted to the final exam, but is strongly recommended. There are no bonus points. The sheets will be published one week before the corresponding exercise session. We encourage you to solve the exercises beforehand to benefit from the discussions in class.

Sheet Discussion Topic Exercise Sheet Exercise Material Solutions
00 20-04-2023 (Thu) Setup Python PDF myfirstscript.py
01 27-04-2023 (Thu) Linear Algebra PDF laserscan.dat PDF
02 04-05-2023 (Thu) Locomotion, Differential Drive PDF PDF
03 11-05-2023 (Thu) Bayes Rule PDF PDF
04 25-05-2023 (Thu) Sampling, Motion Models PDF PDF
05 06-06-2023 (Tue) Sensor Models PDF PDF
06 22-06-2023 (Thu) Discrete Filter, Particle Filter PDF pf_framework.tar.gz PDF (alternate .ipynb solution for q1)
07 29-06-2023 (Thu) Extended Kalman Filter PDF kf_framework.tar.gz PDF
08 06-07-2023 (Thu) Mapping with Known Poses PDF PDF
09 13-07-2023 (Thu) FastSLAM PDF fastSLAM_framework.tar.gz
fastSLAM_algorithm.pdf
PDF
10 20-07-2023 (Thu) Iterative Closest Point Algorithm PDF icp_framework.tar.gz PDF demo code

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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