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 | |
| 01 | 20-04-2023 | Transformations (Linear Algebra) | |
| 02 | 25-04-2023 | Robot Control Paradigms | |
| 03 | 25-04-2023 | Wheeled Locomotion | |
| 04 | 27-04-2023 | Proximity Sensors | |
| 05 | 02-05-2023 | Probabilistic Robotics | |
| 06 | 16-05-2023 | Probabilistic Motion Models | |
| 07 | 23-05-2023 | Probabilistic Sensor Models | |
| 08 | 13-06-2023 | Bayes Filter - Discrete Filters | |
| 09 | 13-06-2023 | Bayes Filter - Particle Filter and MCL | |
| 10 | 20-06-2023 | Bayes Filter - Kalman Filter | |
| 11 | 20-06-2023 | Bayes Filter - Extended Kalman Filter | |
| 12 | 27-06-2023 | Grid Maps and Mapping With Known Poses | |
| 13 | 04-07-2023 | SLAM - Simultaneous Localization and Mapping | |
| 14 | 04-07-2023 | SLAM - Landmark-based FastSLAM | |
| 15 | 11-07-2023 | SLAM - Grid-based FastSLAM | |
| 16 | 11-07-2023 | SLAM - Graph-based SLAM | |
| 17 | 18-07-2023 | Iterative Closest Point Algorithm |
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 | |
Setup Python | myfirstscript.py | ||
| 01 | |
Linear Algebra | laserscan.dat | ||
| 02 | |
Locomotion, Differential Drive | |||
| 03 | |
Bayes Rule | |||
| 04 | |
Sampling, Motion Models | |||
| 05 | |
Sensor Models | |||
| 06 | |
Discrete Filter, Particle Filter | pf_framework.tar.gz | PDF (alternate .ipynb solution for q1) | |
| 07 | |
Extended Kalman Filter | kf_framework.tar.gz | ||
| 08 | |
Mapping with Known Poses | |||
| 09 | |
FastSLAM | fastSLAM_framework.tar.gz fastSLAM_algorithm.pdf |
||
| 10 | |
Iterative Closest Point Algorithm | icp_framework.tar.gz | PDF demo code |