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
- Lecturer: Prof. Dr. Wolfram Burgard,
- Co-organizers: Marina Kollmitz, Oier Mees, Daniel Büscher
- Lecture: Wed 16.00-18.00 / Fri 14.00-15.00, Building: 101, Room: SR 00-010/014
- Exercises: Fri 15.00-16.00, Building: 101, Room: SR 00-010/014
- Exam: There are going to be ORAL EXAMS for bachelor students of Embedded Systems Engineering and Computer Science.
The exam will be WRITTEN for everyone else.
- WRITTEN: Fri 18-08-2017, 10:00, Building: 101, Room: HS 00 036
- ORAL: Fri 18-08-2017, Tue 22-08-2017
- Question Round: Fri 11-08-2017, 15.00-16.00, Building: 101, Room: SR 00-010/014
- post-exam review: Wed 25-10-2017, 16:00-17:00, Building: 052, Room: SR 02-017
Lectures
| Lecture | Dates | Topic | Slides | Recordings |
| 00 | 26-04-2017 | Introduction | MP4 | |
| 01 | 26-04-2017 28-04-2017 |
Linear Algebra | MP4_ss16
MP4 |
|
| 02 | 03-05-2017 | Robot Control Paradigms | MP4 | |
| 03 | 03-05-2017 05-05-2017 |
Wheeled Locomotion | MP4
MP4 |
|
| 04 | 05-05-2017 | Proximity Sensors | MP4 | |
| 05 | 10-05-2017 12-05-2017 |
Probabilistic Robotics | MP4
MP4 |
|
| 06 | 17-05-2017 17-05-2017 19-05-2017 |
Probabilistic Motion Models | MP4_ss16
MP4_ss16 MP4_ss16 |
|
| 07 | 24-05-2017 26-05-2017 |
Probabilistic Sensor Models | MP4_ss16
MP4 |
|
| 08 | 26-05-2017 | Bayes Filter - Discrete Filters | MP4 | |
| 09 | 31-05-2017 31-05-2017 | Bayes Filter - Particle Filter and MCL | MP4_ss16
MP4_ss16 |
|
| 10 | 02-06-2017 | Bayes Filter - Kalman Filter | MP4_ss16 | |
| 11 | 14-06-2017 | Bayes Filter - Extended Kalman Filter | MP4 | |
| 12 | 14-06-2017 16-06-2017 21-06-2017 21-06-2017 |
Grid Maps and Mapping With Known Poses | MP4
MP4 MP4 MP4 |
|
| 13 | 23-06-2017 28-06-2017 |
SLAM - Simultaneous Localization and Mapping | MP4
MP4_ss16 |
|
| 14 | 28-06-2017 | SLAM - Landmark-based FastSLAM | MP4_ss16 | |
| 15 | 30-06-2017 05-07-2017 05-07-2017 |
SLAM - Grid-based FastSLAM | MP4_ss16
MP4 MP4 |
|
| 16 | 12-07-2017 | SLAM - Graph-based SLAM | MP4_ss16 | |
| 17 | 07-07-2017 | Techniques for 3D Mapping | MP4 | |
| 18 | 12-07-2017 | Iterative Closest Point Algorithm | MP4_ss16 |
|
| 19 | 14-07-2017 19-07-2017 21-07-2017 |
Path and Motion Planning | MP4_ss16
MP4 MP4 |
|
| 20 | 26-07-2017 | Multi-Robot Exploration | MP4_ss16 | |
| 21 | 26-07-2017 | Information Driven Exploration | MP4_ss16 | |
| 22 | 28-07-2017 | Summary | MP4_ss14 |
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 | |
Setup Python | func.py | ||
| 02 | |
Linear Algebra | laserscan.dat | ||
| 03 | |
Locomotion, Differential Drive | |||
| 04 | |
Bayes Rule | |||
| 05 | |
Sampling, Motion Models | |||
| 06 | |
Sensor Models | |||
| 07 | |
Discrete Filter, Particle Filter | pf_framework.tar.gz | ||
| 08 | |
Extended Kalman Filter | kf_framework.tar.gz | ||
| 09 | |
Mapping with Known Poses | |||
| 10 | |
Simultaneous Localization and Mapping | |||
| 11 | |
FastSLAM | fastSLAM_framework.tar.gz fastSLAM_algorithm.pdf |
||
| 12 | |
Iterative Closest Point Algorithm | icp_framework.tar.gz | ||
| 13 | |
Path Planning | planning_framework.tar.gz |