Introduction to Mobile Robotics - SS 2016
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, Dr. Michael Ruhnke, Dr. Bastian Steder
- Co-organizers: Ayush Dewan, Marina Kollmitz, Tayyab Naseer, Tim Caselitz
- 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 (Group 1) / HS 00 026 µ (Group 2)
- Exam: Mon 05-09-2016, 10.00, Building: 082, Room: HS 00-006 (Kinohörsaal)
- Post-exam review: Wed 09-11-2016, 16.00-18.00, Building: 101, Room: SR 02-016/18
- Next exam (oral): Tue 21-03-2017
Lectures
| Lecture | Dates | Topic | Slides | Recordings |
| 00 | 20-04-2016 | Introduction | MP4 | |
| 01 | 22-04-2016 27-04-2016 |
Linear Algebra | MP4
MP4 |
|
| 02 | 27-04-2016 | Robot Control Paradigms | MP4 | |
| 03 | 27-04-2016 29-04-2016 |
Wheeled Locomotion | MP4
MP4 |
|
| 04 | 29-04-2015 | Proximity Sensors | MP4 | |
| 05 | 04-05-2016 04-05-2016 06-05-2016 11-05-2016 |
Probabilistic Robotics | MP4
MP4 MP4 MP4 |
|
| 06 | 11-05-2016 13-05-2016 25-05-2016 |
Probabilistic Motion Models | MP4
MP4 MP4 |
|
| 07 | 25-05-2016 27-05-2016 | Probabilistic Sensor Models | MP4
MP4 |
|
| 08 | 01-06-2016 | Bayes Filter - Discrete Filters | MP4 | |
| 09 | 01-06-2016 03-06-2016 | Bayes Filter - Particle Filter and MCL | MP4
MP4 |
|
| 10 | 08-06-2016 | Bayes Filter - Kalman Filter | MP4 | |
| 11 | 08-06-2015 | Bayes Filter - Extended Kalman Filter | MP4 | |
| 12 | 10-06-2016 15-06-2016 |
Grid Maps and Mapping With Known Poses | MP4
MP4 |
|
| 13 | 15-06-2016 | SLAM - Simultaneous Localization and Mapping | MP4 | |
| 14 | 17-06-2016 | SLAM - Landmark-based FastSLAM | MP4 | |
| 15 | 22-06-2016 22-06-2016 |
SLAM - Grid-based FastSLAM | MP4
MP4 |
|
| 16 | 24-06-2016 | SLAM - Graph-based SLAM | MP4 | |
| 17 | 29-06-2016 | Techniques for 3D Mapping | MP4
MP4 |
|
| 18 | 29-06-2016 | Iterative Closest Point Algorithm | MP4 |
|
| 19 | 01-07-2016 06-07-2016 08-07-2016 13-07-2016 |
Path and Motion Planning | MP4
MP4 MP4 MP4 |
|
| 20 | 15-07-2016 | Multi-Robot Exploration | MP4 | |
| 21 | 20-07-2016 | Information Driven Exploration | MP4 | |
| 22 | 22-07-2016 | Summary | 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.
Decide if you prefer to use Octave or Python and join the corresponding exercise group:
Octave: Group 1 - Room: SR 00-010/014 - Organized by: Ayush Dewan, Marina Kollmitz
Python: Group 2 - Room: HS 00 026 µ - Organized by: Tayyab Naseer, Tim Caselitz
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 | Octave | Python | Download |
| 01 | |
Setup Octave / Python | |||
| 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_octave.tar.gz pf_framework_python.tar.gz |
||
| 08 | |
Extended Kalman Filter | ekf_framework_octave.tar.gz ekf_framework_python.tar.gz |
||
| 09 | |
Mapping with Known Poses | |||
| 10 | |
Simultaneous Localization and Mapping | |||
| 11 | |
FastSLAM | fastslam_framework_octave.tar.gz fastslam_framework_python.tar.gz |
||
| 12 | |
Iterative Closest Point Algorithm | icp_framework_octave.tar.gz icp_framework_python.tar.gz |
||
| 13 | |
Path Planning | planning_framework_octave.tar.gz planning_framework_python.tar.gz |