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
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 |
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 |
|
|
Additional Material
- Notes on one dimensional Gaussians (PDF)
- Notes on multi dimensional Gaussians (PDF)
- Python cheat sheet (PDF)
- Matrix cookbook (PDF)
