Estimation for Autonomous Mobile Robots
This lecture introduces estimation techniques to model, localize, and track autonomous mobile robots.
| Instructors | Benjamin Noack |
| Assistants | Marko Ristic, Christopher Funk |
| SWS | 2 + 2 |
| Credits | 6 |
| Languages | English |
| Lecture |
Thursday, 13:15 - 14:45, |
| Exercise |
Tuesday, 15:15 - 16:45, |
Registration
https://elearning.ovgu.de/course/view.php?id=13487
Course Description
Autonomous mobile robots are equipped with different sensors used to answer the question "Where am I?". This lecture introduces the fundamentals of estimation for dead reckoning, localization, and tracking based on different sensors. Participants will learn required methods to model and estimate the state of a mobile robot, to treat model errors and sensor noise, and to fuse data from multiple sensors. Discussed topics include:
- Kinematics, System Models, and Dead Reckoning for Mobile Systems
- Sensor Models and Optimization Methods for Localization and Tracking
- Dynamic State Estimation for Real-Time Localization and Tracking
- Linear Kalman Filtering and Nonlinear Generalizations
Learned Competencies
In this lecture, you will acquire the following competencies:
- You have an overview of basic problems and methods in parameter and state estimation for mobile systems.
- You understand how to develop kinematic models for mobile robots and how to derive discrete-time prediction models.
- You are familiar with the required mathematical tools and can derive and apply least-squares methods for localization and tracking of mobile systems, e.g., based on distance measurements.
- You have a good understanding of Kalman filtering and its nonlinear generalizations for dynamic state estimation and localization of mobile systems.