Seminar Advanced Estimation Methods for Autonomous Robotic Systems
(in German: Seminar Advanced Estimation Methods for Autonomous Robotic Systems )
Module-ID: FIN-INF-120499 |
| Link: | LSF |
| Responsibility: | Prof. Dr.-Ing. Benjamin Noack |
| Lecturer: | Prof. Dr.-Ing. Benjamin Noack |
| Classes: | '- Seminar Advanced Estimation Methods for Autonomous Robotic Systems |
| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INF: Schlüssel- und Methodenkompetenzen - M.Sc. INGINF: Informatik - M.Sc. INGINF: Schlüssel- und Methodenkompetenzen - M.Sc. WIF: Informatik - M.Sc. WIF: Schlüssel- und Methodenkompetenzen - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. VC: Computer Science - M.Sc. VC: Schlüssel- und Methodenkompetenzen |
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Abbreviation AEMARS |
Credit Points 6 |
Semester Summer |
Term
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Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Students who complete the course ...
- can independently research complex topics
- write clear scientific articles
- present informative and understandable scientific talks
- understand challenges and methods in state estimation for robotic systems
Content:
For an autonomous robotic system, being able to extract information, e.g., about its position, orientation, and its surroundings, from sensor readings, is essential for its successful and safe operation.
This seminar covers a variety of methods, such as nonlinear versions of the Kalman filter, particle filters, moving horizon estimation, as well as Bayesian estimation for discrete-valued quantities, for such robotic estimation tasks.
Workload:
Individual Work Time 130h:
- Reading and Understanding of Provided Papers
- Research of Additional Papers
- Writing
- Presentation
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Seminar (2 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
Attendance of Lecture AMR is helpful |
| Media: | Literature: |
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Literature will be announced in the lecture.
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Comments: