Estimation for Autonomous Mobile Robots
(in German: Estimation for Autonomous Mobile Robots )
Module-ID: FIN-INF-120485 |
| Link: | LSF |
| Responsibility: | Benjamin Noack |
| Lecturer: | Benjamin Noack |
| Classes: |
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| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. INGINF: Ingenieurinformatik - M.Sc. WIF: Informatik - M.Sc. DKE: Applied Data Science - M.Sc. DE: Methoden des Digital Engineering - M.Sc. DE: Methoden der Informatik - M.Sc. DE: Fachliche Spezialisierung - M.Sc. VC: Computer Science |
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Abbreviation AMR |
Credit Points 6 |
Semester Winter |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Students who complete the course ...
- have an overview of basic problems and methods in parameter and state estimation for mobile systems.
- understand how to develop kinematic models for mobile robots and how to derive discrete-time prediction models.
- 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.
- have a deep understanding of Kalman filtering and its nonlinear generalizations for dynamic state estimation and localization of mobile systems.
Content:
- Kinematics, System Models, and Dead Reckoning for Mobile Systems
- Sensor Models and Optimization Methods for Localization and Tracking
- DynamicState Estimation for Real-Time Localization and Tracking
- Linear Kalman Filtering and Nonlinear Generalizations
Workload:
56 h contact time
124 h independent study
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Oral exam (30 min) |
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| Prerequisites according to examination regulations: | Recommended prerequisites: |
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none |
Linear Algebra, Analysis |
| Media: | Literature: |
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Literature will be announced in the lecture.
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Comments: