CDS 110b: Sensor Fusion: Difference between revisions
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* Problem setup | * Problem setup | ||
* Kalman filter derivation | * Kalman filter derivation | ||
* Sensor fusion example revisited | * Sensor fusion example revisited | ||
* | <li>Application: Autonomous driving</li> | ||
* Low-level sensor fusion in Alice (Gillula) | |||
* Sensor fusion for urban driving | |||
<li>Particle filters</li> | |||
</ol> | </ol> | ||
<br> | <br> |
Revision as of 15:42, 24 February 2008
CDS 110b | Schedule | Project | Course Text |
In this set of lectures we discuss discrete-time random processes and the discrete-time Kalman filter. We use the discrete-time formulation to consider problems in (multi-rate) sensor fusion and sensor fusion in the presence of information/packet loss. We also introduce the information filter, which provides a particularly simple method for sensor fusion.
Monday
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Wednesday
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References and Further Reading
- R. M. Murray, Optimization-Based Control. Preprint, 2008: Chapter 5 - Sensor Fusion
- Appendix from Ben Grochalsky's thesis on information filter.
- CDS 270-2 (Networked Control Systems) page on Kalman Filtering - provides additional notes and lecture materials (including some nice references)