CDS 110b: Sensor Fusion

CDS 110b

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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 Discrete-time Kalman filter Discrete-time stochastic systems Main theorem (following AM08) Predictor-corrector form Sensor fusion Problem setup → inverse covariance weighting Example: TBD Variations Multi-rate filtering and filtering with data loss

Wednesday Application: Autonomous driving Low-level sensor fusion in Alice (Gillula) Sensor fusion for urban driving (DGC07) Information filters Problem setup Kalman filter derivation Sensor fusion example revisited Modern extensions of Kalman filtering Moving horizon estimation Particle filters

• Lecture notes on sensor fusion
• Lecture slides on applications and extensions of Kalman filters
• HW #7 (due 5 Mar 08)

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)