CDS 110b: Sensor Fusion

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 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 Information filters Problem setup Kalman filter derivation Sensor fusion example revisited Application: Autonomous driving Low-level sensor fusion in Alice (Gillula) Sensor fusion for urban driving Particle 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)