Stanford AA274A Principles of Robotic Autonomy | Autumn 2019 | Parametric Filtering
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This lecture shows how a Bayes filter for robot localization becomes computable: under linear-Gaussian assumptions it is exactly a Kalman filter that tracks only a mean and covariance, and for the nonlinear models rob...