Download PDFOpen PDF in browserGP-SUM. Gaussian Process Filtering of non-Gaussian BeliefsEasyChair Preprint 741, version 216 pages•Date: February 6, 2019AbstractThis work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GPSUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted Sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. Keyphrases: Gaussian mixtures, Gaussian processes, Robotics, filtering, manipulation, pushing
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