Download PDFOpen PDF in browserCurrent versionGP-SUM. Gaussian Process Filtering of non-Gaussian BeliefsEasyChair Preprint 741, version 116 pages•Date: January 19, 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
|