Robust Filtering and Smoothing with Gaussian Processes

TL;DR

This paper introduces a robust Bayesian filtering and smoothing algorithm for nonlinear stochastic systems using Gaussian process models for system and measurement functions, enhancing robustness over existing methods.

Contribution

It presents a novel principled algorithm for analytic smoothing in Gaussian process dynamic systems, improving robustness in robotics and control applications.

Findings

Demonstrates robustness in numerical evaluations

Outperforms existing Gaussian filters and smoothers

Applicable to nonlinear stochastic systems

Abstract

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "system identification" is more robust than finding point estimates of a parametric function representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.