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arXiv · 2202.04213

Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation

Fahira Afzal Maken·Fabio Ramos·Lionel Ott
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Abstract

Estimation of a dynamical system's latent state subject to sensor noise and model inaccuracies remains a critical yet difficult problem in robotics. While Kalman filters provide the optimal solution in the least squared sense for linear and Gaussian noise problems, the general nonlinear and non-Gaussian noise case is significantly more complicated, typically relying on sampling strategies that are limited to low-dimensional state spaces. In this paper we devise a general inference procedure for filtering of nonlinear, non-Gaussian dynamical systems that exploits the differentiability of both the update and prediction models to scale to higher dimensional spaces. Our method, Stein particle filter, can be seen as a deterministic flow of particles, embedded in a reproducing kernel Hilbert space, from an initial state to the desirable posterior. The particles evolve jointly to conform to a posterior approximation while interacting with each other through a repulsive force. We evaluate the method in simulation and in complex localization tasks while comparing it to sequential Monte Carlo solutions.

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Fahira Afzal Maken, Fabio Ramos, Lionel Ott. 2022-02-09. Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation. https://arxiv.org/abs/2202.04213

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