非線形・非ガウス状態推定のためのStein粒子フィルタ
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
更新・予測モデルの微分可能性を活かし、粒子を再生核ヒルベルト空間内で決定的に流して事後分布へ近づける粒子フィルタを提案し、高次元の非線形・非ガウス系の状態推定を可能にした。
著者: Fahira Afzal Maken, Fabio Ramos, Lionel Ott
分類: cs.RO
原文アブストラクト
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.
