An Adaptive Unscented Particle Filter Algorithm through Relative Entropy for Mobile Robot Self-Localization

Abstract

Self-localization is a basic skill for mobile robots in the dynamic environments. It is usually modeled as a state estimation problem for nonlinear system with non-Gaussian noise and needs the real-time processing. Unscented particle filter (UPF) can handle the state estimation problem for nonlinear system with non-Gaussian noise; however the computation of UPF is very high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF) algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.