Particle Filtering on Lie Group for Mobile Robot Localization With Range-Bearing Measurements

This letter proposes a particle filtering method for mobile robot localization. The system model is created based on the robot motion and range-bearing measurements using the Lie group representation, and the perturbation system is obtained by introducing the random variable on the Lie group in order to solve the state estimation problem and reduce impacts from nonlinear approximations. The particle filter is derived to estimate the robot and landmark locations, where the posterior distribution of the robot pose perturbation is approximated by a mixture of Gaussian distributions to mitigate particle impoverishment and improve localization accuracy. The experiment is conducted to evaluate the performance of the proposed method.