A New Particle Filter and Its Application in Mobile Robot Localization

摘要

Particle Filter (PF) is widely used in mobile robot localization, since it is suitable for nonlinear non-Gaussian system. In order to get rid of the bug that the performance of traditional PF is seriously dependent on the selection of proposal distribution, we put forward a Unscented Particle Filter (UPF) algorithm by importing the Unscented Kalman Filter (UKF) to generate the proposal distribution, which means using a series of confirmed samples to approximate the posterior probability density function of the state. Thus the generated proposal distribution will approximate the real posterior probability density function much better, and the quality of the traditional PF will get improved. The simulation result shows that the performance of improved algorithm is better than the traditional Particle Filter although the run time is longer.