A Multi Swarm Particle Filter for Mobile Robot Localization
Particle filter (PF) is widely used in mobile robot localization, since it is suitable for the nonlinear non-Gaussian system. Localization based on PF, However, degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot looses its diversity. One of the main reasons for loosing particle diversity is sample impoverishment. It occurs when likelihood lies in the tail of the proposed distribution. In this case, most of particle weights are insignificant. To solve those problems, a novel multi swarm particle filter is presented. The multi swarm particle filter moves the samples towards region of the state space where the likelihood is significant, without allowing them to go far away from the region of significant values for the proposed distribution. The simulation results show the effectiveness of the proposed algorithm.
Keywords: Localization, Particle Filter, Particle Swarm Optimization (PSO)
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