A localization method for particle-filter based on the optimization of particle swarm

Abstract

To locate a mobile robot efficiently and accurately,we propose a localization algorithm for the particle- filter based on particle swarm optimization.The drawbacks of generic particle- filter are analyzed.By incorporating the newest observations into the sampling process and using particle swarm optimization,the prediction performance of the generic particle-filter is improved.After that,the probabilistic motion-model and observation-model of the mobile robot are established,and the self-localization problem of the mobile robot is resolved by applying the particle swarm optimization to the particle filter.In this method,through particle swarm optimization,particles are moved to the regions where they have larger values of posterior density function.As a result,the impoverishment of the particle filter is overcome and the number of particles needed for accurate location is reduced dramatically.Simulation experiments show the validity of the proposed method.