SLAM Method for Mobile Robot Based on Particle Swarm Optimization

Abstract

To solve the particles degeneracy phenomenon of the Rao-Blackwellized particle filter(RBPF),an improved method of RBPF based on particle swarm optimization(PSO)is presented to solve simultaneous localization and mapping(SLAM)problem of mobile robot.During the particle re-sampling process,the proposed distribution of mobile robot's pose is acquired by PSO.The crossover and mutation operation of genetic algorithm is applied to optimize and adjust the obtained particle sets.The new distribution of particles maintains the diversity of the particles and improves the consistency of robot's pose estimation effectively.Experiment of mobile robot in a particular environment has been implemented.The simulation results demonstrate that the presented approach solves the problem of particles degeneracy effectively,improves the accuracy of SLAM and has the characteristics of feasibility and availability.