Research on SLAM Algorithm Based on Improved RBPF Laser
An improved RBPF-SLAM algorithm is proposed in this paper to solve the problems of insufficient positioning accu-racy and particle degradation in building global map by mobile robot using RBPF-SLAM algorithm.In order to improve the accuracy of mapping,the lidar observation data and odometer model are fused into a mixed proposed distribution.In order to slow down the degradation of particles,particle swarm optimization(PSO)algorithm is introduced to adjust the sampled particle set and make the particles move to the region with high likelihood.At the same time,an adaptive local linear resampling(ALLR)algorithm is pro-posed to resample the particles.The experimental results show that the improved proposed distribution and the ALLR resampling al-gorithm combined with PSO can effectively slow down the particle degradation rate,preserve the particle diversity,reduce the amount of calculation,and improve the mapping,positioning accuracy and running speed.
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