Optimal Design of Laser SLAM Algorithm Based on RBPF improved resampling technology

Abstract

Simultaneous Localization and Mapping (SLAM) has always been a fundamental and central issue for mobile robots. At present, Rao-Blackwellized particle filter (RBPF) is a practical method to solve the Simultaneous Localization and Mapping of robots. In order to improve the problem of lack of particles and lack of diversity in the traditional RBPF algorithm, an optimized laser SLAM algorithm is proposed. Minimum sampling-variance(MSV) resampling is used to improve the original resampling method, and improve the proposal distribution to make it closer to the target distribution. In this paper, simulation experiments are carried out on Matlab and ROS platforms, and the experimental results prove that the improved algorithm can effectively improve the positioning effect and the quality of mapping.