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Improved Rao-Blackwellised Particle Filter Based on Randomly Weighted PSO

  • Ye Zhao
  • Ting WangEmail author
  • Wen Qin
  • Xinghua Zhang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

In this paper, a new RBPF-SLAM based on randomly weighted PSO (Particle Swarm Optimization) is proposed in order to solve some problems in the Rao-Blackwellised particle filter (RBPF), including the depletion of particles and loss of diversity in the process of resampling. PSO optimization strategy is introduced in the modified algorithm, inertia weight is randomly set. Modified PSO is utilized to optimize the particle set to avoid particle degenerating and keep diversity. The proposed algorithm is used in the Qt platform to do simulation and verified in ROS by turtlebot. Results show that the proposed RBPF outperform RBPF-SLAM and FastSLAM2.0.

Keywords

Mobile robot Particle swarm optimization RBPF FastSLAM Turtlebot 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Electrical Engineering and Control ScienceNanjing Tech UniversityNanjingChina

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