Improved Rao-Blackwellised particle filter based on randomly weighted particle swarm optimization. (October 2018)

• Record Type: Journal Article
• Authors: Zhao, Ye; Wang, Ting; Qin, Wen; Zhang, Xinghua
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 477
• Page End: 484
• Publication Date: 2018-10
• Subjects: Mobile robot -- Particle swarm optimization -- Rao-Blackwellised particle filter -- Fastslam -- Turtlebot; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.055
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, a new Rao-Blackwellised particle filter simultaneous localization and mapping method based on randomly weighted particle swarm optimization is proposed in order to solve some problems in the Rao-Blackwellised particle filter, including the depletion of particles and the loss of the diversity in the process of resampling. The particle swarm optimization strategy is introduced in the modified algorithm, and the inertia weight is randomly set. Modified particle swarm optimization method is utilized to optimize the particle set in order to avoid particle degenerating and to keep the diversity. The proposed algorithm is simulated in the Qt platform. To verify the proposed method, the experiment is performed using turtlebot robots under the robot operating system. Results show that the proposed method is superior than extended Kalman filter based on simultaneous localization and mapping method.