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.
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Zhao, Y., Wang, T., Qin, W., Zhang, X. (2020). Improved Rao-Blackwellised Particle Filter Based on Randomly Weighted PSO. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_3
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DOI: https://doi.org/10.1007/978-3-030-04946-1_3
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