FastSLAM Algorithm Based on Artificial Jellyfish Search Optimization
WANG Lin
YANG Guangyong
LIU Fukang
XU Tianqi
Abstract：Aiming at the fact that the propertyal distribution existing in the traditional FastSLAM algorithm is quite different from the actual distribution,so a large number of particles are required to better represent the posterior distribution,causing a mem-ory explosion,the FastSLAM algorithm is proposed to optimize the FastSLAM algorithm with improved jellyfish search,first of all,the particles with poor adaptability values are chaotic processed,and then the wavelet variation is added when the jellyfish position is updated,and finally the fastSLAM particle sampling is updated with the improved jellyfish optimization algorithm.The pose quali-ty of property distribution sampling is improved.Experimental analysis shows that the proposed method can effectively reduce the er-ror of robot positioning mapping and improve its work efficiency,and can be applied to the study of improving FastSLAM algorithm.
Keywords：FastSLAMproposal distributionjellyfish optimizationlocalization mapping
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 297-302 )