Abstract An approach to mobile robot simultaneous localization and mapping (SLAM) based on an improved particle filter is presented. The standard particle filter and particle swarm optimization algorithm are incorporated into the filtering framework of the proposed approach. The newest observations are introduced to adjust proposal distribution of particles, and thus the necessary number of particles for localization and mapping is greatly reduced and the particle degeneracy problem is effectively abated. In addition, considering that the typical resampling process always leads to the loss of diversity in particles,a probabilistic operator is introduced to keep the diversity of particle swarm. Experimental results show the feasibility and effectiveness of the proposed approach.
PAN Wei,CAI Zi-Xing,CHEN Bai-Fan. An Approach to Mobile Robot Simultaneous Localization and Mapping Based on Improved Particle Filter[J]. , 2008, 21(6): 843-848.
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2008, Vol. 21 
