Xu, Shifang; Xie, Li; Liu, Jilin Robot localization based on the MCMC particle filter. (Chinese. English summary) Zbl 1174.68696 J. Zhejiang Univ., Eng. Sci. 41, No. 7, 1083-1087 (2007). Summary: Robot localization based on the Markov Chain Monte Carlo (MCMC) particle filter is proposed to solve the problem that robot localization based on the simple particle filter suffers from severe sample degeneracy. The standard MCMC method, Metropolis Hastings sampling, is incorporated into the filtering framework, and is applied to the robot localization problem. Experimental results show that the MCMC particle filter can increase the sample variety and reduce sample degeneracy. Robot localization based on the MCMC particle filter is much more accurate, compared with robot localization based on the simple particle filter. MSC: 68T40 Artificial intelligence for robotics Keywords:Markov Chain Monte Carlo method; particle filters; robot localization; sample degeneracy PDFBibTeX XMLCite \textit{S. Xu} et al., J. Zhejiang Univ., Eng. Sci. 41, No. 7, 1083--1087 (2007; Zbl 1174.68696)