Monte Carlo localization for mobile robot using adaptive particle merging and splitting technique

Author

Li, Tiancheng ; Sun, Shudong ; Duan, Jun

Author_Institution

Dept. of Mechatron., Northwestern Polytech. Univ., Xi´´an, China

fYear

2010

fDate

20-23 June 2010

Firstpage

1913

Lastpage

1918

Abstract

Monte Carlo localization (MCL) is a success application of particle filter (PF) to mobile robot localization. In this paper, an adaptive approach of MCL to increase the efficiency of filtering by adapting the sample size during the estimation process is described. The adaptive approach adopts an approximation technique of particle merging and splitting (PM&S) according to the spatial similarity of particles. In which, particles are merged by their weight based on the discrete partition of the running space of mobile robot. Using the PM&S technique, a Merge Monte Carlo localization (Merge-MCL) method is detailed. Simulation results illustrate that the approach is efficient.

Keywords

Monte Carlo methods; SLAM (robots); approximation theory; estimation theory; mobile robots; particle filtering (numerical methods); Merge-MCL; adaptive particle merging; approximation technique; estimation process; merge Monte Carlo localization method; mobile robot localization; particle filter; particle splitting technique; Filtering; Hidden Markov models; Merging; Mobile robots; Monte Carlo methods; Particle filters; Probability density function; Robotics and automation; State estimation; Sun; Merging; Monte Carlo localization; Particle filter; Splitting;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation (ICIA), 2010 IEEE International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-5701-4

Type

conf

DOI

10.1109/ICINFA.2010.5512017

Filename

5512017