Title :
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
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;
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
DOI :
10.1109/ICINFA.2010.5512017
