Monte Carlo Localization for Mobile Robot Using Adaptive Particle Merging and Splitting Technique

摘要：

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.

关键词： Monte Carlo localization Particle filter Merging Splitting

作者: Tiancheng Li Shudong Sun Jun Duan

作者单位: Department of MechatronicNorthwestern Polytechnical University Xian,Shanxi Province,China Department of Mechatronic Northwestern Polytechnical University Xian,Shanxi Province,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）