SLAM algorithm with Gaussian distributed resampling Rao-Blackwellized particle filter

被引：0
|
作者：
Zhang Y. [1 ]
Zheng X.-F. [1 ]
Luo Y. [1 ]
Pang D.-X. [1 ]
机构：
[1] Research and Development Center of Information Accessibility Engineering and Robotics, Chongqing University of Posts and Telecommunications, Chongqing
来源：
Zhang, Yi (zhangyi@cqupt.edu.cn) | 2016年 / Northeast University卷 / 31期
关键词：
Gaussian distributed resampling; Mobile robots; Rao-Blackwellized particle filter (RBPF); Simultaneous localization and mapping (SLAM);
D O I：
10.13195/j.kzyjc.2015.1227
中图分类号：
学科分类号：
摘要：
For the estimation problem that the RBPF-SLAM algorithm used in mobile robots suffers from sample impoverishment in grid mapping, a Gaussian distributed resampling (GDR) based RBPF-SLAM algorithm is proposed. Firstly, the improved algorithm sorts particles according to the weight size. Furthermore, Gaussian distributed resampling is applied to disperse the high-weight particles so as to generate new particles. By using GDR, particle diversity can be maintained and sample impoverishment can be avoided. Thus accurate grid mapping is guaranteed. Experimental results show the effectiveness of the proposed algorithm. Meanwhile, the results prove that the proposed algorithm guarantees reliable estimation with less samples, and the computation burden can be reduced efficiently. © 2016, Editorial Office of Control and Decision. All right reserved.
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页码：2299 / 2304
页数：5
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