基于高斯分布重采样的Rao-Blackwellized粒子滤波SLAM算法  

SLAM algorithm with Gaussian distributed resampling Rao-Blackwellized particle filter

作　　者：张毅[1] 郑潇峰 罗元[1] 庞冬雪[1] 

机构地区：[1]重庆邮电大学信息无障碍工程与机器人技术研发中心,重庆400065

出　　处：《控制与决策》2016年第12期2299-2304,共6页Control and Decision

基　　金：国家自然科学基金项目(51075420);国家科技部国际合作项目(2010DFA12160)

摘　　要：针对移动机器人同时定位与地图构建中RBPF-SLAM算法因粒子匮乏而导致栅格地图估计不精确问题,提出一种基于高斯分布重采样的RBPF-SLAM算法.首先,根据粒子权重大小对重采样粒子进行排序;然后,在重采样中利用高斯分布分散高权重粒子得到新粒子,从而保证粒子多样性,避免粒子匮乏,保证栅格地图的精确构建.实验结果表明了所提出算法的有效性,同时也证明该算法能在粒子数减少的条件下保持可靠的估计,有效地减少了计算量.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.

关 键 词：移动机器人 同时定位与地图构建 RAO-BLACKWELLIZED粒子滤波 高斯分布重采样 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]