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改进RBPF的移动机器人同步定位与地图构建

罗元，余佳航，汪龙峰，王运凯

(重庆邮电大学 国家信息无障碍工程研发中心，重庆 400065)

摘要：传统Rao-Blackwellized粒子滤波器(RBPF)在移动机器人同步定位与地图构建(SLAM)研究中，存在算法复杂度过高、占用内存空间过多导致实时性不理想的问题，因此提出一种改进算法。在某一特定状态的一组粒子集中，粒子的统计特性是一致的，改进算法从中选取一个代表粒子，进行卡尔曼更新步骤，并在同一粒子集中重复使用。同时结合Gmapping算法的建议分布和自适应重采样技术。实际Pioneer III移动机器人在机器人操作系统(ROS)平台上进行的实验表明，该方法在保证栅格地图精度的同时能提高系统的实时性，降低复杂度，提高运算速度。

关键词：移动机器人；Rao-Blackwellized粒子滤波器；同步定位与地图构建(SLAM)；Gmapping算法；自适应重采样技术

DOI:10.3969/j.issn.1673-4785.201404024

中图分类号：TP242.6 文献标志码：A

收稿日期：2014-04-15. 网络出版日期：2015-05-07.

基金项目：国家自然科学基金资助项目(51075420)；重庆市教委科学技术研究项目(KJ120519).

作者简介：

中文引用格式：罗元，余佳航，汪龙峰，等. 改进RBPF的移动机器人同步定位与地图构建[J]. 智能系统学报, 2015, 10(3): 460-464.

英文引用格式：LUO Yuan, YU Jiahang, WANG Longfeng, et al. Simultaneous localization and mapping of an improved RBPF based mobile robot [J]. CAAI Transactions on Intelligent Systems, 2015, 10(3):460-464.

Simultaneous localization and mapping of

an improved RBPF based mobile robot

LUO Yuan, YU Jiahang, WANG Longfeng, WANG Yunkai

(National Information Accessibility Engineering Research & Development Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Abstract：As in the research of simultaneous localization and mapping (SLAM) of mobile robot applying traditional Rao-Blackwellized particle filter, the computational complexity is too high and memory space usage is too large, which causes poor real-time performance, an improved approach is proposed. Among a group of particles gathering in a particular state, the statistical properties of particles are identical. By applying the Kalman updating step to one representative particle in the group of particles, and using it repeatedly in the same group, the complexity is reduced and arithmetic speed is improved. Combining the proposed distribution and adaptive resampling methods from the Gmapping algorithm, the results of actual experiment carried out with Pioneer III robot and ROS platform illustrate that the real-time performance of the proposal could be enhanced while ensuring the quality of grid map.

Keywords：mobile robot; Rao-Blackwellized particle filter; simultaneous localization and mapping (SLAM); Gmapping algorithm; adaptive resampling methods

通信作者：余佳航. E-mail: tracy_the_1@126.com.

同步定位与地图构建(simultaneous localization and mapping, SLAM)最先是由Smith等提出来的[1]，指移动机器人从一个未知的位置出发，在运动过程中利用传感器对环境的观测递增地建立地图,同时根据已建立的地图同步确定自己的位置。十多年来，SLAM问题仍然是机器人研究领域的热点[2]。

目前SLAM问题的解决方法以概率估计为主，主要分为基于卡尔曼滤波(KF)的算法和基于粒子滤波(PF)的算法。……