粒子滤波器SLAM算法研究  

Research on Particle Ffilter Based Simultaneous Localization and Mapping Algorithm

作　　者：安婷婷[1] 肖南峰[1] 

机构地区：[1]华南理工大学计算机科学与工程学院,广东广州510006

出　　处：《计算机仿真》2010年第8期73-77,共5页Computer Simulation

基　　金：国家自然科学基金与中国民用航空总局联合资助项目(60776816);广东省自然科学基金重点项目(8251064101000005)

摘　　要：为解决传统的基于Rao-Blackwellized粒子滤波器的同时定位与地图创建(SLAM)算法需要大量的采样粒子,而且频繁重采样操作可能导致粒子耗尽的问题,提出一种改进算法。在计算采样的提议分布时考虑了里程计信息和距离传感器信息,并且通过计算有效粒子数目适时进行重采样操作,通过加入随机粒子来维持多样性。该方法能减少粒子数目,同时保证算法的一致性。仿真结果表明,算法提高了计算效率,创建的栅格地图具有更高的精度。To due with the problems that the conventional Rao-Blackwellized particle filters based simultaneous localization and mapping（SLAM） algorithm needs a large number of particles and that the frequent resampling might lead to the problem of particle impoverishment,an improved approach is proposed.It takes into account both the odometry and the observed information when computing the proposal distribution,resample according to the calculation of the effective sample number and adds some stochastic particles in order to maintain the diversity.Thus this novel method decreases the number of particles and is able to meet the requirement of consistence.The experimental results from stimulation show that the proposed algorithm improves the computational performance as well as builds grid maps with higher accuracy.

关 键 词：移动机器人 粒子滤波器 同时定位与地图创建 提议分布 重采样 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]