摘要
从粒子滤波效率主要取决于粒子集更新的思路出发,提出一种基于离散状态空间的移动机器人粒子滤波定位方法(Discrete space particle filter,DSPF)。根据定位系统误差,将机器人运行空间划分为变精度栅格作为样本粒子描述离散系统模型。地图预处理阶段获取栅格粒子的激光扫描数据,预存为栅格粒子特征。粒子权值更新时,将粒子离散化近似为栅格粒子,采用预存粒子特征进行激光扫描数据匹配,避免粒子特征的实时提取,提高了滤波更新效率。同时采用基于离散后验概率分布与当前粒子集分布之间Kullback-Leibler距离检验方法,自适应选择栅格粒子,平衡滤波效率和定位精度,能够有效解决机器人'绑架'问题。仿真结果表明,DSPF能够显著提高定位效率,并保证滤波精度。
A discrete space based particle filter （DSPF） is presented for mobile robot localization based on the thought that the particle filter efficiency mainly depends on the updating of particle set.According to the localization system error,the robot running environment is divided into variable precision grids and discrete system models are described by these grid-particles.The laser scanning data of these grid-particles are acquired and pre-stored as particle characteristics at the environment map pre-processing stage.Particles are discretely approximated to fixed grid-particles and the pre-stored particle characteristics are matched with range measurements of robot at the stage of weight updating of particles.The real-time extraction of particle characteristics is avoided and the filter updating efficiency is improved.Through calculating the Kullback-Leibler distance between the discrete posterior probability distribution and the current particle set distribution,variable precision grid-particles are selected adaptively.The variable precision localization approach balances the filtering efficiency and the locating precision,and the ＂kidnapped robot＂ problem can be solved.Simulation results show that DSPF improves the locating efficiency while keeping the filtering accuracy.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2010年第19期38-43,共6页
Journal of Mechanical Engineering
基金
西北工业大学研究生创业种子基金资助项目(Z200922)
关键词
粒子滤波
离散空间
变精度栅格
移动机器人定位
Particle filter Discrete space Variable precision grids Mobile robot localization
