光学精密工程2011，Vol.19Issue(12)：3064-3073,10.DOI:10.3788/OPE.20111912.3064
用概率假设密度滤波实现同步定位与地图创建
Implementation of SLAM by probability hypothesis density filter
摘要
Abstract
Traditional Simultaneous Localization and Mapping(SLAM) algorithm is lack of the ability to describe multiple sensor information accurately in a clutter environment, and it is prone to false data association. Therefore, this paper proposes a SLAM algorithm based on Probability Hypothesis Density (PHD) filter to deal with these problems. By taking the sensor observation and environmental map as random finite sets in every time step, a joint target state variable is constructed. Then, with the Probability Hypothesis Density(PHD) filtering, the poses and environmental map of the robot are estimated simultaneously and the PHD filter is realized by a particle filter. To avoid the error caused by cluster, a time-delay particle set outputting approach is proposed for joint target state extracting. The new algorithm can describe the observation uncertainty, loss detecting, false alarm due to a clutter and other sensor information accurately, and also can avoid the data association,by which the system state estimation is closer to real values. The simulation results show that the accuracy of the new algorithm in the vehicle localization and mapping is improved by more than 50% as compared with that of traditional SLAM algorithm. It provides a new solution for SLAM problems in the clutter environment.关键词
同步定位与地图创建/随机有限集/概率假设密度滤波/粒子滤波/目标状态提取Key words
Simultaneous Localization and Mapping (SLAM) /random finite set {probability hypothesis density filter/particle filter/target state extraction分类
天文与地球科学引用本文复制引用
杜航原,郝燕玲,赵玉新,杨永鹏..用概率假设密度滤波实现同步定位与地图创建[J].光学精密工程,2011,19(12):3064-3073,10.基金项目
国家自然科学基金资助项目(No.60904087) （No.60904087）
黑龙江省博士后科研启动基金资助项目(No.LBH-Q09127) （No.LBH-Q09127）
