广东工业大学学报2017，Vol.34Issue(2)：92-96,5.DOI:10.12052/gdutxb.160055
基于粒子滤波的SLAM算法并行优化与实现
Parallel Optimization and Implementation of SLAM Algorithm Based on Particle Filter
摘要
Abstract
Simultaneous localization and mapping is a new type of mobile robot localization method, which can obtain data through the mobile robot's own sensors and simultaneous localization and map building in a completely unknown environment. Based on PF-SLAM algorithm, the probability distribution of the location pose is expressed by the particle set, and the calculated amount is proportional to the size of the particle set, and also the number of particles determines the algorithm's location accuracy and anti-jamming capability. At the same time, increasing the number of particles will increase the computing time, which will lead to the positioning delay and the positioning error of the mobile robot. A method is presented to improve the algorithm by using GPU parallel computing, which can reduce the calculation time, thereby to reduce the positioning error caused by positioning delay. Experimental results show that the improved algorithm of GPU parallel computing has a significant effect.关键词
即时定位与地图构建/粒子滤波/GPU并行计算Key words
simultaneous localization and mapping/particle filter/GPU parallel computing分类
信息技术与安全科学引用本文复制引用
朱福利,曾碧,曹军..基于粒子滤波的SLAM算法并行优化与实现[J].广东工业大学学报,2017,34(2):92-96,5.基金项目
国家自然科学基金资助项目(61202267) （61202267）
广东省产学研合作专项资金资助项目(2014B090904080) （2014B090904080）
