机器人2013，Vol.35Issue(2)：200-207,8.DOI:10.3724/SP.J.1218.2013.00200
基于SRCKF的移动机器人同步定位与地图构建
SRCKF Based Simultaneous Localization and Mapping of Mobile Robots
摘要
Abstract
In order to solve the large computing cost and numerical instabilities of simultaneous localization and mapping (SLAM), a square root cubature Kalman filter (SRCKF) based SLAM algorithm (SRCKF-SLAM) for mobile robots is designed according to cubature Kalman filter (CKF) principle. The SRCKF-SLAM algorithm accomplishes prediction and observation through motion model and observation model, and it is updated iteratively by propagating square root factors of the mean and covariance of the state variable, which guarantees the symmetry and positive semi-definiteness of the covari-ance matrix and therefore improves numerical accuracy and stability. The simulation experiments show that, compared with the CKF-SLAM algorithm, the root-mean square error of the SRCKF-SLAM algorithm decreases 14%, and the percentage of points in consistency area increases 36%, therefore the SRCKF-SLAM algorithm effectively satisfies the requirement of SLAM navigation of mobile robots.关键词
移动机器人/同步定位与地图构建/平方根容积卡尔曼滤波Key words
mobile robot/ simultaneous localization and mapping/ square root cubature Kalman filter分类
信息技术与安全科学引用本文复制引用
王宏健,傅桂霞,边信黔,李娟..基于SRCKF的移动机器人同步定位与地图构建[J].机器人,2013,35(2):200-207,8.基金项目
国家自然科学基金资助项目(E091002/50979017) （E091002/50979017）
教育部高等学校博士学科点专项科研基金资助项目(20092304110008) （20092304110008）
中央高校基本科研业务费专项资金资助项目(HEUCFZ1026) （HEUCFZ1026）
教育部新世纪优秀人才支持计划资助项目(NCET-10-0053) （NCET-10-0053）
哈尔滨市科技创新人才(优秀学科带头人)研究专项资金资助项目(2012RFXXG083). （优秀学科带头人）
