摘要
针对机器人同时定位与地图构建(SLAM)问题,提出基于平方根容积卡尔曼滤波的SLAM算法.该算法主要特点是使用平方根容积卡尔曼滤波计算SLAM后验概率密度,以减小线性化误差,达到提高SLAM定位精度的目的.提出的算法通过传递平方根因子代替系统协方差矩阵,因而在计算中避免了耗费时间的Cholesky分解,提高了算法效率.实验部分使用扩展型卡尔曼滤波SLAM(EKF-SLAM)、无迹卡尔曼滤波SLAM(UKF-SLAM)和所提出的算法进行了对比.实验结果表明:较之EKF-SLAM,容积卡尔曼滤波的精度提高了1倍;相比UKF-SLAM,SCKF-SLAM节省1/4计算资源.
For simultaneous localization and mapping （SLAM） of robots, a new solution is proposed, named square-root cubature Kalman filter based SLAM algorithm （SCKF-SLAM）. The main contribution of the proposed algorithm is that the SLAM posterior probability density is calculated by using the square root cubature Kalman filter in order to reduce lineariza- tion error and improve SLAM accuracy. Instead of covariance matrixes, square-root factors are used in the proposed SLAM algorithm to avoid the time-consuming Cholesky decompositions and improve the calculation efficiency. In experiments, the proposed algorithm is compared with extended Kalman filter SLAM （EKF-SLAM） and unscented Kalman filter SLAM （UKF-SLAM）. The results show that compared with EKF-SLAM, precision of SCKF-SLAM is doubled, and compared with UKF-SLAM, SCKF-SLAM saves a quarter of computation resources.
出处
《机器人》
EI
CSCD
北大核心
2013年第2期186-193,共8页
Robot
基金
国家自然科学基金资助项目(61134001
60909055)
国家973计划资助项目(2012CB215202)
国家863计划资助项目(SS2012AA052302)
中央高校基本科研业务费专项资金资助项目(2012JBM017
2011YJS287)
关键词
移动机器人
卡尔曼滤波
线性化
容积变换
mobile robot
Kalman filter
linearization
cubature transformation
