中文摘要：

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．

英文摘要：

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.