SLAM Algorithm of Iterated Square Root Cubature Kalman Filtering

doi:10.3969/j.issn.1000-3428.2015.09.058

Abstract

Abstract: The disadvantage of Square Root Cubature Kalman Filtering(SR-CKF)algorithm on the Simultaneous Location and Mapping(SLAM)is that with map feature points increasing,the volume points deviate from the ideal trajectory to cause great defects in state estimation.In order to solve that problem,this paper provides an improved square root cubature Kalman filtering algorithm.The algorithm takes advantage of iterative method of the measurement update,which makes the sampling points less distortion and further improves the accuracy in the highly nonlinear environment.Simulation results show that,compared with SR-CKF algorithm,this algorithm can effectively improve the accuracy of position and attitude.

Key words: iteration, Simultaneous Localization and Mapping(SLAM), sampling, Extended Kalman Filtering(EKF), weighted processing

摘要： 平方根容积卡尔曼滤波算法在移动机器人同步定位与地图创建问题中，存在随着地图特征点增多、容积点偏离理想轨迹、状态估计产生较大误差的缺陷。为此,提出一种改进的平方根容积卡尔曼滤波算法。该算法引入迭代测量更新的方法,在更新阶段利用估计值和平方根因子重新确定采样的容积点,使得采样点在高度非线性环境下保持较小失真,进一步提高精度。仿真结果表明,与平方根容积卡尔曼滤波算法相比,该算法能提高机器人位姿精度。

关键词: 迭代, 同步定位与地图创建, 采样, 扩展卡尔曼滤波, 加权处理

CLC Number:

TP24

TAO Ming,LING Youzhu,CHEN Mengyuan,DAI Xuemei. SLAM Algorithm of Iterated Square Root Cubature Kalman Filtering[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2015.09.058.

陶明,凌有铸,陈孟元,戴雪梅. 迭代的平方根容积卡尔曼滤波SLAM算法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2015.09.058.

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References

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