基于SRCKF的移动机器人同步定位与地图构建

doi:10.3724/SP.J.1218.2013.00200

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引用本文:
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.DOI: 10.3724/SP.J.1218.2013.00200. WANG Hongjian, FU Guixia, BIAN Xinqian, LI Juan. SRCKF Based Simultaneous Localization and Mapping of Mobile Robots. ROBOT, 2013, 35(2): 200-207. DOI: 10.3724/SP.J.1218.2013.00200.

摘要

针对移动机器人同步定位与地图构建（simultaneous localization and mapping，SLAM）存在的计算量大、数值不稳定等问题，结合容积卡尔曼滤波（cubature Kalman filter，CKF）原理，设计了一种基于平方根CKF（square root cubature Kalman filter，SRCKF）的SLAM算法（SRCKF-SLAM）\kern-3pt．SRCKF-SLAM算法通过移动机器人运动模型和观测模型进行预测和观测，并以目标状态均值和协方差的平方根进行迭代更新，确保了协方差矩阵的对称性和半正定性，改进了数值精度和稳定性．仿真实验结果表明，相比CKF-SLAM算法，SRCKF-SLAM算法的均方根误差降低了14%，一致性区域内点数的百分比提高了36%，SRCKF-SLAM算法有效地满足了移动机器人SLAM导航需求．

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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

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 covariance 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.

Key words： mobile robot simultaneous localization and mapping square root cubature Kalman filter

收稿日期: 2012-05-23 录用日期: 2013-01-27

TP242.6

基金资助:

国家自然科学基金资助项目（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）；中央高校基本科研业务费专项资金资助项目（HEUCFZ1026）；教育部新世纪优秀人才支持计划资助项目（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）

通讯作者: 王宏健，cctime99@163.com E-mail: cctime99@163.com

作者简介: 王宏健（1971-），女，博士，教授，博士生导师．研究领域：船舶与水下航行器自主控制与仿真．
傅桂霞（1985-），女，博士生．研究领域：自主水下航行器智能控制与仿真．
边信黔（1941-），男，教授，博士生导师．研究领域：潜器与水下机器人控制技术，船舶运动综合控制．

链接本文:

https://robot.sia.cn/CN/10.3724/SP.J.1218.2013.00200 或 https://robot.sia.cn/CN/Y2013/V35/I2/200

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