基于强跟踪的移动机器人CQKF-SLAM方法
CQKF-SLAM method for mobile robot based on strong tracking
张凤 1孙健 1袁帅2
作者信息
- 1. 沈阳建筑大学电气与控制工程学院,辽宁沈阳 110168
- 2. 沈阳建筑大学电气与控制工程学院,辽宁沈阳 110168;沈阳建筑大学计算机科学与工程学院,辽宁沈阳 110168
- 折叠
摘要
针对容积正交卡尔曼滤波(CQKF)在同时定位与地图构建(SLAM)中系统状态驱动模型与观测数据存在突变,以及协方差分解引起系统不稳定,导致移动机器人定位精度降低的问题,提出一种基于多重渐消因子强跟踪的SVDCQKF-SLAM方法.采用奇异值分解(SVD)代替CQKF算法中的乔列斯基分解,抑制状态误差协方差矩阵负定性;引入多重渐消因子强跟踪滤波器调节状态预测协方差矩阵.通过仿真实验,将所提SLAM方法与其它SLAM方法进行对比,其结果表明,该方法能够有效降低SLAM过程中的定位误差,对移动机器人同时定位与地图构建有一定参考价值.
Abstract
Aiming at the problem that the system state driven model and observation data have sudden changes and system insta-bility caused by covariance decomposition in the cubature quadrature Kalman filter(CQKF)simultaneous localization and map-ping(SLAM),leading to the lower positioning accuracy of mobile robots,a multiple fading factor strong tracking singular value decomposition cubature quadrature Kalman filter simultaneous localization and mapping(MST-SVDCQKF-SLAM)method was proposed.Singular value decomposition(SVD)was used to replace Cholesky decomposition in CQKF algorithm to suppress the negative definiteness of the state covariance matrix.A multi fading factor strong tracking filter was adopted to adjust the state prediction covariance matrix.Through simulation experiments,the proposed SLAM method was compared with other SLAM methods.Simulation results show that this method can effectively reduce the positioning error in the SLAM process,and has certain reference value for the simultaneous localization and mapping of mobile robots.
关键词
强跟踪滤波算法/多重渐消因子/奇异值分解/容积正交卡尔曼滤波/同时定位与地图构建/协方差矩阵/移动机器人Key words
strong tracking filter algorithm/multiple fading factor/singular value decomposition/cubature quadrature Kalman filter/simultaneous localization and mapping/covariance matrix/mobile robot引用本文复制引用
基金项目
国家自然科学基金(62073227)
国家自然科学基金(61863033)
辽宁省教育厅项目(LJKZ0581)
辽宁省教育厅项目(LJKZ0584)
辽宁省应用基础研究计划(2023JH2/101300212)
出版年
2024
NETL
NSTL
万方数据