摘要
为解决EKF-SLAM算法的状态估计不一致问题,提出一种改进能观性的EKF算法。利用每一个状态变量的第一个可用估计值计算滤波器的雅可比矩阵,从而保证误差状态系统模型的可观测子空间与实际的非线性SLAM系统的可观测子空间具有相同维数。在测量噪声协方差未知的情况下,对EKF-SLAM算法运用初始化的指数移动平均法估计测量噪声协方差。结果表明:改进的EKF-SLAM算法比传统EKF-SLAM算法的定位更精确,均方误差至少降低30%。
In order to solve the problem of inconsistent state estimation of EKF-SLAM algorithm,an improved observability EKF algorithm was proposed.The first available estimate of each state variable was used to calculate the Jacobian matrix of the filter,thus the observable subspace of the error state system model was guaranteed to have the same dimension as the observable subspace of the actual nonlinear SLAM system.In the case of unknown measurement noise covariance,the initial exponential moving average method was used to estimate the covariance of the measurement noise for the EKF-SLAM.The results show that the improved EKF-SLAM algo⁃rithm is more accurate than the traditional EKF-SLAM algorithm,and the mean square error is reduced by at least 30%.
作者
王立玲
苏华强
马东
WANG Liling;SU Huaqiang;MA Dong(College of Electronic and Information Engineering,Hebei University,Baoding Hebei 071002,China;Key Laboratory of Digital Medical Engineering of Hebei Province,Baoding Hebei 071002,China)
出处
《机床与液压》
北大核心
2021年第15期17-23,共7页
Machine Tool & Hydraulics
基金
国家自然科学基金青年科学基金项目(61703133)
国家重点研发计划(2017YFB1401200)。
关键词
能观性
扩展卡尔曼滤波器
指数移动平均法
可观测子空间
Observability
Extended Kalman filter
Exponential moving average method
Observable subspace
