Simultaneous Localization and Mapping Method Based on Improved Cubature Kalman Filter

Abstract

matrix, root mean square error (RMSE) Toward solving some of the problems of low precision, poor stability, and complex calculation in the simultaneous localization and mapping (SLAM) of mobile robots, an improved cubature Kalman filter SLAM (ICKF-SLAM) algorithm based on the cubature Kalman filter SLAM (CKF-SLAM) algorithm is proposed. Firstly, the error covariance matrix of the state vector is obtained through the motion model and observation model of the mobile robot. Then, the information matrix is obtained by the inverse operation, and the information state vector is updated in the prediction and update phases. The proposed method reduces the computational complexity and improves the accuracy of the algorithm. Simulation results show that compared with CKF-SLAM, the root mean square error of ICKF-SLAM is reduced by 11.8%.