A Kind of SLAM Algorithm Based on Strong Tracking Cubature Kalman Filter

Abstract

Simultaneous Localization and Mapping (SLAM) of mobile robots should satisfy some requirements for calculation accuracy and speed. When observation noise is time-varying and mixed, the filter accuracy will decrease or even divergence, which leads to more position errors or even failure of the mobile robot. To solve the problem, the Strong Tracking Cubature Kalman SLAM algorithm (CKFST-SLAM) is improved. Firstly, the noise estimator is introduced to estimate the noise unbiasedly, and the statistical characteristics of the noise are estimated and corrected in real time. Secondly, the diagonalization of the matrix (DM) is used to replace the Cholesky decomposition in the update phase of the mobile robot position in the algorithm. This algorithm enhances the robustness of the inaccurate statistical characteristics of noise effectively and improves the filter accuracy and stability. The simulation experiment results show that compared with the CKFST-SLAM, the Root Mean Square Error (RMSE) of the algorithm proposed in this paper is reduced by 43.8% and 34.8% under two scenarios with different observation noise.