When the observed noise parameters are unknown or change with time

the performance of the traditional SLAM algorithm will decline. In this paper

an SLAM algorithm is presented based on variational Bayes noise adaptive cubature Kalman filter (VB-ACKF). The inverse Wishart distribution was used to model the observed noise parameters

and the nonlinear variational Bayes filter was utilized to estimate the joint posteriori probability of the mobile robot state and the unknown observation noise parameter. The proposed algorithm effectively solved the problem of filtering divergence of traditional filtering algorithms when the observed noise parameter was unknown or changing. Simulation results show that the positioning accuracy of VB-ACKF-SLAM algorithm was greatly improved compared with the SLAM method based on the cubature Kalman filter (CKF-SLAM)

the unscented Kalman filter SLAM algorithm (UKF-SLAM)

and the extended Kalman filter SLAM algorithm (EKF-SLAM) when the observed noise parameters are unknown or changing. The effectiveness of the algorithm is proved.