Variational Bayesian Based Adaptive CKF-SLAM Algorithm under Non-steady Noise *

The variational Bayesian based adaptive CKF-SLAM algorithm is proposed to resolve the challenge of non-steady noise caused by erratic wheel slippage and sensor performance variances in mobile robot systems. First of all, the inverse Wishart distribution is adopted to establish a prior model for the observation noise matrix. Then, according to orthogonality principle, an adaptive factor is derived to participate in the iterative filtration calculation by using the provided measurement value. Furthermore, the posterior distribution is approximated precisely through variational inference. The pose optimal estimation is obtained by using the Cubature Kalman filter algorithm to literately update the relevant parameters. Finally, simulations under non-steady noise are performed to illustrate VBACKF-SLAM superiority over UKF-SLAM, CKF-SLAM and VBCKF-SLAM. The simulation results represent that the accuracy of mobile robot state estimation is improved by the proposed algorithm, and significant enhancement in positioning and mapping precision is obtained, which could be a valid solution for the real-time localization and map building of mobile robots.