SLAM estimation method for uncertain model noise parameters

Abstract

It is difficult to accurately obtain the statistical parameters of mobile robots motion parameters for its mobility. And it is also the same in observation system because of the environmental variability, which would cause the SLAM system noise statistical parameters uncertainty, and decrease the filtering performance. A SLAM adaptive filtering algorithm with noise statistical characteristics estimation is proposed, when the noise statistical parameters are constant but unknown, three additional maximum posteriori estimators are constructed, and adaptive CKF SLAM algorithm are designed. Compared with the standard CKF SLAM algorithm, the results show that under the uncertainty noise parameters, if the noise variance is Gaussian white noise distribution, the designed adaptive CKF SLAM with three maximum posterior can well estimate the state, track noise variance, and handle the problem of nonlinear systems. The simulation results verify the effectiveness of the proposed SLAM estimation method with the uncertainty of the model noise parameters.