Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter

Simultaneous Localization and Mapping (SLAM) is an effective method for estimating and correcting the pose of the mobile robot. However, a large amount of external noise and observed outliers in complex unknown environments often lead to a decrease in the estimation accuracy and robustness of the SLAM algorithm. To improve the performance of the Square Root Unscented Kalman Filter SLAM (SRUKF-SLAM), this paper proposes the Maximum Correntropy Square Root Unscented Kalman Filter SLAM (MCSRUKF-SLAM) algorithm. The method first generates an estimate of the predicted state and covariance matrix through the Unscented Transform (UT), and then obtains the square root matrix of the covariance through Cholesky and QR decomposition to replace the original covariance, effectively preserving the positive definiteness of the covariance and improving the accuracy of the algorithm. Moreover, the proposed MCSRUKF-SLAM recharacterizes measurement information at the cost of the Maximum Correntropy (MC) instead of the Minimum Mean Square Error (MMSE), effectively improving the SLAM algorithm's ability to suppress non-Gaussian noise. The simulation results show that compared with EKF-SLAM, UKF-SLAM, SRUKF-SLAM, and MCUKF-SLAM, the accuracy of the proposed MCSRUKF-SLAM in Gaussian mixture noise improves by 81.8%, 80.9%, 78.7%, and 63.6%, and the accuracy of the proposed MCSRUKF-SLAM in colored noise improves by 50.3%, 39.9%, 38.2%, and 36.3%.