AN APPROACH ON ADVANCED UNSCENTED KALMAN FILTER FROM MOBILE ROBOT-SLAM
In the past 30 years, Kalman filter is a classical method to solve the problem of simultaneous localization and mapping (SLAM) of mobile robots. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are derived from Kalman filter. Extended Kalman filter (EKF) overcomes the nonlinear problem that Kalman filter cannot solve. However, when it is strongly nonlinear, EKF violates the assumption of local linearity, and EKF algorithm may make the filter diverge. Secondly, because the Jacobian matrix is needed in the online processing of EKF, its tedious calculation process makes the implementation of this method relatively difficult. Unscented Kalman filter (UKF) uses nonlinear model directly, avoids operation of Jacobian matrix of complex nonlinear function, and ensures the general adaptability of nonlinear system. In this paper, based on the square root unscented Kalman filter, sigma points are selected according to the square-root decomposition of prior covariance, and then weighted mean and covariance are calculated. The quaternion is used to represent the attitude, and the quaternion vector is converted to the rotation space for matrix operation. Comparing the robot poses estimated based on the square root traceless Kalman filter (QSR-UKF), square root traceless Kalman filter (SR-UKF), and extended Kalman filter (EKF), the simulation results show the QSR-UKF proposed in this paper is effective.