Robust Student's t-based trajectory tracking for inspection wall-climbing robot

To address the problem of measurement noise interference in the trajectory tracking process of a wall-climbing robot (WCR), a robust Student's t-based extended Kalman filter (STEKF) is proposed. The measurement noise of the extended Kalman filter (EKF) is modeled with a Student's t distribution instead of a Gaussian distribution. Three kinds of filters are selected for comparison. Simulation results show that STEKF algorithm is more robust with accuracy than existing state-of-the-art filters.