Rank Kalman Filter-SLAM for Vehicle with Non-Gaussian Noise

Abstract

Simultaneous localization and mapping (SLAM) is a crucial problem to solve the navigation and positioning for an autonomous robot moving in an unknown environment. This work proposes a rank Kalman filter (RKF) SLAM algorithm based on the principle of rank statistic to deal with the robot SLAM. The RKF algorithm can effectively simulate the probability distribution of nonlinear systems by using even rank sample points. The proposed RKF-SLAM is validated in simulations by comparing with two nonlinear filtering SLAM algorithms under the situation that the observation noises distribute to Gaussian distribution or non-Gaussian distribution.