A Novel EKF-SLAM Algorithm Against Outlier Disturbance

Abstract

There is not only sensor noise,but also outlier disturbance when a robot explores in unknown environments.The traditional EKF-SLAM algorithm does not consider the impact of outlier disturbance that may lead to positioning failure.The new algorithm detects the outlier disturbance by comparing two observations result using polar coordinates.Covariance would be inflated when disturbance is detected,so that system state of uncertainty is expanded and the state quickly converges to the true value.Simulation results show that the proposed algorithm is better than EKF-SLAM both in mobile robot SLAM accuracy and robustness.