Robust EKF-SLAM method against disturbance using the Shifted Mean based Covariance Inflation Technique

This paper presents a novel solution to overcome the disturbance noise (outlier) for the Extended Kalman Filter based Simultaneous Localization And Mapping (EKF-SLAM). The standard Kalman Filter (KF) is not robust to the disturbance noise. The possibility that disturbance may happen is high, because SLAM aims at exploring unknown environment. Hence KF based SLAM methods should consider how to handle the disturbance noise. Variations of KF have been introduced to overcome this problem. However, these methods employ manual parameter tuning, detecting/weighting method. The core of our algorithm is to inflate the state uncertainty by using the magnitude of innovation, without tuning and detecting. Although it is impossible to estimate the state value immediately, the inflated state uncertainty makes it possible for the estimated value to converge on the true value much faster. We evaluate the proposed method under the well-known benchmark Matlab program. The results show that the proposed method overcomes the disturbance noise and increases the performance of EKF-SLAM.