Sliding Mode SLAM for Robust Simultaneous Localization and Mapping

Normal SLAMs use the extended Kalman filter to estimate robot localization and the mapping simultaneously. They do not work well under big disturbances and bounded noises. In this paper, the sliding mode method is applied for the SLAM. The proposed sliding model SLAM only requires the noises and the disturbances are bounded. The estimation errors are analyzed, and the stability of the novel SLAM is proposed. A mobile robot is applied in the experiment to show the effectiveness of the sliding mode SLAM in the presence of bounded noises.