Robust SVSF-SLAM for Unmanned Vehicle in Unknown Environment

Abstract

Abstract: Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. The most popular solutions of this problem are the EKF-SLAM and the FAST-SLAM, the former requires an accurate process and observation model and suffer from the linearization problem, and the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF-SLAM) algorithm is proposed in this paper to solve the Unmanned Ground Vehicle (UGV) SLAM problem. The SVSF filter which is formulated in a predictor-corrector format is robust face parameters uncertainties and error modeling and doesn’t require any assumption about noise characteristics. In this paper the SVSF-SLAM algorithm is implemented using the odometer and LASER data to construct a map of the environment and localize the UGV within this map. The proposed algorithm is validated and compared to the EKF-SLAM algorithm. Good results are obtained by the SVSF-SLAM comparing to the EKF-SLAM especially when the noise is colored or affected by a variable bias. Which confirm the efficiency of our approaches.