Simultaneous localization and mapping algorithm for unmanned ground vehicle with SVSF filter

Abstract

Filtering strategies play an important role in estimation theory, and are used to extract knowledge of the true states typically from noisy measurements or observations made of the system. This paper describes a novel approach that combines the information given by an odometer and a laser range finder sensors to efficiently solve the Simultaneous Localization and Mapping (SLAM) problem of the Unmanned Ground Vehicle (UGV) and reconstruct a $2D$ representation of the environment. In recent years, to solve the SLAM problem, many solutions have been proposed. To resolve this problem, the most commonly used approaches are the EKF-SLAM and the FASTSLAM. An accurate process and a model of observation are needed for the first approach, which is suffering the linearization problem. While the second one is not convenient and is not suitable for real time implementation. Therefore, a new state and parameter estimation method is introduced based on the smooth variable structure filter (SVSF) is proposed in this paper to solve the UGV SLAM problem. The SVSF is a relatively new estimation strategy based on sliding mode concepts, formulated in a predictor-corrector format. It has been shown to be very robust to modeling errors and uncertainties. In this work the SVSF-SLAM algorithm is implemented 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.