Simultaneous localisation and mapping for autonomous underwater vehicle using a combined smooth variable structure filter and extended kalman filter

ABSTRACT Localisation technology is one of the most important challenges of underwater vehicle applications that accomplish any scheduled mission in the complex underwater environment. Currently, the Simultaneous Localisation and Mapping (SLAM) of the Autonomous Underwater Vehicle (AUV) is becoming a hotspot research. AUVs have, only recently, received more attention and underwater platforms continue to dominate the research. To ensure the success of an accurate AUV localisation mission, the problem of drift on the estimated trajectory must be overcome. In order to improve the positioning accuracy of the AUV localisation, a new filter referred to as the Adaptive Smooth Variable Structure Filter (ASVSF) based SLAM positioning algorithm is proposed. To verify the improvement of this filter, the combined SVSF and the Extended Kalman Filter (EKF) are presented. Experimental results based on dataset for underwater SLAM algorithm show the accuracy and stability of the ASVSF AUV localisation position. Several experiments were tested under real-life conditions with an autonomous underwater vehicle based on different filters. The results of these filters have been compared based on Root Mean Squared Error (RMSE) and in terms of localisation and map building errors. It is shown that the adaptive SVSF-SLAM strategy obtains the best performance compared to other algorithms.