Autonomous Underwater Vehicle Localization and Map Building Based SVSF Using Smoothing Boundary Layer Width

Localization technology is one of the most crucial issues for underwater vehicle applications that must accomplish any scheduled task in the challenging aquatic environment. Simultaneous Localization and Mapping (SLAM) with an Autonomous Underwater Vehicle (AUV) is currently a hot topic in the study. UAVs have only recently achieved greater recognition, and underwater platforms are still being studied. An approach has been established to ensure the success of an accurate localization mission by overcoming the problem of drifting from an AUV’s planned trajectory. A new adaptive technique based on two merged filters, Smooth Variable Structure Filter (SVSF) and Extended Kalman Filter (EKF), is presented to increase the accuracy of AUV localization. The accuracy, stability, and reduction in terms of Root Mean Square Error (RMSE) of the localization position of the AUV have been tested in natural conditions using the results of several experiments. These experimental data are used in the SLAM algorithm, which shows the best result and performance using the proposed approach compared to other algorithms.