A three-dimensional localisation algorithm for underwater acoustic sensor networks

Abstract

Simultaneous localisation mapping (SLAM) is a scheme for location-aware technology that can be applied in vehicles or autonomous robots in outdoor environments. The SLAM scheme helps in the localisation of AUVs (autonomous underwater vehicles) used in deep ocean exploration. In this paper, we propose a 3D SLAM scheme that utilises an extended Kalman filter (EKF) based on landmarks in underwater sensor networks (UWSNs). We obtained our research results by the following three steps. First, we designed and implemented the AUV model using kinematics. Second, we analysed the EKF for use in SLAM and derived an optimisation model of the EKF using information on a moving path and landmarks in an underwater environment. Finally, we implemented the map through the derived EKF, as applied to SLAM in UWSNs. We have thoroughly compared the value of a pre-defined path to the predicted value and confirmed the average position error in terms of AUV speed, number of landmarks, and the sampling time. Through Matlab simulations, we have shown that the proposed scheme achieves a smaller position error under a change in vehicle speed and number of landmarks. We have also confirmed the minimum performance of the 3D-SLAM technique for use in underwater applications.