Resumen
Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.