An obstacle avoidance sonar based SLAM algorithm for AUV integrated navigation

In this paper, we investigate a simultaneous localization and mapping (SLAM) algorithm based on obstacle avoidance sonar. The proposed algorithm is employed to improve the precision of long-distance and deep-sea navigation for autonomous underwater vehicles (AUVs) with payload capacity, power and cost constraints. Through the continuously estimation and observation of the vehicle states and the map, the resulting estimated error is sent back to the Inertial Navigation System (INS) for correction. Then, the accumulated errors of attitude, velocity and position caused by the distance increasing and time running can be effectively reduced. Extensive simulations are provided to show that the proposed algorithm can improve AUV navigation and positioning accuracy effectively.