Master-slave AUV cooperative localization algorithm based on factor graph

Using factor graph (FG), a master-slave cooperative localization technique is suggested to meet the high-precision positioning needs of autonomous underwater vehicle (AUV) clusters. First, the state equation and measurement equation for a master-slave AUV cooperative localization system are formulated, and a corresponding FG model is constructed. Second, message passing between nodes within the FG model is derived using the sum-product algorithm (SPA), leading to the acquisition of the probability density function (PDF) for the slave AUV’s position. In order to carry out useful experimental verification, a one-master-one-slave cooperative localization test platform is subsequently set up utilizing ground vehicles, GPS, inertial equipment, and data link equipment. The experimental results demonstrate that the proposed cooperative localization algorithm can enhance positioning accuracy by 18.60% compared to the conventional extended Kalman filter (EKF)-based cooperative localization algorithm. Additionally, the results indicate that ranging errors significantly impact the accuracy of cooperative localization