Abstract
In view of challenges of complex underwater environments with a high occurrence of sensor outliers, traditional Kalman filter (KF)-based localization methods are difficult to achieve precise positioning. To address this issue, a factor graph optimization (FGO)-based tightly-coupled (TC) inertial navigation system (INS)/ultra-short baseline system (USBL)/Doppler velocity log (DVL) scheme and core fusion algorithm are proposed. The FGO method is used to construct a TC INS/USBL/DVL factor graph model, and a weight function is added to adjust the weights of each factor, thereby improving the utilization of underwater sensor information and enhancing the accuracy and reliability of underwater navigation. The feasibility of the proposed scheme is verified through simulations and field tests. The results show that the proposed method outperforms KF-loosely-coupled (KF-LC), KF-TC, and FGO-LC methods, with horizontal position accuracy improvements of 29.2%, 6.6%, and 13.2%, respectively. Moreover, the proposed method exhibits smooth navigation error performance in the presence of abnormal sensor information.
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