A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Abstract

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.