Guided Trajectory Filtering for Challenging Long-Range AUV Navigation

Aiming at the challenging underwater navigation of autonomous underwater vehicles (AUV), this article develops a novel filtering method with a guided trajectory. The relatively low frequency of ultra-short baseline positioning system (USBL) measurement and the noise caused by complex scenes have a significant impact on the accuracy of AUV motion estimation. Although the existing adaptive extended Kalman filter (EKF) and adaptive unscented Kalman filter (UKF) can roughly eliminate these outliers, the filtering results of these algorithms are not smooth and have a serious degree of jitter, and then greatly affect the local consistency and quality of topographic map seamless stitching. We exploit the relatively smooth strap-down inertial navigation system (SINS) as the guided trajectory for motion estimation, and propose a trajectory smoothness-preserving filter algorithm. This system has the advantage of maintaining local gradient consistency, which not only improves the navigation accuracy, but also ensures the smoothness of the estimation system. Simulation results show that our approach has high robustness and good smoothness. The effectiveness and practicability of the method are demonstrated by real experiments in multiple deep-sea areas with an average depth of more than 1000 m in the southwest Indian Ocean.

Automated summary

This article proposes a novel filtering method with a guided trajectory to address the challenging underwater navigation of autonomous underwater vehicles (AUV). By utilizing the relatively smooth strap-down inertial navigation system (SINS), the proposed algorithm ensures smoothness in motion estimation and maintains local gradient consistency. Simulation results demonstrate high robustness and good smoothness, while real experiments in deep-sea areas further validate the effectiveness and practicability of the method.