Error-state Kalman filter-based localization algorithm with velocity estimation for deep-sea mining vehicle

Abstract

The deep-sea mining vehicle is one of the critical equipment of the deep-sea mining system, which is used to collect manganese nodules on the seafloor. When the deep-sea mining vehicles operates, a reliable localization system is essential. This study developed and validated a localization algorithm, named ‘ESKF-slip’, for deep-sea mining vehicles. The algorithm uses error-state Kalman filtering and incorporates velocity estimation using angle encoders, considering the effects of slip and sinkage. A localization system was established for a newly designed deep-sea mining vehicle named “Pioneer 1” based on the proposed localization algorithm. The hardware included an inertial measurement unit, an ultrashort baseline, a compass, and angle encoders of tracks, and the software framework was coded based on the Robot Operating System. Sea trials were performed in the South China Sea to validate the localization algorithm. The results show that the deep-sea mining vehicle could obtain accurate localization results in various complex navigations, demonstrating the feasibility and applicability of the proposed algorithm.