Research on berthing information perception of unmanned ship based on UAV vision

In the process of automatic berthing, the Marine Autonomous Surface Ship ( MASS ) needs to perceive its own state relative to the berth in real time and accurately to avoid collision. The camera mounted on the drone can act as the 'eyes' of the ship, and extending its "arm" allows for a clearer view, while there is no obvious accurate feature on the berth for drone visual recognition. Therefore, this paper proposes a MASS berthing state perception method based on UAV visual recognition ArUco code. The method consists of three parts : camera calibration, ArUco code detection and berthing perception. Firstly, the camera calibration is carried out by the calibration method proposed by Zhengyou Zhang. Secondly, the ArUco code is identified by image gray processing, thresholding, Gaussian filter illumination compensation and other methods. Then, a berthing state perception model is proposed, including the calculation methods of bow and stern berthing distance, approaching speed, forward speed and ship berthing angle. Finally, a test platform was built and a berthing experiment was carried out. The accuracy of the method is verified by real ship test, which meets the requirements of real-time and accuracy for berthing state.