Addressing the requirement for precise sensing during ship berthing and unberthing at close range, we propose a high-precision berthing and unberthing positioning method by fusing LiDAR with an inertial navigation system.

Initially, we develop a multi-coordinate conversion and time registration algorithm for ship navigation, achieving the temporal and spatial unification of various sensing devices while correcting the collected point cloud data. Subsequently, we introduce an enhanced random sample consensus (RANSAC) berth shoreline detection method which mitigates the impact of point cloud data randomness on shoreline detection accuracy by considering berth elevation and optimizing the clustering cost function. The identified shoreline intersection serves as a characteristic point for berthing, aiding unmanned surface vehicles in positioning. Finally, the effectiveness of the proposed method is validated through real ship experiments.

The experimental outcomes demonstrate that this positioning method yields positioning information for ships with an error of less than 0.256 m and exhibits strong adaptability to various interferences in real-world environments.

The improved RANSAC method for ship berthing and unberthing can obtain high positioning accuracy even when the quality of the point cloud data is poor.