Estimation of ship berthing parameters based on Multi-LiDAR and MMW radar data fusion

Abstract

Achieving autonomous and safe berthing can reflect the intelligence level of unmanned ships, which is also an important manifestation to ensure the safe navigation of ships. As high-precision sensors, LiDAR (Light Detection and Ranging) and MMW Radar (millimeter wave radar) can provide environmental data for berthing. However, the use of one of them has certain shortcomings, and the fusion of the data from both sensors can provide more accurate and ample berthing information. Therefore, a novel data fusing method is proposed for assisting berthing. This method combines multi-solid-state LiDAR point cloud and MMW Radar data to obtain berthing parameters. Firstly, the collected data are pre-processed according to the characteristics of asynchronous sensors separately, and then the berthing parameters under the two sensors are obtained by the relative spatial position between the ship and the berth; In addition, the data-level fusion of multiple LiDARs point cloud is achieved by the point cloud registration-based 3D-NDT (Normal Distributions Transform) algorithm, and the decision-level fusion of berthing parameters acquired from the multi-LiDAR point cloud and MMW Radar data is performed by the weighted data fusion algorithm. Two sensors were used as perceptual devices to effectively monitor the berthing process of the experimental Ro-Ro ship at Pikou Port. The results demonstrate that the proposed method can make the data of the two sensors complement each other and effectively estimate model-free berthing parameters, which is important to assist in achieving efficient and safe berthing of ships.