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Abstract Ports often experience congestion due to the presence of other ships. This congestion poses challenges for ship berth-ing, and even minor errors during the berthing process can lead to significant accidents. Therefore, it is crucial to accu-rately detect the port surroundings and generate a route for automatic berthing based on this accurate detection. To ad-dress these concerns, this study proposes a method that utilizes LIght Detection And Ranging (LIDAR) to detect nearby objects, such as other ships and quays, and generate automatic berthing routes. LIDAR is known for its high detection accuracy compared to other sensors. However, LIDAR data is typically represented as a point cloud, requir-ing the de-tection of individual objects within it. This study employed a deep learning-based method for precise object detection in the point cloud.
Deep learning-based object detection methods have gained significant attention recently. However, they often re-quire large amounts of data for effective training. While various sensor data related to autonomous vehicle navigation are openly available, similar resources for ship navigation are limited. To overcome this limitation, this study collected virtual data by implementing a virtual marine environment using Unity and LIDAR sensors. Object detection using LIDAR alone is insufficient for generating automatic berthing routes. Therefore, this study incorporated real-time ship location recogni-tion and the generation of a surrounding map. The generated map enables the recognition of the ship's surrounding situa-tion, facilitating the creation of automatic berthing routes. The proposed method was applied and validated in a virtual marine environment using Unity, confirming the high accuracy of object detection. Additionally, the effectiveness of generating automatic berthing routes was also demonstrated.
Publication Date 2023-10-19

In-Chang Yeo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Method for Automatic Berthing of a Ship Using LIDAR", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20


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