Traditional accuracy check methods for cargo hold in container ships rely solely on manual and visual operations, which are time-consuming and resource-intensive. Addressing the challenge of extracting and analyzing key data, such as cell guides and container pedestals, from large-scale point clouds obtained through three-dimensional (3D) laser scanning in container ship trial runs, this paper proposes an algorithmic framework based on 3D laser scanning. Building upon this framework, an improved coordinate-axis filtering RANSAC algorithm is employed to optimize the extraction of cell guide planes. Additionally, an algorithmic process based on Bhattacharyya distance is utilized to automatically extract container pedestal point clouds. Furthermore, a combination of the genetic algorithm and the ICP algorithm is proposed to achieve the fitting of the container pedestal edge contour through point cloud registration. Experimental results demonstrate the consistency between the extracted cell guide and container pedestal data and the actual results, indicating the high practical value of the proposed methodology.


container ship loading test; cargo hold accuracy check; 3D point cloud processing

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