Research on Large-scale Point Cloud Data Processing Algorithm for Simulated Container Loading Test

Traditional methods of the cargo hold of container ships rely entirely on manual and visual operations, which are time-consuming and resource-intensive. Advanced 3D scanning devices are applicable to acquire high-precision 3D point clouds of large-scale cargo hold scenes. To address the challenge of extracting and analyzing key data, such as cell guides and bottom cones, from the large-scale point cloud of the cargo hold, this paper proposes a framework to analyze the construction quality of the cargo hold based on the 3D point cloud. In this framework, the plane filtering condition of the RANSAC algorithm is improved based on the geometrical properties of the cell guide. To address the problem of the small proportion and extracting difficulty of the foundation point cloud, an automatic extraction method for the bottom base point cloud is proposed based on Bhattacharyya distance. To overcome the difficulty of determining the contour features and the center point of the bottom cone point cloud, a method combining genetic algorithm and ICP algorithm is proposed. Experiments are conducted on actual point cloud data to verify the effectiveness of the proposed method. © 2023 Editorial office of Ship Building of China. All rights reserved.