Development and Validation of a Point Cloud Data Processing Algorithm for Obstacle Recognition in Double Hull Block

Abstract

Shipyards have recently been experiencing severe labor shortages, prompting increased adoption of production automation systems to mitigate this issue. This paper introduces a point cloud data acquisition and processing system designed to support automation operations within double-hull block environments. The acquisition system utilizes LiDAR sensors and is built as a portable device capable of conducting 360-degree scans inside double-hull blocks. The processing system integrates the RANSAC algorithm for plane recognition and a voxelization algorithm for object detection, enabling accurate identification of obstacles within the double-hull block. To validate the system, a full-scale test bench was constructed to replicate actual working conditions. Experimental results indicated that the system could detect the positions of various obstacles within the test bench with an accuracy of up to 100 mm, which is sufficient for the implementation of automation systems. The findings from this research are expected to facilitate the adoption of production automation in shipyards, enhancing productivity and addressing labor shortages in the industry.

Keywords

• 3d scanner
• double hull block
• lidar
• point cloud
• obstacle detection