Robust and optimised sub-component detection using 3D point cloud for shipbuilding sub-assembly automation

This study proposes a 3D object detection system to reliably detect components in complex environments using 3D point cloud data to automate arrangement operations in shipbuilding sub-assembly processes. To achieve this, an improved RANSAC algorithm was developed by integrating a scoring scheme that considers normal vector similarity, inlier distribution, and the presence of regions containing the topmost feature points. Following the initial model estimation, a refitting procedure based on the inlier set was performed to enhance the stability and accuracy of the plane model. Additionally, a priority-based sampling method was applied to the plane estimation process to optimise computational efficiency. A component-selection algorithm was developed that considered the classification ID, position, size, count, and remaining status of the recognised components, and a fully integrated processing framework was constructed to transmit the recognition results in real time for direct use by a gantry robot. Applying the proposed system to a shipyard sub-assembly environment demonstrated stable recognition performance under stacking conditions, with average dimensional and pose errors of 2.4 mm and 0.35°, respectively. The average processing time was 0.99 s, confirming the suitability of the system for real-time automated processes. • A robust 3D sub-component recognition method is developed for sub-assembly automation under complex conditions. • An improved RANSAC plane estimation method reliably extracts the topmost component with high computational efficiency. • A real-time integrated recognition–selection–transmission framework supports direct gantry robot operation. • Field experiments demonstrate high accuracy, with average dimensional error of 2.4 mm, pose error of 0.35°.