Extraction of complex pipeline features from incomplete point clouds

Abstract

Abstract Control point detection in industrial pipelines, characterized by flanges and multiple passes, is critical for accurate virtual simulations and quality assessments in manufacturing. This paper introduces an innovative method for detecting control points in complex pipelines using incomplete point clouds, significantly streamlining the process. Our approach uniquely requires only the straight sections and end-plane localizations as inputs, markedly reducing both data acquisition and processing times. We develop a robust feature descriptor to align the CAD model with incomplete point clouds, facilitating semantic automatic segmentation despite the lack of explicit semantic information. Following this, geometric primitives are fitted to the segmented clouds, and a cylindrical fitting algorithm tailored for incomplete data is introduced. The control points are computed based on the relative positions and geometric parameters of these primitives. Our method has been validated through experiments on several real-world industrial complex pipelines. The results confirm that our approach achieves a high measurement accuracy of 0.067 mm, even with point cloud incompleteness up to 50%. These findings highlight the effectiveness of our method in accurately determining the geometric parameters of complex pipelines and suggest its considerable potential for practical applications.