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 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 of up to 50%. These findings highlight the effectiveness of our method in
accurately determining the geometric parameters of complex pipelines and suggesting its
considerable potential for practical applications.