Fully Automated As-Built 3D Pipeline Extraction Method from Laser-Scanned Data Based on Curvature Computation

Abstract

There has been a growing demand for the three-dimensional (3D) reconstruction of as-built pipelines. The as-built 3D pipeline reconstruction process consists of the measurement of an industrial plant, identification of pipelines, and generation of 3D models of the pipelines. Although measurement is now efficiently performed using laser-scanning technology, and in spite of significant progress in 3D pipeline model generation, the identification of pipelines from large and complex sets of laser-scanned data continues to pose a challenge. The aim of this study is to propose a method to automatically extract 3D points corresponding to as-built pipelines that occupy large areas of industrial plants from laser-scanned data. The proposed extraction method consists of the following steps: preprocessing, segmentation of the 3D point cloud, feature extraction based on curvature computation, and pipeline classification. An experiment was performed at an operating industrial plant to validate the proposed method. The experimental result revealed that the proposed method can indeed contribute to the automation of as-built 3D pipeline reconstruction.