Automatic 3D Reconstruction of As-built Pipeline Based on Curvature Computations from Laser-Scanned Data

Abstract

Demand has been growing for three-dimensional (3D) reconstruction of asbuilt pipelines that occupy large areas within operating plants. In practice, measurements are efficiently performed using laser-scanning technology; however reconstructing an as-built pipeline from this laser-scanned data remains challenging. The data acquired from the plant facility can be incomplete due to complex occlusion, or it can be affected by noise due to the reflective surfaces of the pipelines and other parts. The aim of this study is to propose a method for generating models of entire pipelines that include straight pipes, elbows, reducers, and tee pipes from laserscanned data. The proposed 3D reconstruction method for as-built pipelines is divided into three main tasks: (1) identifying the types and locations of the pipelines from the laser-scanned data; (2) segmenting the pipelines into each type of pipe form; and (3) reconstructing the pipelines' geometry and topology and generating models of them. Field experiments were performed at an operating industrial plant in order to validate the proposed method. The results revealed that the proposed method can indeed contribute to the automation of 3D reconstruction of as-built pipelines.

Fast Cylinder Shape Matching Using Random Sample Consensus in Large Scale Point Cloud

“…After that, Hough transform was used to project a plane on the normal vector and propose a cylinder shape estimation method through circle fitting [9][10][11]. Son determined the central axis of the pipe by the skeleton extraction algorithm through Laplacian smoothing and estimated the curved region and the T shape of the cylinder by dividing [12]. Huang classified meaningful data through fast point feature histogram (FPFH) and SVM and segmented it with the classical flood-fill algorithm.…”

Automatic Detection and Modeling of Underground Pipes Using a Portable 3D LiDAR System

“…Although the proposed method effectively extracts featured structures and also identifies connection relationships in the piping network, it somewhat struggles to find straight portions of the pipe in the point cloud. The authors of [12] used the skeleton extraction algorithm with Laplacian smoothing to determine the central axis of the pipe. This axis was then divided to estimate the curved region and the T shape of the cylinder.…”