管道(软件)
计算机视觉
人工智能
校准
计算机科学
摄像机切除
管道运输
三维重建
像素
摄像机自动校准
忠诚
毫米
单摄像机
由运动产生的结构
计算机图形学(图像)
工程类
运动估计
光学
数学
统计
物理
程序设计语言
电信
环境工程
作者
Zhexiong Shang,Zhigang Shen
标识
DOI:10.1016/j.autcon.2022.104231
摘要
A novel inline inspection (ILI) approach using depth cameras array (DCA) is introduced to create high-fidelity, dense 3D pipeline models. A new camera calibration method is introduced to register the color and the depth information of the cameras into a unified pipe model. By incorporating the calibration outcomes into a robust camera motion estimation approach, dense and complete 3D pipe surface reconstruction is achieved by using only the inline image data collected by a self-powered ILI rover in a single pass through a straight pipeline. The outcomes of the laboratory experiments demonstrate one-millimeter geometrical accuracy and 0.1-pixel photometric accuracy. In the reconstructed model of a longer pipeline, the proposed method generates the dense 3D surface reconstruction model at the millimeter level accuracy with less than 0.5% distance error. The achieved performance highlights its potential as a useful tool for efficient in-line, non-destructive evaluation of pipeline assets.
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