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Comparison of point cloud data and 3D CAD data for on-site dimensional inspection of industrial plant piping systems

机译:比较点云数据和3D CAD数据以进行工厂厂房管道系统的现场尺寸检查

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摘要

Inspection is vital in industrial plant construction and management. However, traditional inspection methods that rely on human involvement and paper documentation are becoming untenable as modern industrial plants are becoming larger and more complex than legacy facilities. Hence, an efficient and robust method is required to support the inspection of modern industrial plants. In this paper, an improved technique relying on terrestrial laser scanning (TLS) for data acquisition and normal-based region growing and efficient random sample consensus (RANSAC) for point cloud data processing is proposed for the on-site dimensional inspection of the piping systems of an industrial plant. Consequently, the as-built condition of the plant is assessed via a distance-based deviation analysis and a comparison of geometric parameters between the as-designed and as-built models. The method is validated using a dataset acquired from a compartment of a ship has verified the robustness and reliability of the proposed approach.
机译:检查对工厂的建设和管理至关重要。但是,随着现代工业工厂的规模比传统工厂更大,更复杂,依靠人工参与和纸质文档的传统检查方法变得站不住脚。因此,需要一种有效且鲁棒的方法来支持对现代工业工厂的检查。本文提出了一种基于地面激光扫描(TLS)的数据采集和基于法线的区域增长以及基于点云数据处理的有效随机样本共识(RANSAC)的改进技术,用于管道系统的现场尺寸检查工厂的一部分。因此,可通过基于距离的偏差分析以及设计模型和构建模型之间的几何参数比较来评估工厂的构建条件。使用从船舱获得的数据集验证了该方法,该数据集已验证了所提出方法的鲁棒性和可靠性。

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