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A feature extraction method for deformation analysis of large-scale composite structures based on TLS measurement

机译:基于TLS测量的大型复合结构变形分析特征提取方法

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摘要

AbstractHow to obtain a three-dimensional (3D) model efficiently and extract the feature information of larger-scale composite structures, such as tunnels, accurately is a significant issue in the field of health monitoring. Therefore, an effective method based on TLS measurement is proposed and developed using surface-based non-destructive technology.In this paper, terrestrial laser scanning (TLS) technology is adopted to investigate the tunnel structure, focusing on the extraction of the characteristic section and central curve, which could be applied in deformation monitoring. Point cloud data from TLS measurement is processed in four steps: section extraction, section projection, calculation of central points and curve approximation.The innovation of this paper lies in the projection and iterative filtering of the ring data and rasterization of the point clouds for vertical and horizontal lines. The random sample consensus (RANSAC) algorithm is implemented to approximate the vertical and horizontal lines. The central curve, approximated from the central points, agrees with the general design model and the accuracy falls within the millimeter range.
机译: 摘要 如何有效地获取三维(3D)模型并提取大型复合结构(例如隧道)的特征信息,在健康监测领域中,准确地是一个重大问题。因此,提出并开发了一种基于表面无损技术的基于TLS测量的有效方法。 地面激光扫描技术研究隧道结构,重点是特征截面和中心曲线的提取,可用于变形监测。 TLS测量的点云数据分为四个步骤处理:截面提取,截面投影,中心点计算和曲线逼近。 本文的创新之处在于环形数据的投影和迭代过滤以及垂直和水平线的点云的栅格化。实现随机样本共识(RANSAC)算法以近似垂直线和水平线。从中心点近似的中心曲线与通用设计模型一致,精度在毫米范围内。

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