基于近似b样条曲面和全填充合成协方差矩阵的双曲线木结构两历元TLS变形分析

Gabriel Kerekes, Jakob Raschhofer, C. Harmening, H. Neuner, V. Schwieger
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引用次数: 1

摘要

本文提出了一种基于b样条的区域变形分析方法,应用于双曲线木塔的单元。监控对象为Urbach Tower,高度为14m。利用在现实条件下获得的两个时代的地面激光扫描,逼近了塔外壳的两个联合参数化b样条曲面。表面近似中观测值的随机模型基于初等误差理论,由方差-协方差矩阵(SVCM)定义。在此基础上,将物体的尺寸从几dm扩展到几m,测量距离从20到60 m。此外,在SVCM中处理了特定环境的误差源,揭示了物体尺寸以及附加的基本误差对估计的b样条曲面和随后的变形分析的影响。利用曲面参数网格构造点对,估计被测对象的刚体运动,同时检测木塔的变形区域。所有的变形分析结果都采用基于初等误差模型的假设检验进行统计验证,该模型通过曲面估计和变形分析处理算法传播。结果表明,在建模和变形分析过程中,测量噪声被降低,从而以统计正确的方式检测到畸变区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-epoch TLS deformation analysis of a double curved wooden structure using approximating B-spline surfaces and fully-populated synthetic covariance matrices
This contribution presents a B-spline-based approach of area-wise deformation analysis applied on elements of a double curved wooden tower. The monitored object is the Urbach Tower with a height of 14 m. Terrestrial laser scans from two epochs acquired under real-world conditions are used for approximating two jointly parametrized B-spline surfaces of the tower’s outer shell. The stochastic model of the observations used within the surface approximation is based on elementary error theory and is defined by a synthetic variance-covariance matrix (SVCM). In addition to previous work on this topic, the object’s dimension is extended from a few dm to a few m and the measurement distance ranges from 20 to 60 m. Moreover, environment specific error sources are addressed in the SVCM, revealing the effect of the object’s dimension as well as of additional elementary errors on the estimated B-spline surfaces and the subsequent deformation analysis. Based on constructed points pairs using a grid of surface parameters, rigid body movements of the object under investigation are estimated while at the same time distorted regions of the wooden tower are detected. All results of the deformation analysis are statistically verified using hypothesis tests based on the elementary error model propagated through the processing algorithms of surface estimation and deformation analysis. The results demonstrate that during the modelling and deformation analysis, the measurement noise is reduced and therefore distorted regions are detectable in a statistically correct way.
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