在TLS随机模型中考虑对象表面特性的一种方法.
Gabriel Kerekes1, Volker Schwieger1
1University of Stuttgart, Institute of Engineering Geodesy, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany.
概括
这项研究模拟了陆地激光扫描 (TLS) 的随机错误,这些错误是由表面的粗性和反射率等特性引起的. 将它们纳入合成差异-共差矩阵 (SVCM) 提高了结构健康监测的测量质量.
科学领域:
- 地理学工程 工程地质学
- 光学计量学 在光学计量学
- 结构健康监测 结构健康监测
背景情况:
- 陆地激光扫描 (TLS) 的基本原理涉及激光-物体表面相互作用.
- 表面的光学特性 (粗度,反射率) 是系统和随机TLS错误的关键来源.
- 这些错误会影响使用TLS数据的结构健康监测应用程序.
研究的目的:
- 开发一种方法来量化TLS测量中因物体表面特性引起的随机误差.
- 在TLS数据上建模表面粗度和反射率的影响.
- 通过引入一种新的差异和协差确定方法来增强TLS的随机模型.
主要方法:
- 模拟表面特性对TLS测量的影响,使用基本误差理论.
- 开发一个合成方差-共方差矩阵 (SVCM) 来表示这些错误.
- 通过造石头立面元素的现实世界测量来验证该方法.
主要成果:
- 提出的方法有效地模拟了由表面粗度和反射率引起的随机错误.
- 合成方差-共方差矩阵 (SVCM) 提供了一个结构化的方法来整合这些错误来源.
- 验证证实,使用适当的SVCM可以提高基于TLS的估计质量.
结论:
- 通过SVCM计算表面特性对于准确的TLS测量至关重要.
- 开发的方法为TLS随机建模提供了显著的改进.
- 这种方法提高了使用TLS数据进行结构健康监测的可靠性.
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