本文为美国俄勒冈州立大学（作者：Keith E. Williams）的硕士论文，共162页。

三维激光扫描使空间数据采集发生了革命性的变化，可以在多种平台上完成，包括机载（ALS）、移动（MLS）和静态地面（TLS）激光扫描。MLS是一种快速发展的技术，它在提高静态TLS的效率和安全性的同时，仍然能够提供相似的精度和分辨率。组成MLS系统的组件与机载激光扫描（ALS）的并行性要高于TLS。然而，对于MLS系统，可实现的准确度、精度和分辨率没有明确定义。因此，行业专业人员需要准则来规范数据收集、处理和报告的过程。本文为MLS指南奠定了基础，对现有文献进行了全面的回顾，以证明通用MLS系统的能力和局限性。MLS和TLS之间的一个关键区别是，移动平台能够沿着导航路径收集地理参考点的连续路径，而TLS能够在扫描仪从一个位置移动到另一个位置时从多个单独的参考帧收集点。每个单独的TLS设置必须被配置（与公共坐标系链接）到相邻的扫描设置。

本文完成了一项研究，比较了TLS配准和地理参考的常用方法（例如，目标、云到云和混合方法），以帮助TLS勘测员确定最适合其项目的方法。结果提供了洞察精度水平（毫米到厘米级）可以实现的各种方法，以及现场收集和办公室处理所需的时间，以获得一个充分的地理参考点云。

最后，我们为任何形式的激光雷达数据开发了一种质量保证方法，以在不使用向后反射目标的情况下验证点云的绝对和相对精度。该方法结合了扫描仪点云的全站仪验证，以比较常见特征的斜率。通过比较横截面复杂几何图形中的二维坡度特征，可以在水平和垂直分量中提供三维位置误差。该方法利用点云的较大一部分进行统计精度验证，降低了点云单点精度统计的不确定性。使用物理特征进行精度验证对MLS系统尤其重要，因为MLS系统无法对精度验证的目标产生足够的分辨率，除非这些目标位置靠近车辆。

Three-dimensional laser scanning has revolutionized spatial dataacquisition and can be completed from a variety of platforms including airborne(ALS), mobile (MLS), and static terrestrial (TLS) laser scanning. MLS is arapidly evolving technology that provides increases in efficiency and safetyover static TLS, while still providing similar levels of accuracy andresolution. The componentry that make up a MLS system are more parallel toAirborne Laser Scanning (ALS) than to that of TLS. However, achievableaccuracies, precisions, and resolution results are not clearly defined for MLSsystems. As such, industry professionals need guidelines to standardize theprocess of data collection, processing, and reporting. This thesis lays thefoundation for MLS guidelines with a thorough review of currently availableliterature that has been completed in order to demonstrate the capabilities andlimitations of a generic MLS system.A keydifference between MLS and TLS is that a mobile platform is able to collect acontinuous path of geo-referenced points along the navigation path, while a TLScollects points from many separate reference frames as the scanner is movedfrom location to location. Each individual TLS setup must be registered (linkedwith a common coordinate system) to adjoining scan setups. A study wascompleted comparing common methods of TLS registration and geo-referencing (e.g., target,cloud-cloud, and hybrid methods) to assist a TLS surveyor in deciding the mostappropriate method for their projects. Results provide insight into the levelof accuracy (mm to cm level) that can be achieved using the various methods aswell as the field collection and office processing time required to obtain afully geo-referenced point cloud.Lastly,a quality assurance methodology has been developed for any form of LiDAR datato verify both the absolute and relative accuracy of a point cloud without theuse of retro-reflective targets. This methodology incorporates total stationvalidation of a scanners point cloud to compare slopes of common features. Thecomparison of 2D slope features across a complex geometry of cross-sectionsprovides 3D positional error in both horizontal and vertica component. Thismethodology lowers the uncertainty of single point accuracy statistics forpoint clouds by utilizing a larger portion of a point cloud for statisticalaccuracy verification. This use of physical features for accuracy validation isparticularly important for MLS systems because MLS systems cannot producesufficient resolution on targets for accuracy validation unless they are placedclose to the vehicle.

1. 引言

2. MLS在交通运输中的应用综述

3. 手稿章节

4. 基于自然特征的激光雷达数据三维位置误差及二维斜率估计

5. 结论

附录A 交通运输中MLS术语审查表

附录B 第三章的独立目标转换参数

附录C 坡度和可视移位质量保证电子表格的简要说明

附录D 坡度和可视移位质量保证电子表格的数字访问

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