本文为德国维尔茨堡大学（作者：Hamidreza Houshiar）的博士论文，共199页。

三维点云实际上是三维文档和建模的标准。激光扫描技术的进步拓宽了三维测量系统的可用性和使用范围。三维点云在机器人、三维建模、考古学、测量学等学科中有着广泛的应用。扫描器能够每秒获取高达一百万个点，用密集的点云来表示环境。这表示捕获的环境具有非常高的细节程度。激光扫描技术与摄影技术的结合为点云增加了色彩信息。因此，环境表现得更加真实。完整的三维环境模型，没有任何遮挡，需要多次扫描。合并点云是一个具有挑战性的过程。

本文提出了基于扫描生成全景图像的点云配准方法。点云的图像表示将二维图像处理方法引入到三维点云中。本文提出了几种生成点云全景图的投影方法，此外，还提出了基于全景图的点云压缩方法。由于三维测量系统产生的数据量很大，这些方法对于改进点云的处理、传输和存档是非常必要的。本文将点云处理方法作为考古发掘数字化的新框架。该框架取代了传统开挖现场的文件编制方法。在现场考古学家的帮助下，可以使用点云生成挖掘的数字文档。

三维点云不仅用于数据表示，还用于分析和知识生成。最后，本文提出了一个自主的室内移动地图绘制系统。该映射系统主要研究传感器的布局规划方法。捕获一个完整的环境需要多次扫描。传感器布局规划方法解决了大环境数字化所需扫描量最小的问题。将此方法与移动机器人平台上的导航系统相结合，使其能够完全自主地获取数据。本文介绍了一种新的点云孔洞检测方法，用于检测捕获环境中的遮挡部分。传感器布局规划方法能够选择下一个扫描位置，该位置对遮挡环境的覆盖范围最大，从而减少了所需的扫描次数。机器人平台导航系统由路径规划、路径跟踪和避障组成。这保证了移动机器人平台在扫描位置之间的安全导航。传感器布置规划方法被设计为一个独立的过程，可与移动机器人平台一起用于环境的自主映射，或作为勘测员在扫描项目中的辅助工具。

3D point clouds are a de facto standard for 3D documentation andmodelling. The advances in laser scanning technology broadens the usability andaccess to 3D measurement systems. 3D point clouds are used in many disciplinessuch as robotics, 3D modelling, archeology and surveying. Scanners are able toacquire up to a million of points per second to represent the environment witha dense point cloud. This represents the captured environment with a very highdegree of detail. The combination of laser scanning technology with photographyadds color information to the point clouds. Thus the environment is representedmore realistically. Full 3D models of environments, without any occlusion,require multiple scans. Merging point clouds is a challenging process. Thisthesis presents methods for point cloud registration based on the panoramaimages generated from the scans. Image representation of point cloudsintroduces 2D image processing methods to 3D point clouds. Several projectionmethods for the generation of panorama maps of point clouds are presented inthis thesis. Additionally, methods for point cloud reduction and compressionbased on the panorama maps are proposed. Due to the large amounts of datagenerated from the 3D measurement systems these methods are necessary toimprove the point cloud processing, transmission and archiving. This thesisintroduces point cloud processing methods as a novel framework for thedigitisation of archeological excavations. The framework replaces theconventional documentation methods for excavation sites. It employs pointclouds for the generation of the digital documentation of an excavation withthe help of an archeologist on-site. The 3D point cloud is used not only fordata representation but also for analysis and knowledge generation. Finally,this thesis presents an autonomous indoor mobile mapping system. The mapping systemfocuses on the sensor placement planning method. Capturing a completeenvironment requires several scans. The sensor placement planning method solvesfor the minimum required scans to digitise large environments. Combining thismethod with a navigation system on a mobile robot platform enables it toacquire data fully autonomously. This thesis introduces a novel hole detectionmethod for point clouds to detect obscured parts of a captured environment. Thesensor placement planning method selects the next scan position with the mostcoverage of the obscured environment. This reduces the required number of scans.The navigation system on the robot platform consist of path planning, pathfollowing and obstacle avoidance. This guarantees the safe navigation of themobile robot platform between the scan positions. The sensor placement planningmethod is designed as a stand alone process that could be used with a mobilerobot platform for autonomous mapping of an environment or as an assistant toolfor the surveyor on scanning projects.

1. 引言

2. 点云表示

3. 点云配准

4. 点云降冗

5. 点云压缩

6. 应用：点云处理在考古学中的应用

7. 应用：一个完全自主的室内移动地图系统

8. 结论

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