Terrestrial Laser Scanning (TLS) is an efficient and reliable method for collecting point clouds which have a range of applications in the Architecture, Engineering and Construction (AEC) domain. To ensure that the acquired point clouds are suitable to any given application, data collection must guarantee that all scanning targets are acquired with the specified data quality, and within time limits. Efficiency of data collection is important to reduce jobsite activity disruptions. Effective and efficient laser scanning data collection can be achieved through a prior planning optimisation process, which can be called Planning for Scanning (P4S). In the construction domain, the P4S problem has attracted increasing interest from the research community and a number of approaches have been proposed.

This manuscript presents a systematic review of prior P4S works in the AEC domain and presents a categorisation of point cloud data quality criteria. The review starts with the identification and grouping in three categories of the point cloud data quality criteria that are commonly considered as constraints to the P4S problem. The three categories of data quality criteria include 1) completeness, 2) accuracy and spatial resolution, and 3) ‘registrability’. The prior P4S works are then reviewed in a structured way by contrasting them in the way they formulate the P4S optimisation problem: the type of inputs they assume (model and possible scanning locations), the constraints they consider, and the algorithm they utilise to solve the optimisation problem. This work makes two contributions: (1) it identifies gaps in knowledge that require further research such as the need to establish a fully automated scan plan which provides the optimum coverage in construction domain specifically for indoor construction; and (2) it provides a framework — principally a set of criteria — for others to compare new P4S methods against the existing state of the art in the field. This will not only be valuable for young researchers who want to start research in solving the P4S problem, but also for the ones already working in the domain to rethink the problem from different perspectives.

地面激光扫描（TLS）是一种有效且可靠的方法，用于收集点云，这些点云在建筑，工程和构造（AEC）领域中具有广泛的应用。为了确保所采集的点云适用于任何给定的应用程序，数据收集必须保证在指定的时间范围内以指定的数据质量采集所有扫描目标。数据收集的效率对于减少作业现场的活动中断很重要。可以通过先前的计划优化过程来实现有效而高效的激光扫描数据收集，该过程可以称为扫描计划（P4S）。在建筑领域，P4S问题引起了研究界的越来越多的关注，并提出了许多方法。

该手稿对AEC领域中以前的P4S工作进行了系统的回顾，并对点云数据质量标准进行了分类。审查从识别和分组点云数据质量标准的三类开始，这些标准通常被认为是P4S问题的约束。数据质量标准的三类包括1）完整性，2）准确性和空间分辨率，以及3）“可注册性”。然后，通过以结构化的方式对先前的P4S作品进行审查，方法是将它们与他们制定P4S优化问题的方式进行对比：他们假设的输入类型（模型和可能的扫描位置），考虑的约束条件以及用于解决问题的算法优化问题。这项工作有两个贡献：（1）找出需要进一步研究的知识空白，例如需要建立一个全自动扫描计划，以提供专门针对室内建筑的建筑领域的最佳覆盖范围；（2）它为其他人提供了一个框架-主要是一组标准-以便将新的P4S方法与该领域的现有技术水平进行比较。这不仅对希望开始研究解决P4S问题的年轻研究人员非常有价值，而且对于已经在该领域工作以从不同角度重新思考问题的研究人员来说，也是有价值的。（2）它为其他人提供了一个框架-主要是一组标准-以便将新的P4S方法与该领域的现有技术水平进行比较。这不仅对希望开始研究解决P4S问题的年轻研究人员非常有价值，而且对于已经在该领域工作以从不同角度重新思考问题的研究人员来说，也是有价值的。（2）它为其他人提供了一个框架-主要是一组标准-以便将新的P4S方法与该领域的现有技术水平进行比较。这不仅对希望开始研究解决P4S问题的年轻研究人员非常有价值，而且对于已经在该领域工作以从不同角度重新思考问题的研究人员来说，这是有价值的。