This study had two main aims: (1) to provide a comprehensive review of terrestrial laser scanner (TLS) point cloud registration methods and a better understanding of their strengths and weaknesses; and (2) to provide a large-scale benchmark data set (Wuhan University TLS: Whu-TLS) to support the development of cutting-edge TLS point cloud registration methods, especially deep learning-based methods. In particular, we first conducted a thorough review of TLS point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends. We then reviewed the existing benchmark data sets (e.g., ETH Dataset and Robotic 3D Scanning Repository) for TLS point cloud registration and summarized their limitations. Finally, a new benchmark data set was assembled from 11 different environments (i.e., subway station, high-speed railway platform, mountain, forest, park, campus, residence, riverbank, heritage building, underground excavation, and tunnel environments) with variations in the point density, clutter, and occlusion. In addition, we summarized future research trends in this area, including auxiliary data-guided registration, deep learning-based registration, and multi-temporal point cloud registration.

这项研究有两个主要目的：（1）对陆地激光扫描仪（TLS）点云注册方法进行全面综述，并更好地了解其优缺点；（2）提供大规模基准数据集（武汉大学TLS：Whu-TLS），以支持开发最新的TLS点云注册方法，尤其是基于深度学习的方法。特别是，我们首先从成对粗注册，成对精注册和多视图注册方面全面回顾了TLS点云注册方法，并分析了它们的优缺点和未来的研究趋势。然后，我们回顾了用于TLS点云注册的现有基准数据集（例如，ETH数据集和机器人3D扫描存储库），并总结了它们的局限性。最后，在11个不同的环境（例如，地铁站，高速铁路平台，山脉，森林，公园，校园，住宅，河岸，历史建筑，地下挖掘和隧道环境）中组装了一个新的基准数据集密度，混乱和遮挡。此外，我们总结了该领域的未来研究趋势，包括辅助数据指导的注册，基于深度学习的注册和多时间点云注册。