Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense trees. We propose an automatic, robust, and efficient method for the registration of forest point clouds. Our approach first locates tree stems from raw point clouds and then matches the stems based on their relative spatial relationship to determine the registration transformation. The algorithm requires no extra individual tree attributes and has quadratic complexity to the number of trees in the environment, allowing it to align point clouds of large forest environments. Extensive experiments on forest terrestrial point clouds have revealed that our method inherits the effectiveness and robustness of the stem-based registration strategy while exceedingly increasing its efficiency. Besides, we introduce a new benchmark dataset that complements the very few existing open datasets for the development and evaluation of registration methods for forest point clouds. The source code of our method and the dataset are available at https://github.com/zexinyang/GlobalMatch.

翻译：点云配准是激光雷达在精准林业应用中不可或缺的前提条件。现有森林点云配准方法需要提取单木属性，在处理密集林木的真实森林点云时存在效率瓶颈。我们提出一种自动、鲁棒且高效的森林点云配准方法。该方法首先从原始点云中定位树干，然后基于树干间的相对空间关系进行匹配，求解配准变换。算法无需额外的单木属性，计算复杂度与环境树木数量呈二次关系，能够实现大规模森林环境点云的对齐。在森林地面点云上的大量实验表明，该方法继承了基于树干配准策略的有效性与鲁棒性，同时显著提升效率。此外，我们引入了一个新的基准数据集，以补充现有极少的用于森林点云配准方法开发与评估的开放数据集。本方法源代码及数据集可于https://github.com/zexinyang/GlobalMatch获取。