Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) are two effective platforms for acquiring forest point clouds. TLS has an advantage in the acquisition of below-canopy information but does not include the data above the canopy. UAVs acquire data from the top viewpoint but are confined to the information above the canopy. To obtain complete forest point clouds and exploit the application potential of multiple platforms in large-scale forest scenarios, we propose a practical pipeline to register multisource point clouds automatically. We consider the spatial distribution differences of trees and achieve the coarse alignment of multisource point clouds without artificial markers; then, the iterative closest point method is used to improve the alignment accuracy. Finally, a graph-based adjustment is designed to remove accumulative errors and achieve the gapless registration. The experimental results indicate high efficiency and accuracy of the proposed method. The mean errors for the registration of multi-scan TLS point clouds subsampled at 0.03 m are approximately 0.01 m, and the mean errors for registration of the TLS and UAV data are less than 0.03 m in the horizontal direction and approximately 0.01 m in the vertical direction.

中文翻译：

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使用粗到全局调整策略自动对多源森林点云进行无标记注册

陆地激光扫描（TLS）和无人机（UAV）是获取林点云的两个有效平台。TLS在获取树冠下信息方面具有优势，但不包括树冠上的数据。无人机从顶部视角获取数据，但仅限于顶篷上方的信息。为了获得完整的森林点云并充分利用多个平台在大规模森林场景中的应用潜力，我们提出了一种实用的管道来自动注册多源点云。我们考虑了树木的空间分布差异，并在没有人工标记的情况下实现了多源点云的粗略对齐；然后，采用迭代最近点法提高对准精度。最后，基于图形的调整旨在消除累积误差并实现无缝隙配准。实验结果表明，该方法具有较高的效率和准确性。在0.03 m处二次采样的多扫描TLS点云的配准平均误差约为0.01 m，TLS和UAV数据配准的平均误差在水平方向上小于0.03 m，在垂直方向上小于0.01 m方向。