Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy.

Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions.

从地块收集的精确结构信息对于森林资源的管理和决策具有重要意义。当前，激光扫描被广泛用于林业清单中以获取三维（3D）结构信息。在基于地面的森林测量中，共有三种主要的数据采集模式：单次扫描陆地激光扫描（TLS），多次扫描TLS和多次单次扫描TLS。然而，每种模式都会给森林测量带来特殊的困难。由于遮挡效应，单次扫描TLS模式仅对树的一侧进行扫描。多次扫描TLS模式克服了遮挡问题，但是以更长的获取时间，更多的人工和更多的数据预处理工作为代价。多单次扫描TLS模式减少了工作量和遮挡效果，但缺乏对森林的完整3D重建。使用移动激光扫描（MLS）可以很大程度上避免TLS方法中的这些问题；然而，森林的几何特性（例如，树木形状，位置和遮挡之间的相似性）使运动估计变得复杂，并降低了制图精度。

因此，本文提出了一种结合单扫描TLS和MLS进行森林3D数据采集的新方法。我们使用单扫描TLS数据作为参考，在其上注册了MLS点云，因此它们填补了单扫描TLS数据的遗漏。为了在参考上注册MLS点云，我们提取了对树茎中心线进行采样的虚拟特征点，并提出了一个基于优化的新注册框架。与以前的基于MLS的研究相比，提出的方法充分利用了树木的自然几何特征。我们在三个数据集中展示了所提方法的有效性，鲁棒性和准确性，并从中提取了结构信息。实验结果表明，一次扫描导致的树茎数据遗漏可以通过MLS数据得到补偿，现场测量的时间比多重扫描TLS模式的时间要少得多。此外，单次扫描TLS数据为基于MLS的森林映射提供了强大的全局约束，从而可以实现较低的映射误差，例如，水平和垂直方向的平均误差均小于2.0 cm。