Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) registration is an essential method for acquiring comprehensive forest structural information and conducting forest resource inventories. Due to the sparsity of the understory point cloud in ULS, existing methods for forest area point cloud registration have limitations in processing. To address this issue, this study proposes an efficient and robust coarse registration algorithm for forest area TLS-ULS point clouds. First, the tree top points are obtained based on the neighborhood maximum and H eight And angle threshold constraint methods, which are used as keypoints to construct an irregular triangular mesh. Line segment feature descriptors are then constructed for each mesh edge to establish matching relationships for registration transformation. The experimental results obtained using multiple airborne and terrestrial point cloud datasets from different regions demonstrate that the proposed algorithm does not rely on tree trunk attributes and has no strict density requirements for airborne point clouds. High registration accuracy is achieved for eight test plots in two study areas, with translation and rotation errors of 0.28° and 0.12 m, respectively, and an average pointwise error of 0.14 m. This indicates that the proposed algorithm has high registration accuracy and strong robustness, making it suitable for TLS-ULS registration in forest scenes.

中文翻译：

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基于线段描述符的森林TLS-ULS点云高效粗配准


无人机激光扫描（ULS）和地面激光扫描（TLS）登记是获取全面的森林结构信息和进行森林资源清查的重要方法。由于ULS中林下点云的稀疏性，现有的林区点云配准方法在处理上存在局限性。为了解决这个问题，本研究提出了一种高效、鲁棒的林区 TLS-ULS 点云粗配准算法。首先，基于邻域最大值和H八角阈值约束方法获得树顶点，将其作为关键点构建不规则三角形网格。然后为每个网格边缘构建线段特征描述符，以建立配准变换的匹配关系。使用来自不同地区的多个机载和地面点云数据集获得的实验结果表明，该算法不依赖于树干属性，并且对机载点云没有严格的密度要求。两个研究区域的 8 个测试样地实现了较高的配准精度，平移和旋转误差分别为 0.28° 和 0.12 m，平均逐点误差为 0.14 m。这表明该算法具有较高的配准精度和较强的鲁棒性，适合森林场景中的TLS-ULS配准。