In recent years, terrestrial laser scanning technology has been widely used to collect tree point cloud data, aiding in measurements of diameter at breast height, biomass, and other forestry survey data. Since a single scan from terrestrial laser systems captures data from only one angle, multiple scans must be registered and fused to obtain complete tree point cloud data. This paper proposes a marker-free automatic registration method for single-tree point clouds based on similar tetrahedras. First, two point clouds from two scans of the same tree are used to generate tree skeletons, and key point sets are constructed from these skeletons. Tetrahedra are then filtered and matched according to similarity principles, with the vertices of these two matched tetrahedras selected as matching point pairs, thus completing the coarse registration of the point clouds from the two scans. Subsequently, the ICP method is applied to the coarse-registered leaf point clouds to obtain fine registration parameters, completing the precise registration of the two tree point clouds. Experiments were conducted using terrestrial laser scanning data from eight trees, each from different species and with varying shapes. The proposed method was evaluated using RMSE and Hausdorff distance, compared against the traditional ICP and NDT methods. The experimental results demonstrate that the proposed method significantly outperforms both ICP and NDT in registration accuracy, achieving speeds up to 593 times and 113 times faster than ICP and NDT, respectively. In summary, the proposed method shows good robustness in single-tree point cloud registration, with significant advantages in accuracy and speed compared to traditional ICP and NDT methods, indicating excellent application prospects in practical registration scenarios.

翻译：近年来，地面激光扫描技术被广泛应用于采集树木点云数据，辅助测量胸径、生物量等林业调查数据。由于地面激光系统单次扫描仅能从一个角度获取数据，必须通过多次扫描的配准与融合才能获得完整的树木点云数据。本文提出一种基于相似四面体的单木点云无标记自动配准方法。首先，利用同一树木两次扫描的点云分别生成树木骨架，并基于骨架构建关键点集；随后根据相似性原则筛选并匹配四面体，将匹配成功的两个四面体顶点作为匹配点对，从而完成两次扫描点云的粗配准；接着对粗配准后的叶片点云应用ICP方法获取精配准参数，完成两棵树木点云的精确配准。实验采用八棵不同树种、形态各异的树木地面激光扫描数据，使用RMSE与Hausdorff距离作为评价指标，将所提方法与传统的ICP及NDT方法进行对比。实验结果表明，所提方法在配准精度上显著优于ICP与NDT方法，且配准速度分别最高可达ICP与NDT方法的593倍和113倍。综上所述，所提方法在单木点云配准中具有良好的鲁棒性，在精度与速度方面相较传统ICP与NDT方法具有显著优势，在实际配准场景中展现出良好的应用前景。