Terrestrial laser scanning (TLS) generates high-resolution three-dimensional point cloud data, yielding precise tree structures and facilitating a deeper understanding of dynamic changes within forest ecosystems. Point cloud registration is a crucial foundation for forestry applications. This study proposes a marker-free registration method for forest point clouds based on tree branching structures, which reduces the time and labor required in traditional manual registration.

This study proposes an innovative automatic registration method based on tree branching structures. First, during the preprocessing stage, ground points were filtered out, and data volume was reduced through random sampling. A graph-based connectivity method was used to separate woody points from leaf points. Subsequently, a digital terrain model (DTM) was constructed for the scene, and the bottom points were selected for density-based clustering. Two collaborative detection methods were then employed to identify tree trunks, which were used as seed points for single-tree segmentation based on comparing shortest paths. For each individual tree, key points were detected through hierarchical clustering, and key point sets were constructed. Coarse registration was performed using the four-point congruent sets (4PCS) algorithm, followed by fine registration using the point-to-plane iterative closest point (ICP) algorithm to improve registration accuracy.

To validate the proposed method, experiments are conducted using forest point cloud data from Yeyahu National Wetland Park, Haidian Park, and the Tongji Tree dataset, which vary in terms of tree numbers and scene sizes. The Yeyahu and Haidian datasets form the experimental group, while the Tongji Tree dataset is used as an open-source reference. The experimental results show that the proposed method achieves a root mean square error (RMSE) of approximately 2.4 cm and a mean absolute error (MAE) of approximately 2.1 cm (Table 2). The registration results of this method are compared with those of the Super4PCS method, and registration overlap is used as a supplementary metric to evaluate registration performance (Table 3). Compared with other key point detection methods such as SIFT, Harris 3D, and ISS (Fig. 8), the proposed method demonstrates superior performance. It achieves an average overlap of 0.980 across five plots (see Table 4), with an average registration time of 9.74 s (Table 4). The proposed key point extraction method significantly outperforms other methods in terms of registration accuracy and efficiency.

This study proposes an automatic marker-free registration method for terrestrial LiDAR forest point clouds that leverage tree branching structures. The approach involves hierarchical clustering of branches to construct key points sets, followed by coarse registration using the 4PCS algorithm and fine registration of the forest point cloud scene using the point-to-plane ICP method. Experimental results indicate that using tree branch structures as key features enables high accuracy and efficiency in marker-free registration. The presence of sufficient branching features in overlapping areas is critical to ensure registration accuracy and effectiveness.