Line Segment Descriptor-Based Efficient Coarse Registration for Forest TLS-ULS Point Clouds
Xiaoyang Wu, Xiaohuan Xi, Chengwen Luo, Cheng Wang, Sheng Nie · IEEE Transactions on Geoscience and Remote Sensing · 2024
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 HeightAnd 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.