Automated Registration of Terrestrial Point Clouds Through Ground Overlapping Searching in Forests

Terrestrial laser scanning (TLS) technology has been demonstrated to be able to measure forest structure fast, nondestructively, and with high precision. The registration of point cloud data from different scans is a prerequisite for an in-depth understanding of forest structure. Currently, automated registration methods for forest point clouds typically rely on tree attributes (such as tree position and stem diameter). However, these methods are often suitable for easy forest conditions, where the occlusion effects are less significant and the overlapped areas between scans are large. This article proposes an automated point cloud registration method utilizing ground points without the need for extracting individual tree attributes. In the state-of-the-art hardware setup, the scanner automatically maintains a level position during data acquisition. Therefore, the differences in z-coordinates between the corresponding points in different scans should be equal. Leveraging this property and the rhombic region correspondence principle (RRCP), the proposed method identifies the overlapping rhombic region in the target and reference TLS data, which is located in the middle of the two scanning positions in the plot. Points within the overlapping rhombic region are used as registration primitives. Experiments were carried out in 24 plots with diverse stem densities, tree species, and altitudes located in two test areas in Jiangxi and Zhejiang, China. The resulting average pointwise error, average translation error, and average rotation error of TLS-TLS forest point cloud registrations are 5.77 cm, 4.92 cm, and 3.37 mrad. The results showed the potential of applying ground points for scan-to-scan registration.