Robust Multisource Forest Point Cloud Registration With Distribution Similarity Analysis

Aerial and terrestrial laser scanning (TLS) technologies offer complementary, high-precision 3-D data on forest structure. Registering point cloud data from multiplatform is crucial for achieving a more comprehensive understanding of forest structure. Currently, multisource forest point cloud registration remains challenging due to factors such as unstable point- and object-level features, varying observation perspectives, and nonstandardized processing pipelines. To address these challenges, this study introduces a unified and automated method for registering aerial and ground-based point clouds in forest areas. First, keypoints are extracted from multisource point clouds using fuzzy c-means (FCM) clustering. Local transformation is then derived from the keypoint sets using the gradient-based local convergence (GLC) algorithm, which is integrated with a nested branch and bound (BnB) structure for further optimization. We also developed a novel distribution similarity index (DSI) to evaluate the alignment of the keypoint sets and determine the initial transformation. Finally, this initial transformation is applied to the ground-based point cloud and refined using the GLC algorithm. Compared with existing methods that rely on point and object features, the proposed method does not depend on geometric descriptions or tree attributes (e.g., tree position and canopy structure). It also demonstrates robustness to variations in the initial position of the point clouds. Testing on 15 datasets with varying plot sizes and tree characteristics shows that the proposed method achieves comparable or superior performance to existing methods, with an average distance residual of 6.59 cm and an average runtime of 90.04 s.