Multisource forest point cloud registration with semantic-guided keypoints and robust RANSAC mechanisms

The Increasing availabilities of Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) have advanced accurate and detailed forest structural measurements. Registration of multi-perspective observations from different platforms is a prerequisite for a comprehensive forest structure understanding. Currently, forest point cloud registrations are typically done by single tree attribute-based (e.g. tree location, stem diameter) methods, which suffer from various and complex forest compositions and terrains, and can be unreliable for forests with regular tree layouts and insufficient common trees. Therefore, this study presents a unified and marker-free framework for both TLS-TLS and ULS-TLS point cloud registrations in forest areas. First, motivated by wood-leaf separation, semantic-guided keypoints from TLS and ULS point clouds are detected by utilizing a wood response indicator (WRI), which represents reliable repeatability between different views of forest point clouds. Second, an initial correspondence set is generated by using the WRI filter and Binary Shape Context (BSC) descriptor matching. Finally, the initial correspondence set is trimmed and optimized through a robust RANSAC mechanism, consisting of a geometric compatibility filter to guide and trim the hypothesis generation process and a modified hypothesis evaluation step to further optimize the transformation. Experiments have been conducted on six plots involving diverse stem densities and tree species located in forest farms in Guangxi, China. The resulting average registration residual and runtime are 0.049 m and 93 s for TLS-TLS scenes, 0.299 m and 242 s for ULS-TLS scenes, respectively. Comprehensive comparisons demonstrate that the proposed method outperforms other baselines. It is demonstrated that the proposed framework can improve the practicality and efficiency of multi-source data collection and registration, thus facilitating the application of TLS and ULS in forest ecosystem science.