A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments

Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV-lidar) and terrestrial lidar (T-lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV-lidar and T-lidar data in forest scenes. It employs density-based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst-pattern-based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T-lidar and UAV-lidar registration over forest environments.