An Improved RANSAC-ICP Method for Registration of SLAM and UAV-LiDAR Point Cloud at Plot Scale

Abstract

Simultaneous Localization and Mapping (SLAM) using LiDAR technology can acquire the point cloud below the tree canopy efficiently in real time, and the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) can derive the point cloud of the tree canopy. By registering them, the complete 3D structural information of the trees can be obtained for the forest inventory. To this end, an improved RANSAC-ICP algorithm for registration of SLAM and UAV-LiDAR point cloud at plot scale is proposed in this study. Firstly, the point cloud features are extracted and transformed into 33-dimensional feature vectors by using the feature descriptor FPFH, and the corresponding point pairs are determined by bidirectional feature matching. Then, the RANSAC algorithm is employed to compute the transformation matrix based on the reduced set of feature points for coarse registration of the point cloud. Finally, the iterative closest point algorithm is used to iterate the transformation matrix to achieve precise registration of the SLAM and UAV-LiDAR point cloud. The proposed algorithm is validated on both coniferous and broadleaf forest datasets, with an average mean absolute distance (MAD) of 11.332 cm for the broadleaf forest dataset and 6.150 cm for the coniferous forest dataset. The experimental results show that the proposed method in this study can be effectively applied to the forest plot scale for the precise alignment of multi-platform point clouds.