Point Cloud Registration Based on Fast Point Feature Histogram Descriptors for 3D Reconstruction of Trees

Abstract

Three-dimensional (3D) reconstruction is an essential technique to visualize and monitor the growth of agricultural and forestry plants. However, inspecting tall plants (trees) remains a challenging task for single-camera systems. A combination of low-altitude remote sensing (an unmanned aerial vehicle) and a terrestrial capture platform (a mobile robot) is suggested to obtain the overall structural features of trees including the trunk and crown. To address the registration problem of the point clouds from different sensors, a registration method based on a fast point feature histogram (FPFH) is proposed to align the tree point clouds captured by terrestrial and airborne sensors. Normal vectors are extracted to define a Darboux coordinate frame whereby FPFH is calculated. The initial correspondences of point cloud pairs are calculated according to the Bhattacharyya distance. Reliable matching point pairs are then selected via random sample consensus. Finally, the 3D transformation is solved by singular value decomposition. For verification, experiments are conducted with real-world data. In the registration experiment on noisy and partial data, the root-mean-square error of the proposed method is 0.35% and 1.18% of SAC-IA and SAC-IA + ICP, respectively. The proposed method is useful for the extraction, monitoring, and analysis of plant phenotypes.