Low Overlapping Plant Point Cloud Registration and Splicing Method Based on FPFH

Abstract

AbstractThree-dimensional point clouds captured by sensors can quantify plant phenotype, which plays an important role in agricultural intelligence. Many scanned objects in agriculture and forestry are tall and obscured by leaves, so point clouds captured by either terrestrial or airborne methods may be incomplete. In order to obtain a more complete point cloud, this paper proposes a point cloud registration method based on the fast point feature histogram (FPFH), which aligns point clouds collected from different viewpoints. This method calculates the FPFH feature of each point and the Bhattacharyya distance between point pairs. Effective strategies pick out reliable sets of point pairs. Singular value decomposition is used to obtain the transformation relationship between point clouds. Experimental results show that the proposed method has high accuracy for plant point cloud registration in real scenes, and the root-mean-square error is smaller than that of common registration methods of SAC-IA and NDT.KeywordsFast point feature histogramBhattacharyya distanceRandom sample consensusPoint cloud registration