Automatic marker-free registration of single tree point-cloud data based on rotating projection

作     者：Xiuxian Xu Pei Wang Xiaozheng Gan Jingqian Sun Yaxin Li Li Zhang Qing Zhang Mei Zhou Yinghui Zhao Xinwei Li 

作者机构：School of ScienceBeijing Forestry UniversityBeijing 100083China Key Laboratory of Quantitative Remote Sensing Information TechnologyAcademy of Opto-ElectronicsBeijing 100094China Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China 

出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)

年 卷 期：2022年第6卷第1期

页      面：176-188页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：funded by the Fundamental Research Funds for the Central Universities(No.2021ZY92) National Students'innovation and entrepreneurship training program(No.201710022076) the State Scholarship Fund from China Scholarship Council(CSC No.201806515050) 

主　　题：Coarse registration Feature-point matching Fine registration Multi-station tree point cloud Point-cloud registration 

摘      要：Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry *** scans are generally used to overcome occlusion effects and obtain complete tree structural ***,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and *** this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was *** coarse registration,point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional(2D)images,which are used to estimate the initial positions of multiple *** feature-point pairs are then extracted from these series of 2D *** fine registration,point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation *** evaluate the accuracy of registration results,we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent *** accurate evaluation,we conducted experiments on two simulated trees and six real-world *** registration errors of the proposed method were 0.026 m around on simulated tree point clouds,and 0.049 m around on real-world tree point clouds.