Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural infor...Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information.However,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult.In this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was proposed.In 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 scans.Corresponding feature-point pairs are then extracted from these series of 2D images.In 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 parameters.To 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 scans.For accurate evaluation,we conducted experiments on two simulated trees and six real-world trees.Average 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.展开更多
鉴于剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm,PPA)准确性差、不能提取特殊地形等问题,提出了一种结合形态学的剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm ba...鉴于剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm,PPA)准确性差、不能提取特殊地形等问题,提出了一种结合形态学的剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm based on morphology erosion,MEPPA).通过剖面识别提取原始的骨架特征候选点,根据方向系数连接成多边形条带,在此基础上提出了生成标量特征域的填充算法;引入形态学区域细化思想,提出了形态编码和骨架特征形态简化算法,将特征域简化为骨架特征线;为了满足各领域对矢量骨架特征的需求,提出了标量特征线复原、检测与优化剔除等策略,准确地复原了矢量骨架特征模型;提出了保留外分支和环路特征的解决方案,解决了传统骨架特征提取方法不能保留较长的主干线以及不能提取环路地形特征的问题.在真实数据上进行了实验研究,结果表明提取的骨架特征形态的整体效果优于传统方法.展开更多
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).
文摘Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information.However,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult.In this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was proposed.In 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 scans.Corresponding feature-point pairs are then extracted from these series of 2D images.In 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 parameters.To 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 scans.For accurate evaluation,we conducted experiments on two simulated trees and six real-world trees.Average 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.
文摘鉴于剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm,PPA)准确性差、不能提取特殊地形等问题,提出了一种结合形态学的剖面识别骨架特征提取方法(profile recognition and polygon breaking algorithm based on morphology erosion,MEPPA).通过剖面识别提取原始的骨架特征候选点,根据方向系数连接成多边形条带,在此基础上提出了生成标量特征域的填充算法;引入形态学区域细化思想,提出了形态编码和骨架特征形态简化算法,将特征域简化为骨架特征线;为了满足各领域对矢量骨架特征的需求,提出了标量特征线复原、检测与优化剔除等策略,准确地复原了矢量骨架特征模型;提出了保留外分支和环路特征的解决方案,解决了传统骨架特征提取方法不能保留较长的主干线以及不能提取环路地形特征的问题.在真实数据上进行了实验研究,结果表明提取的骨架特征形态的整体效果优于传统方法.
