Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional fore...Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical structure.TLS data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual trees.However,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex environments.In this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,China.First,the point normal vector and its Z-axis component are used as trunk-growth constraints.Then,the points surrounding the trunk are searched to account for regrowth.Finally,the nearest distributed branch and leaf points are used to complete the individual tree segmentation.The results show that the TG method can effectively segment individual trees with an average F-score of 0.96.The proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural forests.The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.展开更多
An approach is presented to generate rough interference-free tool-paths directly from massive unorganized data in rough machining that is performed by machining volumes of material in a slice-by-slice manner.Unorganiz...An approach is presented to generate rough interference-free tool-paths directly from massive unorganized data in rough machining that is performed by machining volumes of material in a slice-by-slice manner.Unorganized point-cloud is firstly converted to cross-section data.Then a robust data-structure named tool-path net is constructed to save tool-path data.Optimal algorithms for partitioning sub-cut-areas and computing interference-free cutter-locations are put forward.Finally the tool-paths are linked in a zigzag milling mode,which can be transformed into a traveling sales man problem.The experiment indicates optimal tool paths can be acquired,and high computation efficiency can be obtained and interference can be avoided successfully.展开更多
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
基金The work was supported by the National Natural Science Foundation of China(Grant Number 41961060)the Key Program of Basic Research of Yunnan Province,China(Grant Number 2019FA017)+1 种基金the Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China(Grant Number 2018YFE0184300)the Program for Innovative Research Team in Science and Technology research and innovation fund(ysdyjs 2020058)in the University of Yunnan Province.
文摘Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical structure.TLS data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual trees.However,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex environments.In this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,China.First,the point normal vector and its Z-axis component are used as trunk-growth constraints.Then,the points surrounding the trunk are searched to account for regrowth.Finally,the nearest distributed branch and leaf points are used to complete the individual tree segmentation.The results show that the TG method can effectively segment individual trees with an average F-score of 0.96.The proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural forests.The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.
文摘An approach is presented to generate rough interference-free tool-paths directly from massive unorganized data in rough machining that is performed by machining volumes of material in a slice-by-slice manner.Unorganized point-cloud is firstly converted to cross-section data.Then a robust data-structure named tool-path net is constructed to save tool-path data.Optimal algorithms for partitioning sub-cut-areas and computing interference-free cutter-locations are put forward.Finally the tool-paths are linked in a zigzag milling mode,which can be transformed into a traveling sales man problem.The experiment indicates optimal tool paths can be acquired,and high computation efficiency can be obtained and interference can be avoided successfully.
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.
