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
为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度...为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度的车站周边土地利用特征。结合以上两类特征,建立基于非监督学习K-Means++方法的地铁车站分类模型,将北京地铁307个车站分为7类。根据其客流和周边用地特征分别识别为配套设施开发完善的典型居住型车站,具有商业开发潜力的典型居住型车站,配置一定工作岗位的居住型车站,高度开发的典型工作型车站,职住结合的工作型车站,旅游休闲型的车站,以及尚待开发的远郊车站。经过分析,该分类结果与实际情况高度吻合,验证了模型的有效性,可以为城市规划及车站周边土地开发提供依据。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.
文摘为研究地铁站的精细化分类问题,利用基于出行链分析的通勤出行识别方法筛选通勤客流,结合早晚高峰的进出站客流量,识别车站的职住功能特征;基于百度地图开源平台抓取POI(Point of interest)数据,从用地功能角度进行组合归类,得到细粒度的车站周边土地利用特征。结合以上两类特征,建立基于非监督学习K-Means++方法的地铁车站分类模型,将北京地铁307个车站分为7类。根据其客流和周边用地特征分别识别为配套设施开发完善的典型居住型车站,具有商业开发潜力的典型居住型车站,配置一定工作岗位的居住型车站,高度开发的典型工作型车站,职住结合的工作型车站,旅游休闲型的车站,以及尚待开发的远郊车站。经过分析,该分类结果与实际情况高度吻合,验证了模型的有效性,可以为城市规划及车站周边土地开发提供依据。
