The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a...The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks.展开更多
为了使用四旋翼无人机搭载二维激光雷达进行空间环境探测与建模,设计了无人机LIDAR(Light Detection and Ranging)探测方案,提出了基于欧式聚类与Alpha-shape算法的点云数据建模方法。以室内环境建模为例,通过无人机LIDAR测得室内多位...为了使用四旋翼无人机搭载二维激光雷达进行空间环境探测与建模,设计了无人机LIDAR(Light Detection and Ranging)探测方案,提出了基于欧式聚类与Alpha-shape算法的点云数据建模方法。以室内环境建模为例,通过无人机LIDAR测得室内多位置、多高度的平面点云数据。根据室内环境点云数据分块聚集的特性,对数据进行统计滤波消噪,并采用欧式聚类算法对点云数据进行聚类,对每个聚类分别选取合适的参数α绘制其Alpha-shape图形。对于采样高度均匀、雷达扫描频率稳定的点云数据,考虑到无人机激光雷达的数据特点,以每个聚类中点的数量和其包络在x-y平面的投影面积为参数,结合测量经验提出了α的计算式。利用此方法可以实现使用二维激光雷达进行空间建模,相较于使用三维激光雷达成本更低,测量更灵活。展开更多
基金The National Natural Science Foundation of China(No.52338011)the Start-up Research Fund of Southeast University(No.RF1028624058)+1 种基金the Southeast University Interdisciplinary Research Program for Young Scholarsthe National Key Research and Development Program of China(No.2024YFC3014103).
文摘The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks.
文摘为了使用四旋翼无人机搭载二维激光雷达进行空间环境探测与建模,设计了无人机LIDAR(Light Detection and Ranging)探测方案,提出了基于欧式聚类与Alpha-shape算法的点云数据建模方法。以室内环境建模为例,通过无人机LIDAR测得室内多位置、多高度的平面点云数据。根据室内环境点云数据分块聚集的特性,对数据进行统计滤波消噪,并采用欧式聚类算法对点云数据进行聚类,对每个聚类分别选取合适的参数α绘制其Alpha-shape图形。对于采样高度均匀、雷达扫描频率稳定的点云数据,考虑到无人机激光雷达的数据特点,以每个聚类中点的数量和其包络在x-y平面的投影面积为参数,结合测量经验提出了α的计算式。利用此方法可以实现使用二维激光雷达进行空间建模,相较于使用三维激光雷达成本更低,测量更灵活。
