摘要
目的机载激光雷达(light detection and ranging,LiDAR)能够快速获取建筑物表面的3维点云,为提取建筑物轮廓提供重要的数据支撑,但由于激光脚点的随机性和点云自身的离散性,常规固定半径Alpha Shapes(A-Shapes)算法难以兼顾轮廓提取的精细度和完整度,且在点数量较大情况下计算效率较低。因此,提出一种基于网格的可变半径Alpha Shapes方法用于提取机载LiDAR点云建筑物轮廓。方法对3维点云进行投影降维,对投影后2维离散点的范围构建规则格网,接着根据网格内点云填充情况筛选出边界网格,计算边界网格的平滑度并加权不同的滚动圆半径,再以边界网格为中心生成3×3邻域网格检测窗口,利用滚动圆原理提取窗口内点集的边界点,迭代检测直到所有边界网格遍历完成,最后获取点云的完整轮廓。结果在精度评价实验中,与固定半径A-Shapes方法和可变半径Alpha Shapes(variable radius Alpha Shapes, VA-Shapes)方法相比,若建筑物以直线特征为主且边缘点云参差不齐,则本文方法的提取效果不理想;若建筑物含有较多拐角特征,则本文方法的提取效果较好。在效率评价实验中,与A-Shapes方法、VA-Shapse方法以及包裹圆方法相比,若点云数据量较小,则4种方法的耗时差距不大;若数据量较大,则本文方法和包裹圆方法的耗时远小于固定半径A-Shapes方法。实验结果表明,本文提出的轮廓提取方法适用于多种形状的建筑物点云。从轮廓完整性、几何精度以及计算效率等几方面综合考虑,本文方法提取建筑物点云轮廓效果较好。结论本文提出的基于网格的可变半径Alpha Shapes建筑物点云轮廓提取方法结合了网格划分和滚动圆检测的优点,能够有效提取机载LiDAR建筑物点云顶部轮廓,具有较高的提取效率和良好的鲁棒性,提取的轮廓精度较高。
Objective Buildings are major spatial elements in urban areas, and 3D building models are significant for construction of intelligent cities. Airborne light detection and ranging(LiDAR) has the advantages of low operation cost, fast acquisition, and all-weather access to the point cloud with high accuracy. As a high-quality data source, the airborne LiDAR point cloud provides convenience for building extraction, feature recognition, and 3D model reconstruction. Extracting accurate and complete building contours from the point cloud is important. However, due to the dispersion and randomness of scanning point, it is hard to balance the contour accuracy and completeness by using conventional fixed radius Alpha Shapes(A-Shapes) algorithm in extracting point cloud building contours. Moreover, in the case of large amount of data,the computational efficiency is relatively low, and the detection is time-consuming. The boundary extraction algorithm of variable rolling circle radius based on 2D grids is proposed to extract the rooftop contour of airborne LiDAR data of buildings.Method The proposed method consists of several steps. First, the origin point cloud is projected to the 2D plane, and a 2D grid structure is obtained by dividing the entire point cloud into the regular grid net. Boundary grids that contain LiDAR points can be selected by conducting 8-neighborhood detection. Then, the smoothness of each boundary grid can be calculated by using the connect line of the gravity center of discrete points in each grid. On the basis of the smoothness of the boundary grid, the multi-level radius value of the rolling circle can be determined adaptively. The 3 × 3 grid detection window should be generated according to the boundary grid. Moreover, all points in the detection window can be detected by using the rolling circle principle of Alpha Shapes algorithm, and the boundary points in the detection windows can be extracted according to the moving track of the rolling circle. The detection window is iteratively moving one
作者
伍阳
王丽妍
胡春霞
程亮
Wu Yang;Wang Liyan;Hu Chunxia;Cheng Liang(The 28th Research Institute of China Electronics Technology Group Corporation,Nanjing 210007,China;Nanjing Institute of Surveying Mapping&Geotechnical Investigation.Co.Ltd.,Nanjing 210019,China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,Nanjing University,Nanjing 210023,China)
出处
《中国图象图形学报》
CSCD
北大核心
2021年第4期910-923,共14页
Journal of Image and Graphics
基金
国家自然科学基金项目(41622109)
南京市测绘勘察研究院股份有限公司科研项目(2019RD09)。
