地面LiDAR数据中建筑轮廓和角点提取

建筑轮廓和角点作为多平台激光雷达数据常用的配准基元,其提取方法正受到越来越多的关注.投影密度法是一种常用的从地面LiDAR数据中提取建筑轮廓和角点的方法,然而以往研究对于直接影响建筑轮廓提取结果的格网密度阈值考虑较少.提出一种轮廓密度估计的方法,能够根据点云实际情况自动准确地计算出格网密度阈值,从而提取较为准确的建筑轮廓格网.在此基础上,利用轮廓线段高程分割和密度延伸的方法对轮廓进行分割和恢复,能够提取完整的建筑轮廓.最后,利用轮廓线段的相交关系获得建筑角点.实验结果表明,本文方法能够有效从地面LiDAR数据中提取建筑轮廓和角点,正确性、完整性和定位精度较高.

Extraction of building contours and corners from terrestrial LiDAR data

Building contours and corners, as common registration primitives of multi-platform LiDAR data, are drawing more and more attraction on their extraction. Density of projected points (DoPP) method is a usual approach for building contours and corners extraction from terrestrial LiDAR, however previous studies paid little attention on the determination of grid density threshold, which directly affects the extraction results. This paper proposes a building contour density estimation method, which can provide a grid density threshold according to the actual point cloud, so that accurate building contours can be extracted. On this basis, the extracted contours are segmented and recovered using contour segmentation and density extension method, thus complete building contours are obtained. Finally, building corners are gained through the intersection of building contours. The experiment shows that the proposed method can effectively extract building contours and corners from terrestrial LiDAR with high correctness, completeness and positioning precision.