一种分步式建筑物屋顶面点云高精度提取方法

A Stepwise High-Precision Extraction for Point Cloud of Building Roof

针对现有机载激光雷达(LiDAR)点云高精度提取方法存在建筑物屋顶面提取精度较低、适应性较差等问题,提出一种分步式建筑物屋顶面点云高精度提取方法。该方法通过主成分分析计算点云可靠性指标,选取可靠平面点;然后,利用K-means算法实现可靠点在法向量空间上的聚类,并通过逐步平面估计,提取初始屋顶面片;最后,进行面片的合并与未标记点的归属判断。实验结果表明,本文方法提取结果优异,效率较高,且对不同复杂程度的建筑物屋顶面均能取得较好的提取效果。

Aiming at the problems of low accuracy and poor adaptability in extracting building roof using LiDAR point cloud data,we propose a stepwise method for high-precision extraction of building roof point clouds.We calculate the reliability index of the point cloud through principal component analysis,select the reliable plane points,then use the K-means algorithm to realize the clustering of the reliable points in the normal vector space and extract the initial roof patch through stepwise plane estimation.Finally,we process the combination of building roof patches and the attribution judgment of unmarked points.The test results show that the proposed method has excellent extraction results,high extraction efficiency,and can obtain better extraction results for building roofs of different complexity levels.