露天煤矿场景车载LiDAR点云台阶边缘线检测

On-board LiDAR point cloud step edge line detection in open-pit coal mine scenes

露天煤矿开采过程中,矿山台阶边缘线的确定十分重要,传统矿山台阶边缘线量测方法工作量大,精度低、工作效率低下,降低了矿山地形建模效率和验收精度。因此,本文基于车载激光雷达生成露天煤矿密集点云数据,构建一种台阶边缘线提取方法,利用渐进形态学滤波对点云数据进行预处理,通过球半径滤波器、点云聚类、内在形状特征检测算法提取出点云数据中的台阶边缘线特征点,再通过样条拟合生成台阶边缘线。该方法可自动、高效地提取露天煤矿山台阶边缘线,可为矿山三维场景建模和开采监测提供技术支撑。

During the mining process of open-pit coal mines,the determination of the edge lines of mining benches is very important and has important value for mining open-pit coal mines.Traditional methods for measuring the edge lines of mining benches have large workload,low accuracy,and low work efficiency,reducing the efficiency of mining terrain modeling and acceptance accuracy.Therefore,this paper constructs a step edge line extraction method based on the generation of dense point cloud data from open-pit coal mines using a vehicular laser radar,preprocesses the point cloud data using progressive morphological filtering,extracts step edge line feature points from the point cloud data through a ball radius filter,point cloud clustering,and intrinsic shape feature detection algorithm,and then generates step edge lines through spline fitting.Experiments show that this method can automatically and efficiently extract the edge lines of open-pit coal mine steps,and can provide technical support for 3D scene modeling and mining monitoring in mines.