露天矿点云数据中台阶线提取

台阶线信息对于露天开采具有重要价值，现有获取台阶线的方法工作量大、效率低、精度差，降低了矿山的生产效率和验收精度.因此，本文基于序列无人机影像生成的露天矿密集点云数据，研究并提出了一种自动提取露天矿台阶线的方法.该方法利用渐进形态学滤波算法对点云进行预处理，提出一种顾及邻域几何属性的三维边缘检测与曲率指数加权方法提取出台阶线特征点，并使用移动最小二乘法精确拟合出台阶线.实验结果表明该算法可以自动、高效、精确地提取出露天矿台阶线，生成露天开采现状图，对于露天矿生产和安全具有重要的应用价值.

Step Line Extraction from Point Cloud Data of Open-Pit Mine

The information of step line is of great importance to open-pit mining. The existing method of obtaining step line has large workload， low efficiency and poor accuracy， which reduces the production efficiency and acceptance accuracy of the mine.Thus， a method of automatically extracting open-pit mine step lines from the dense point cloud data of open-pit mines generated by sequence UAV images is proposed in this paper. This method uses progressive morphological filtering algorithm to preprocess the point cloud， and a three-dimensional edge detection and curvature index weighting method that takes into account the geometric properties of the neighborhood is proposed to extract the feature points of the step line， then， it uses the moving least squares method to accurately fit the step line. The experimental results show that the algorithm can automatically， efficiently and accurately extract the step line of the open-pit mine， and generate open-pit mining status map. It has important application value for open-pit mine production and safety.