基于无人机点云数据的露天采场矿车提取方法

针对在基于无人机点云数据进行露天采场验收测量过程中，由于矿车点集的存在导致验收精度降低的关键问题，提出了一种露天采场矿车点集自动提取方法.以哑巴岭露天采场无人机点云为数据源，首先利用渐进式形态学滤波算法分割出地面点与非地面点，然后通过改进的欧氏聚类算法对非地面点中的矿车点集进行聚类提取，最后基于国际摄影测量和遥感学会(ISPRS)提出的误差评判标准对矿车提取结果进行评估分析.分析结果表明，该方法可以有效提取露天采场中的矿车点集，为实现露天采场快速高效验收提供了重要的技术支持.

Extraction Method of Open Pit Mine Car Based on UAV Point Cloud Data

In order to solve the key problem that the existence of the mine car point sets in open pit decreases the accuracy of the UAV point cloud data， an automatic extraction method of the mine car point sets in open pit stope was proposed. Using the UAV point cloud of the Yabaling open pit as the data source， the ground points and non-ground points were distinguished using the progressive morphological filtering algorithm， then the set of mine car points were extracted from the non-ground points by clustering them with an improved Euclidean clustering algorithm， and finally the mine car extraction results were evaluated and analyzed based on the error criterion proposed by the International Society for Photogrammetry and Remote Sensing (ISPRS). The results show that the method can effectively extract the set of mine car points in the open pit， and can provide an important technical support for fast and efficient check and acceptance of the open pit.