Point cloud data can accurately and intuitively reflect the spatial relationship between the coal wall and underground fully mechanized mining equipment. However, the indirect method of point cloud feature extraction based on deep neural networks will lose some of the spatial information of the point cloud, while the direct method will lose some of the local information of the point cloud. Therefore, we propose the use of dynamic graph convolution neural network (DGCNN) to extract the geometric features of the sphere in the point cloud of the fully mechanized mining face (FMMF) in order to obtain the position of the sphere (marker) in the point cloud of the FMMF, thus providing a direct basis for the subsequent transformation of the FMMF coordinates to the national geodetic coordinates with the sphere as the intermediate medium. Firstly, we completed the production of a diversity sphere point cloud (training set) and an FMMF point cloud (test set). Secondly, we further improved the DGCNN to enhance the effect of extracting the geometric features of the sphere in the FMMF. Finally, we compared the effect of the improved DGCNN with that of PointNet and PointNet++. The results show the correctness and feasibility of using DGCNN to extract the geometric features of point clouds in the FMMF and provide a new method for the feature extraction of point clouds in the FMMF. At the same time, the results provide a direct early guarantee for analyzing the point cloud data of the FMMF under the national geodetic coordinate system in the future. This can provide an effective basis for the straightening and inclining adjustment of scraper conveyors, and it is of great significance for the transparent, unmanned, and intelligent mining of the FMMF.

中文翻译：

---

基于DGCNN的综采工作面激光点云处理

点云数据可以准确直观地反映煤壁与井下综采设备的空间关系。然而，基于深度神经网络的点云特征提取的间接方法会丢失点云的一些空间信息，而直接方法会丢失点云的一些局部信息。因此，我们提出使用动态图卷积神经网络（DGCNN）对综采工作面（FMMF）点云中球体的几何特征进行提取，从而获得球体（标记）在综采工作面点云中的位置。 FMMF 的点云，从而为后续将 FMMF 坐标转换为以球体为中间介质的国家大地坐标提供直接依据。首先，我们完成了多样性球体点云（训练集）和FMMF点云（测试集）的制作。其次，我们进一步改进了DGCNN，以增强FMMF中提取球体几何特征的效果。最后，我们将改进的 DGCNN 与 PointNet 和 PointNet++ 的效果进行了比较。结果表明了利用DGCNN提取FMMF中点云几何特征的正确性和可行性，为FMMF中点云的特征提取提供了一种新的方法。同时，该结果为以后在国家大地坐标系下分析​​FMMF的点云数据提供了直接的早期保证。这可以为刮板输送机的矫直和倾斜调整提供有效依据，