Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN.

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.