Research on 3D modeling of underground roadways in coal mines
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摘要:
巷道三维重建是实现矿井探测的重要手段。三维激光扫描结合同时定位与建图（SLAM）技术可实现巷道扫描与三维重建，但在井下几何特征稀疏环境中存在点云配准精度不足、效率不高等问题。以陕西煤业化工集团柠条塔煤矿南翼东区辅运巷道为工程背景，对井下巷道高精度建模展开研究。针对柠条塔煤矿井下环境特点，将测量控制点布置在巷道顶板，减少环境因素对控制点位置的干扰；采用GOSLAM−GSJH12手持式三维激光扫描仪采集点云数据；通过构建已知点约束，根据已知控制点坐标对点云坐标进行非线性优化，实现点云漂移校正；采用小波分解、非局部均值等去噪算法和基于PointNet++的深度学习分割算法去除点云数据中的噪声；基于改进的Harris3D角点检测器和随机样本一致(RANSAC)算法提取巷道点云特征，融合三维激光雷达和惯性测量单元数据进行巷道点云配准，实现高精度地图构建；采用Delaunay三角剖分算法构建井下巷道的不规则三角网模型，再通过多阶段优化实现三维模型精细化重建，最终通过可视化平台展示。研究成果可与物联网、大数据、人工智能等技术结合，实现矿井智能化管理和决策。
Abstract:3D reconstruction of underground roadways is an important approach for mine surveying. 3D laser scanning combined with Simultaneous Localization and Mapping (SLAM) technology enables roadway scanning and 3D reconstruction. However, in underground environments with sparse geometric features, there exist problems such as insufficient point cloud registration accuracy and low efficiency. Taking the auxiliary haulage roadway in the southeastern section of the south wing of Ningtiaota Coal Mine under Shaanxi Coal and Chemical Industry Group as the engineering background, this study conducted high-precision modeling of underground roadways. Considering the characteristics of the underground environment of Ningtiaota Coal Mine, control points were arranged on the roadway roof to reduce environmental interference on their positions. A GOSLAM-GSJH12 handheld 3D laser scanner was used to collect point cloud data. By constructing known point constraints and performing nonlinear optimization on the point cloud coordinates based on the known control point coordinates, point cloud drift was corrected. Denoising algorithms such as Wavelet Decomposition and Non-Local Means, along with the deep learning segmentation algorithm based on PointNet++, were applied to remove noise in the point cloud data. Roadway point cloud features were extracted using an improved Harris3D corner detector and the Random Sample Consensus (RANSAC) algorithm. Point cloud registration was performed by fusing data from 3D LiDAR and the Inertial Measurement Unit (IMU), enabling high-precision map construction. The Delaunay Triangulation algorithm was adopted to construct an irregular triangular mesh model of the underground roadway, and multi-stage optimization was used to achieve fine 3D reconstruction, which was finally presented via a visualization platform. The research results can be integrated with technologies such as the Internet of Things, big data, and artificial intelligence to realize intelligent mine management and decision-making.
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图 1 矿井巷道三维模型重建技术研究路线
Figure 1. Research route for 3D model reconstruction technology of mine roadways
表 1 采用不同点云漂移校正算法时区域样本误差统计
Table 1 Error statistics of regional samples under different point cloud drift correction algorithms
编号 实际值/m Lego−LOAM LIO−SAM 已知点约束 测量值/m 绝对误差/m 相对误差/% 测量值/m 绝对误差/m 相对误差/% 测量值/m 绝对误差/m 相对误差/% 1 4.600 4.356 0.244 5.30 4.562 0.038 0.83 4.582 0.018 0.39 2 2.000 1.916 0.084 4.20 1.986 0.014 0.70 1.991 0.009 0.45 3 0.600 0.585 0.015 2.50 0.595 0.005 0.83 0.595 0.005 0.83 4 3.500 3.663 0.163 4.66 3.533 0.033 0.94 3.487 0.013 0.37 5 0.900 0.923 0.023 2.56 0.862 0.038 4.22 0.893 0.007 0.78 6 1.500 1.482 0.018 1.20 1.490 0.01 0.67 1.493 0.007 0.47 
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