基于三维激光点云比对的煤矿巷道表面位移监测研究

Study on coal mine tunnels surface displacement monitoring based on 3D laser point cloud comparison

煤矿巷道表面位移监测对于确保矿山安全和稳定运营至关重要。采用基于点云法向量的下采样算法与多尺度模型到模型的点云比对算法(Multiscale Model-to-Model Cloud Comparison,M3C2)相融合的方式,实现了对巷道表面的特征提取与整体性监测;通过三维激光扫描仪获取的三维激光点云对巷道表面进行重构,获取三维点云模型,利用点云对比算法对不同时期的2期点云数据进行处理,并以色值表征煤矿巷道位移区域变化,运用于实际巷道表面位移监测。结果表明:应用基于点云法向量的下采样算法与M3C2算法相融合的方式成功提取特征区域并实现巷道表面位移的整体性监测,此方法能直观反映巷道表面的变形区域,相比在巷道直接应用M3C2算法性能更优。

Monitoring the surface displacement of coal mine tunnel is very important to ensure the safe and stable operation of the mine.By combining the downsampling algorithm based on the normal vector of point cloud with the multiscale model-to-model point cloud comparison algorithm,the feature extraction and integrity monitoring of tunnel surface can be realized.The three-dimensional laser point cloud obtained by the three-dimensional laser scanner is used to reconstruct the roadway surface,and the three-dimensional point cloud model is obtained.The point cloud comparison algorithm is used to process the two periods of point cloud data in different periods.The color value is used to represent the regional change of coal mine tunnel displacement,which is applied to the actual tunnel surface displacement monitoring.The research shows that the down-sampling algorithm based on point cloud normal vector and M3C2 algorithm are combined to successfully extract the feature area and realize the overall monitoring of tunnel surface displacement.This method can directly reflect the deformation area of tunnel surface,and its performance is better than that of directly applying M3C2 algorithm in tunnel.