煤矿井下巷道点云变形检测及可视化研究
Research on Point Cloud Deformation Detection and Visualization in Underground Coal Mine Tunnels
穆莉莉 1杨紫威 1刘帅帅 1王天棋 1李训杰1
作者信息
- 1. 安徽理工大学 机电工程学院,安徽 淮南 232001
- 折叠
摘要
为解决煤矿井下巷道变形传统检测的非连续、效率低、智能化程度低的问题,提出一种深度相机与激光雷达点云融合三维空间重构后进行变形分析的新方法.设计了点云切片及提取骨骼线算法,建立了巷道变形检测模型.三维点云处理复杂,算法集成难度大,基于Qt开发了可视化变形监控软件,通过信号槽机制完成人机交互设计.测试表明,系统可对巷道全域进行自动连续检测,变形检测精度高,软件具有良好的人机交互性.
Abstract
In order to solve the problems of discontinuity,low efficiency,and low intelligence level in tradi-tional deformation detection of underground coal mine tunnels,a new method of analyzing deformations by fusing depth cameras and LIDAR point clouds for three-dimensional space reconstruction is proposed.An algorithm for point cloud slicing and skeleton line extraction is designed,and a tunnel deformation detection model is estab-lished.Due to the complexity of processing three-dimensional point clouds and the difficulty of integrating algo-rithms,a visualization deformation monitoring software is developed based on Qt,which achieves human-ma-chine interaction design through signal-slot mechanism.Tests show that the system can automatically and contin-uously detect the whole region of the tunnel,with high deformation detection accuracy and good human-machine interaction of the software.
关键词
巷道变形检测/骨骼线提取/点云切片/Qt/可视化Key words
tunnel deformation detection/skeleton line extraction/point cloud slicing/Qt/visualization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据