摘要
为对采煤沉陷形成的面状盆地进行全面监测,提高矿区地表沉陷监测的效率和精度,研究基于无人机激光雷达技术,监测矿区地表沉陷。并提出结合无人机航线设计和激光扫描仪,来采集点云数据。同时联合形态学滤波与不规则三角网,对沉陷盆地数据进行去噪。通过Kriging插值法构建高精度数字高程模型,求取地表沉陷盆地。无人机LiDAR技术结合点云滤波算法在矿区沉陷监测中展现了高效率和精度。改进的密度聚类算法在去噪和结构保持上表现优异,平均绝对误差值在0.07 m~0.09 m,夹角平均值5.8°~7.0°。无人机LiDAR与实测误差小,平均误差-0.16 m~0.06 m,平均绝对误差0.14 m~0.31 m,均方根误差0.17 m~0.34 m,验证了方法的可靠性。研究为矿区沉陷监测提供了技术革新途径,具有重要的生态环境保护和煤矿智能开采价值。
To comprehensively monitor the surface basin formed by coal mining subsidence and improve the efficiency and accuracy of mining area surface subsidence monitoring,this paper studies the surface subsidence monitoring based on LiDAR technology.It is proposed to combine UAV route design and laser scanner to collect point cloud data.At the same time,morphological filtering and irregular triangulation network are combined to denoise the data of the subsidence basin.Kriging interpolation method is used to construct a high-precision digital elevation model to obtain the surface subsidence basin.LiDAR technology combined with point cloud filtering algorithm shows high efficiency and accuracy in mining subsidence monitoring.The improved density clustering algorithm has excellent performance in denoising and structure preservation.The average absolute error value is 0.07~0.09 m,and the average Angle is 5.8~7.0°.The average error of LiDAR is-0.16 m~0.06 m,the average absolute error is 0.14 m~0.31 m,and the rootmean-square error is 0.17 m~0.34 m,which verifies the reliability of the method.The research provides a technological innovation way for mining subsidence monitoring,which has important ecological environment protection and intelligent mining value.
作者
黎娟
LI Juan(Xi’an Aeronautical Polytechnic Institute,Xi’an 710089,China)
出处
《自动化与仪器仪表》
2025年第7期38-42,共5页
Automation & Instrumentation
基金
陕西省教育科学“十四五”规划课题《产教融合背景下高职测绘类专业社会服务能力提升实践研究》(SGH24Y3295)
西安航空职业技术学院《融合思政与双创教育的高职无人机专业课程教学改革探索与实践》(24XHJG19)
西安航空职业技术学院2021年度科技创新团队:无人机贴近摄影测量技术创新团队(KJTD21-001)。
