东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (3): 438-443.DOI: 10.12068/j.issn.1005-3026.2014.03.030

• 资源与土木工程 • 上一篇    下一篇

深部复杂环境下采空区激光扫描异常点云数据修正

熊立新，罗周全，罗贞焱，谢承煜   

  1. (中南大学 资源与安全工程学院， 湖南 长沙410083)
  • 收稿日期:2013-05-07 修回日期:2013-05-07 出版日期:2014-03-15 发布日期:2013-11-22
  • 通讯作者: 熊立新
  • 作者简介:熊立新(1983-)，男，湖南长沙人，中南大学博士研究生；罗周全(1966-)，男，湖南邵阳人，中南大学教授，博士生导师.
  • 基金资助:
    “十二五”国家科技支撑计划项目(2012BAK09B02-05)；国家自然科学基金资助项目(51274250).

Data Amendment of Abnormal Point Cloud of Goaf by Laser Scan in Deep Complex Environment

XIONG Lixin， LUO Zhouquan， LUO Zhenyan， XIE Chengyu   

  1. School of Resource & Safety Engineering， Central South University， Changsha 410083， China.
  • Received:2013-05-07 Revised:2013-05-07 Online:2014-03-15 Published:2013-11-22
  • Contact: LUO Zhouquan
  • About author:-
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摘要： 分析了激光扫描轨迹曲线的拓扑关系;研究了深部复杂环境下采空区激光探测结果中异常点:“坏点”和“噪声点”产生的环境因素.针对XYZ格式数据，基于四点插入法提出了坏点修正方法；基于二阶几何连续性提出了弦夹角和弦高比的噪声点过滤复合判据，利用阈值选择方法，过滤噪声点.工程验证表明:修正后空区内部形态、顶板边界更准确，体积修正约20~70m3，顶板高度提高1~2m左右，为空区安全预警提供依据.该算法逻辑简单、计算量小，为深部复杂环境下采空区三维边界信息精确获取提供了一种新方法.

关键词: 复杂环境, 深部采空区, 激光扫描, 点云, 数据修正

Abstract: The topology relationship of laser trace lines was analyzed. The abnormal points were divided into two categories:dead points and noise points. The environmental factors that affect the dead point error in laser detection were analyzed. The 3D point cloud data were obtained as XYZ format data files， and the data amendment algorithms for dead points were proposed based on 4point interpolation. Moreover， the noise filtering algorithms for the minimal angle and stringheight ratio of noise points based on G2continuity were proposed. The validation shows that the volume， the exposed roof area and roof height are consistent with the reality after reconstruction. For example，the volume revised about 20~70m3， and the height of roof increased about 1~2m. The algorithms is simple logically and less timeconsuming， thereby providing a new method for dealing with goaf point cloud data.

Key words: complex environment, deep goaf, laser scan, point cloud, data amendment

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