摘要
机载激光雷达数据已经广泛应用于铁路的勘测工作,针对机载激光雷达采样密度低所引发的铁轨点提取精度低、建模难度大的问题,提出一种从机载激光雷达数据中自动检测铁轨和轨道建模的新方法。首先通过铁路区域特有的空间分布特征提取铁轨上方的悬链线,进而实现铁路区域分割;然后利用alpha-shapes算法检测轨道点;最后构建铁轨参数模型,采用基于主元分析的点云配准方法将检测出的铁轨点与铁轨参数模型进行匹配,实现轨道的整体建模。应用某铁路站点的机载激光雷达数据进行试验,实验表明:该方法可以实现铁轨点的高精度提取和铁轨模型的快速拟合;铁轨点提取的准确度可以达到97%,完整度可以达到94%以上。
The airborne LiDAR data has been widely used in railway survey work.The problem of low accuracy and difficult modeling of rail point extraction caused by low sampling density of airborne LiDAR is proposed.This paper presents a new method for automatic detection of orbit and orbit modeling from airborne LiDAR data.Firstly,the catenary line above the rail is extracted by using the unique spatial distribution characteristics of the railway area to realize the division of the railway area.Then use the alpha-shapes algorithm to detect orbit points.Finally,the rail parameter model is constructed,and the detected point of the rail is matched with the parametric model by the point cloud registration method based on principal component analysis to realize the overall modeling of the orbit.The airborne LiDAR data of a railway station is used to test.The experiment shows that the method can achieve high-precision extraction of rail points and rapid fitting of rail models.The accuracy of rail point extraction can reach 97%,and the integrity can reach more than 94%.
作者
王凡
许筱
Wang Fan;Xu Xiao(Schod of Earth Science and Survey,China University of Mining & Technology,Beijing)
出处
《现代矿业》
CAS
2019年第6期185-190,共6页
Modern Mining
