铁路复测中车载LiDAR数据精度控制分析

Accuracy Control Analysis of Mobile Laser Scanning Data on Railway Re-Survey

铁路作为长大线状工程,沿线地形、环境复杂,利用车载LiDAR技术进行铁路复测可有效解决传统铁路复测技术手段落后的难题,但各种系统误差易导致原始解算点云数据无法满足铁路复测精度要求。首先,分析数据获取与处理的误差来源;然后,基于某铁路专用线建立长度为4 km的试验场,以获取车载LiDAR扫描数据;再基于靶标控制网的高精度点云约束平差处理方法,通过沿线路交叉均匀布设的靶标控制点对原始点云进行约束纠偏;最后采用两套检核数据对约束前后三维点云的平面、高程精度进行分析。统计结果显示,约束后点云平面和高程中误差分别为0.4 cm、0.5 cm,满足铁路复测精度要求。研究表明,基于靶标控制网的高精度点云约束平差处理方法可显著改善测量及处理误差造成点云精度损失。

Railway is considered as a long and linear project,the terrain and environment along the railway are complex.Using mobile LiDAR technology for railway resurvey can effectively solves the problems of traditional technology.However,various systematic errors easily make the original point cloud data hardly meet the accuracy requirements of railway resurvey.Firstly,the error sources of data acquisition and processing were analyzed,then a 4km long test site based on a railway special line was established to obtain the mobile scanning data.Moreover,based on the high-precision point cloud constrained adjustment processing method of target control network,the original point cloud was constrained and corrected by the target control points cross-distributed along the railway.Finally,the plane and elevation accuracy of 3D point cloud were analyzed before and after constrain by using two sets of checking data.The statistical results show that the plane and elevation mean square errors of the constrained point cloud are 0.4cm and 0.5cm respectively,which meet the requirements of railway resurvey accuracy.The high-precision point cloud constrained adjustment method based on target control network can significantly improve the point cloud precision loss caused by the measurement and processing errors.