利用平面特征和邻域信息改正的激光雷达形变监测方法研究
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摘要
地面激光雷达形变监测过程中,多期数据间的残余系统误差和激光点的偶然误差对监测结果有较大影响。采用平面特征和邻域信息的改正方法:针对残余系统误差,提取稳定区域的至少3组平面进行拟合,利用平面的法向量和交点计算多期数据间的旋转量和平移量,对系统误差进行改正;针对激光点的偶然误差,对点云构建kd-tree,搜索k邻域计算重心,利用其邻域信息对偶然误差进行削弱。以某隧道口岩体监测实验为例,对提取形变的均值和标准差进行统计,该方法对系统误差的改正量达80%以上,并可将形变提取精度提高2倍。结果表明,利用平面特征和邻域信息的改正方法,能够有效地对多期数据间残余的系统误差进行改正,并削弱激光点偶然误差的影响,可以更准确、有效地分离及提取形变。
In the process of deformation monitoring based on terrestrial LIDAR,the residual systematic errors in the multi-phase data and the accidental errors of laser points have great influence on the monitoring results.We adopted the correction method using plane features and neighborhood information: In the case of residual system error,at least three sets of planes in the stable area were extracted for fitting,and the normal vector and the intersection points of the plane were used to calculate the translation and rotation of multi-phase data,correcting the system error; in the case of the accidental error of laser points,KD-tree was constructed with point cloud,computing center of gravity by searching k-neighborhood,and the accidental error was eliminated according to its neighborhood information. Taking the rock deformation monitoring experiment of a tunnel as an example,the mean and standard deviation of the extracted deformation information were statistically analyzed.The correction of the system error by the proposed method reached 80%,and the accuracy of the extracted deformation information was increased by 2 times. The results show that the proposed correction method using plane features and neighborhood information can effectively correct the residual systematic errors of multi-phase data and eliminate the influence of accidental errors of laser points. Therefore, the proposed correction method can separate and extract deformation information more accurately and effectively.
In the process of deformation monitoring based on terrestrial LIDAR,the residual systematic errors in the multi-phase data and the accidental errors of laser points have great influence on the monitoring results.We adopted the correction method using plane features and neighborhood information: In the case of residual system error,at least three sets of planes in the stable area were extracted for fitting,and the normal vector and the intersection points of the plane were used to calculate the translation and rotation of multi-phase data,correcting the system error; in the case of the accidental error of laser points,KD-tree was constructed with point cloud,computing center of gravity by searching k-neighborhood,and the accidental error was eliminated according to its neighborhood information. Taking the rock deformation monitoring experiment of a tunnel as an example,the mean and standard deviation of the extracted deformation information were statistically analyzed.The correction of the system error by the proposed method reached 80%,and the accuracy of the extracted deformation information was increased by 2 times. The results show that the proposed correction method using plane features and neighborhood information can effectively correct the residual systematic errors of multi-phase data and eliminate the influence of accidental errors of laser points. Therefore, the proposed correction method can separate and extract deformation information more accurately and effectively.
引文
[1]Bitelli G,Dubbini M,Zanutta A.Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies[J].International Archives of Photogrammetry,Remote Sensing and Spatial Information Sciences,2004,35(Part B 5):246-251.
[2]赵小平,闫丽丽,刘文龙.三维激光扫描技术边坡监测研究[J].测绘科学,2010,35(4):25-27.
[3]邢正全,邓喀中.三维激光扫描技术应用于边坡位移监测[J].地理空间信息,2011,9(1):68-70.
[4]侯俊岭.三维激光扫描技术在铁路危岩落石勘测中的应用[J].铁道勘察,2017,43(2):31-34.
[5]朱雪峰.基于地面激光雷达技术的危岩落石调查方法[J].铁道勘察,2017(3):7-10.
[6]武鹏,缪志修.地面三维激光扫描仪标靶布设研究[J].铁道勘察,2017(6):34-37.
[7]马立广.地面三维激光扫描测量技术研究[D].武汉:武汉大学,2005.
[8]韩三琪.三维激光扫描仪单点精度的检验与分析[J].铁道勘察,2013,39(6):9-11.
[9]孔祥元,郭际明,刘宗泉.大地测量学基础[M].武汉:武汉大学出版社,2010.
[10]官云兰,程效军,施贵刚.一种稳健的点云数据平面拟合方法[J].同济大学学报(自然科学版),2008,36(7):981-984.
[11]武汉大学测绘学院测量平差学科组.误差理论与测量平差基础[M].武汉:武汉大学出版社,2008.
[12]Theiler P W,Schindler K.Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces[J].ISPRS Ann.Photogrammetry.Remote Sens.Spatial Inf.Sci,2012,1:173-178.
[13]刘绍堂,刘文锴,周跃寅.一种隧道整体变形监测方法及其应用[J].武汉大学学报(信息科学版),2014,39(8):981-986.
[14]Liu C,Li N,Wu H,et al.Detection of High Speed Railway Subsidence and Geometry Irregularity Using Terrestrial Laser Scanning[J].Journal of Surveying Engineering,2013,140(3).
[15]Friedman J H,Bentley J L,Finkel R A.An algorithm for finding best matches in logarithmic expected time[J].ACM Transactions on Mathematical Software(TOMS),1977,3(3):209-226.
[2]赵小平,闫丽丽,刘文龙.三维激光扫描技术边坡监测研究[J].测绘科学,2010,35(4):25-27.
[3]邢正全,邓喀中.三维激光扫描技术应用于边坡位移监测[J].地理空间信息,2011,9(1):68-70.
[4]侯俊岭.三维激光扫描技术在铁路危岩落石勘测中的应用[J].铁道勘察,2017,43(2):31-34.
[5]朱雪峰.基于地面激光雷达技术的危岩落石调查方法[J].铁道勘察,2017(3):7-10.
[6]武鹏,缪志修.地面三维激光扫描仪标靶布设研究[J].铁道勘察,2017(6):34-37.
[7]马立广.地面三维激光扫描测量技术研究[D].武汉:武汉大学,2005.
[8]韩三琪.三维激光扫描仪单点精度的检验与分析[J].铁道勘察,2013,39(6):9-11.
[9]孔祥元,郭际明,刘宗泉.大地测量学基础[M].武汉:武汉大学出版社,2010.
[10]官云兰,程效军,施贵刚.一种稳健的点云数据平面拟合方法[J].同济大学学报(自然科学版),2008,36(7):981-984.
[11]武汉大学测绘学院测量平差学科组.误差理论与测量平差基础[M].武汉:武汉大学出版社,2008.
[12]Theiler P W,Schindler K.Automatic registration of terrestrial laser scanner point clouds using natural planar surfaces[J].ISPRS Ann.Photogrammetry.Remote Sens.Spatial Inf.Sci,2012,1:173-178.
[13]刘绍堂,刘文锴,周跃寅.一种隧道整体变形监测方法及其应用[J].武汉大学学报(信息科学版),2014,39(8):981-986.
[14]Liu C,Li N,Wu H,et al.Detection of High Speed Railway Subsidence and Geometry Irregularity Using Terrestrial Laser Scanning[J].Journal of Surveying Engineering,2013,140(3).
[15]Friedman J H,Bentley J L,Finkel R A.An algorithm for finding best matches in logarithmic expected time[J].ACM Transactions on Mathematical Software(TOMS),1977,3(3):209-226.