一种基于全站扫描的特征点自约束点云变形分析方法
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摘要
为了对大坝、高层建筑、滑坡区、采空区等危险变形体进行变形观测,针对智能全站仪、GNSS测量、三维激光扫描等变形监测技术无法很好地实现变形特征点和点云的综合变形分析问题,提出了基于全站扫描特征点自约束点云变形分析方法,获取变形体的离散特征点和整体点云变形数据,利用特征点形变矢量求取点云至模型的形变量,从而刻画变形体的整体形变信息。试验结果表明,利用本文所提出的方法能够成功计算出点云至模型的形变量,经统计分析,所有点的形变量真误差的期望值为-0.04 mm,结果精度为1.2 mm。试验结果能够反映变形体的整体形变信息,且具有较高精度。
In order to monitor the dangerous deformation body,such as dam,high-rise buildings,landslide area and goaf,the intelligent total station,GNSS measurement,3D laser scanning and other deformation monitoring technology cannot achieve the comprehensive deformation analysis of deformation feature points and point clouds commendably,the point cloud deformation analysis method of feature point self-constrained based on multistation is proposed. The discrete feature points and the whole point cloud deformation data of the deformable body are obtained,and the shape variables of point cloud to the model are obtained using the feature point deformation vector,so as to depict the whole deformation information of the deformable body. The experimental results show that the shape variables of point cloud to model can be successfully calculated by using the proposed method. By statistical analysis,the expected value of the true error of all points is-0.04 mm and the precision of the results is 1.2 mm. The experimental results can reflect the whole deformation information of the deformable body and have high accuracy.
In order to monitor the dangerous deformation body,such as dam,high-rise buildings,landslide area and goaf,the intelligent total station,GNSS measurement,3D laser scanning and other deformation monitoring technology cannot achieve the comprehensive deformation analysis of deformation feature points and point clouds commendably,the point cloud deformation analysis method of feature point self-constrained based on multistation is proposed. The discrete feature points and the whole point cloud deformation data of the deformable body are obtained,and the shape variables of point cloud to the model are obtained using the feature point deformation vector,so as to depict the whole deformation information of the deformable body. The experimental results show that the shape variables of point cloud to model can be successfully calculated by using the proposed method. By statistical analysis,the expected value of the true error of all points is-0.04 mm and the precision of the results is 1.2 mm. The experimental results can reflect the whole deformation information of the deformable body and have high accuracy.
引文
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[6]付崇江,张斌,朱云,等. GPS技术在三峡库区云阳县滑坡监测中的应用[J].重庆交通大学学报(自然科学版),2011,30(S1):538-540.
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[9]周保兴,岳建平,张磊,等.基于地面三维激光扫描技术的建筑物整体位移监测[J].测绘通报,2013(10):76-79.
[10]徐进军,王海城,罗喻真,等.基于三维激光扫描的滑坡变形监测与数据处理[J].岩土力学,2010,31(7):2188-2191.
[11]唐棍,花向红,魏成,等.基于三维激光扫描的建筑物变形监测方法研究[J].测绘地理信息,2013,38(2):54-55.
[12]罗德安,朱光,陆立,等.基于3维激光影像扫描技术的整体变形监测[J].测绘通报,2005(7):40-42.
[13]党晓斌.三维激光扫描技术在建筑物形变监测中的应用研究[D].西安:长安大学,2011:36-44.
[14] PAGOUNIS V,TSAKIRI M,PALASKAS S,et al. 3D laser scanning for road safety and traffic accident reconstruction[C]∥Shaping the Change XXIII FIG Congress Munich. Berlin:[s.n.],2006:8-13.
[15]黄晓阳,栾元重,闫勇,等.基于三维激光扫描测量技术的井架变形观测[J].工程勘察,2012,40(4):66-69.
[16] CHEN B,DENG K,FAN H,et al. Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration[J]. International Journal of Mining Science and Technology,2013,23(4):555-561.
[17]李东敏,范百兴,周蕴,等.全站式扫描仪测量原理及精度分析[J].测绘通报,2014(8):131-133.
[18]杨凡,李广云,范百兴,等.亚毫米级高精度全站仪坐标测量精度分析[J].测绘通报,2012(1):95-97.
[19] EDELSBRUNNER H. Discrete and computational geometry[M].Berlin:Springer Press,2003.
[20] ALEXA M,BEHR J,COHEN-OR D,et al. Point set surfaces[C]∥Proceedings of IEEE Visualization. San Diego:[s.n.],2001:21-28
[21] LEVINE M D,SHAHEEN S I. A modular computer vision system for picture segmentation and interpretation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1981,3(5):540-556.
[22]李德仁,袁修孝.误差处理与可靠性理论[M].武汉:武汉大学出版社,2002.
[23]隋立芬,宋力杰,柴洪洲.误差理论与测量平差基础[M].北京:测绘出版社,2010.
[2]刘沛.自动化全站仪在高层建筑基坑变形监测中的应用[J].测绘与空间地理信息,2011,34(3):239-241.
[3]王洪.徕卡TS30超高精度全站仪在变形监测中的应用[J].辽宁工程技术大学学报(自然科学版),2015,34(3):401-404.
[4]肖根旺,郭红星,徐忠阳.全站仪自动变形监测系统在招宝山大桥变形监测中的应用[J].测绘信息与工程,2002,27(4):19-20.
[5]张建坤,黄声享,李翅,等. GPS技术在滑坡变形监测中的应用[J].地理空间信息,2009,7(1):110-112.
[6]付崇江,张斌,朱云,等. GPS技术在三峡库区云阳县滑坡监测中的应用[J].重庆交通大学学报(自然科学版),2011,30(S1):538-540.
[7] ZHOU D,WU K,CHEN R,et al. GPS/terrestrial 3D laser scanner combined monitoring technology for coal mining subsidence:a case study of a coal mining area in Hebei,China[J]. Natural Hazards,2014,70(2):1197-1208
[8]林鸿,欧海平,王峰.地面激光扫描技术在建筑变形测量中的应用探讨[J].测绘通报,2016(6):73-76.
[9]周保兴,岳建平,张磊,等.基于地面三维激光扫描技术的建筑物整体位移监测[J].测绘通报,2013(10):76-79.
[10]徐进军,王海城,罗喻真,等.基于三维激光扫描的滑坡变形监测与数据处理[J].岩土力学,2010,31(7):2188-2191.
[11]唐棍,花向红,魏成,等.基于三维激光扫描的建筑物变形监测方法研究[J].测绘地理信息,2013,38(2):54-55.
[12]罗德安,朱光,陆立,等.基于3维激光影像扫描技术的整体变形监测[J].测绘通报,2005(7):40-42.
[13]党晓斌.三维激光扫描技术在建筑物形变监测中的应用研究[D].西安:长安大学,2011:36-44.
[14] PAGOUNIS V,TSAKIRI M,PALASKAS S,et al. 3D laser scanning for road safety and traffic accident reconstruction[C]∥Shaping the Change XXIII FIG Congress Munich. Berlin:[s.n.],2006:8-13.
[15]黄晓阳,栾元重,闫勇,等.基于三维激光扫描测量技术的井架变形观测[J].工程勘察,2012,40(4):66-69.
[16] CHEN B,DENG K,FAN H,et al. Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration[J]. International Journal of Mining Science and Technology,2013,23(4):555-561.
[17]李东敏,范百兴,周蕴,等.全站式扫描仪测量原理及精度分析[J].测绘通报,2014(8):131-133.
[18]杨凡,李广云,范百兴,等.亚毫米级高精度全站仪坐标测量精度分析[J].测绘通报,2012(1):95-97.
[19] EDELSBRUNNER H. Discrete and computational geometry[M].Berlin:Springer Press,2003.
[20] ALEXA M,BEHR J,COHEN-OR D,et al. Point set surfaces[C]∥Proceedings of IEEE Visualization. San Diego:[s.n.],2001:21-28
[21] LEVINE M D,SHAHEEN S I. A modular computer vision system for picture segmentation and interpretation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1981,3(5):540-556.
[22]李德仁,袁修孝.误差处理与可靠性理论[M].武汉:武汉大学出版社,2002.
[23]隋立芬,宋力杰,柴洪洲.误差理论与测量平差基础[M].北京:测绘出版社,2010.
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