Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 10: 1734 - 1741

DOI: https://doi.org/10.11947/j.AGCS.2017.20170323

Progress on Dynamic and Precise Engineering Surveying for Pavement and Track

  • LI Qingquan ,
  • MAO Qingzhou
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  • 1. Key Laboratory for Geo-Environmental Monitoring for Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China;
    2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received date: 2017-06-19

  Revised date: 2017-07-05

  Online published: 2017-10-26

Supported by

The National Natural Science Foundation of China(Nos. 91546106;41201483);The Fundamental Research Funds for the Central Universities;The National Key Research and Development Plan(No. 2016YFF0103502)

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Abstract

The traditional precise engineering surveying is characterized by manual static discrete observation and geometric model solution,which cannot meet the requirements of large-scale infrastructure such as pavement and track,where need of wide-range,continuous,dynamic and high-precision surveying for these infrastructures' and systems' maintenance and management.The dynamic surveying based on multi-sensor integration technology and the cooperative computing based on multi-source spatial-temporal data are the important development directions for precise engineering surveying.First,the paper introduced the uniform and conversion of high-precision spatial-temporal datum,the multi-sensor synchronous control,the fusion of the observed data,the quality improvement of the surveying data and the feature extraction and recognition of the pavement cracks based on the three-dimensional image.Then,the progress of dynamic and precise surveying applications such as pavement roughness,rutting and deflection survey,as well as geometrical parameters of track,fastener status and rail damage detection are described.

Key words： dynamic measurement; precision measurement; spatial-temporal datum; multi-sensor synchronization

Cite this article

LI Qingquan , MAO Qingzhou . Progress on Dynamic and Precise Engineering Surveying for Pavement and Track[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(10) : 1734 -1741 . DOI: 10.11947/j.AGCS.2017.20170323

References

[1] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057. LI Deren. Towards Geo-spatial Information Science in Big Data Era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057.
[2] 葛纪坤, 万宏德, 李军吉. 精密工程测量的发展与展望[J]. 地理空间信息, 2015(1):155-156. GE Jikun, WAN Hongde, LI Junji. Development and Prospect of Precision Engineering Surveying[J]. Geospatial Information, 2015(1):155-156.
[3] 王丹. 工程测量的发展与需求[J]. 测绘通报, 2003(4):18-21. DOI:10.3969/j.issn.0494-0911.2003.04.006. WANG Dan. Engineering Surveying:Development and Demands[J]. Bulletin of Surveying and Mapping, 2003(4):18-21. DOI:10.3969/j.issn.0494-0911.2003.04.006.
[4] 李清泉, 毛庆洲. 车载道路快速检测与测量技术研究[J]. 交通信息与安全, 2009, 27(1):7-10. LI Qingquan, MAO Qingzhou. Land-borne Pavement Rapid Test and Measurement[J]. Journal of Transport Information and Safety, 2009, 27(1):7-10.
[5] 张德津, 李清泉. 公路路面快速检测技术发展综述[J]. 测绘地理信息, 2015, 40(1):1-8. ZHANG Dejin, LI Qingquan. A Review of Pavement High Speed Detection Technology[J]. Journal of Geomatics, 2015, 40(1):1-8.
[6] 王刚. 路面病害光学无损检测技术——基于超小波与分形理论的算法研究[D]. 南京:南京理工大学, 2007. WANG Gang. The Optical Nondestructive Examination for Pavement Distress-Algorithm Research Based on Super Wavelet and Multifractal Theorem[D]. Nanjing:Nanjing University of Science and Technology, 2007.
[7] 王泉. 北斗卫星通信授时系统设计及应用[D]. 西安:西北大学, 2009. WANG Quan. Design and Application of Beidou Satellite Communication Time Service System[D]. Xi'an:Northwestern University, 2009.
[8] CHEN Qijin, NIU Xiaoji, ZHANG Quan, et al. Railway Track Irregularity Measuring by GNSS/INS Integration[J]. Navigation, 2015, 62(1):83-93.
[9] 李清泉, 毛庆洲, 高庆武, 等. 高精度时空数据获取的多传感器集成同步控制方法和系统:中国, CN101949715A[P]. 2011-01-19. LI Qingquan, MAO Qingzhou, GAO Qingwu, et al. Multi-sensor Integrated Synchronous Control Method and System for High-precision Time-space Space Data Acquisition:China, CN101949715A[P]. 2011-01-19.
[10] LI Qingquan, LIU Yong, MAO Qingzhou, et al. Data Acquisition, Analysis and Applications of Multi-sensor Integration[J]. Engineering Sciences, 2010, 8(1):2-10.
[11] 毛庆洲. 车载道路快速检测与测量关键技术研究[D]. 武汉:武汉大学, 2008. MAO Qingzhou. Research on Key Technique of Land-borne based Pavement Rapid Test and Measurement[D]. Wuhan:Wuhan University, 2008.
[12] 张亮. 地面复杂环境下移动三维测量精度改善方法研究[D]. 武汉:武汉大学, 2015. ZHANG Liang. Accuracy Improvement for Mobile Mapping in Complex Ground Environments[D]. Wuhan:Wuhan University, 2015.
[13] MAO Qingzhou, ZHANG Liang, LI Qingquan, et al. A Least Squares Collocation Method for Accuracy Improvement of Mobile LiDAR Systems[J]. Remote Sensing, 2015, 7(6):7402-7424.
[14] ZHANG Liang, MAO Qingzhou, LI Qingquan, et al. An Accuracy-Improvement Method for GPS/INS Kinematic Levelling for Use in Linear Engineering Surveying Projects[J]. Measurement, 2014, 54:22-30.
[15] 杨必胜, 魏征, 李清泉, 等. 面向车载激光扫描点云快速分类的点云特征图像生成方法[J]. 测绘学报, 2010, 39(5):540-545. YANG Bisheng, WEI Zheng, LI Qingquan, et al. A Classification-oriented Method of Feature Image Generation for Vehicle-borne Laser Scanning Point Clouds[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(5):540-545.
[16] 程效军, 贾东峰, 程小龙. 海量点云数据处理理论与技术[M]. 上海:同济大学出版社, 2014. CHENG Xiaojun, JIA Dongfeng, CHENG Xiaolong. Theory and Technology of Massive Point Cloud Data Processing[M]. Shanghai:Tongji University Press, 2014.
[17] 陈利, 贾友, 张尔严. 激光雷达技术及其应用[J]. 河南理工大学学报(自然科学版), 2009, 28(5):583-586. CHEN Li, JIA You, ZHANG Eryan. LiDAR Technology and Its Application[J]. Journal of Henan Polytechnic University (Natural Science), 2009, 28(5):583-586.
[18] 王愉龙. 公路路面裂缝三维检测技术[J]. 电子测试, 2016(7):49-50. WANG Yulong. 3D Road Surface Cracks Detection Technology[J]. Electronic Test, 2016(7):49-50.
[19] 赵海伟. 路面裂缝三维检测算法研究[D]. 西安:长安大学, 2015. ZHAO Haiwei. The Research on 3D Pavement Crack Detection Algorithm[D]. Xi'an:Chang'an University, 2015.
[20] 李清泉, 毛庆洲, 熊智敏, 等. 基于深度和灰度图像的路面裂缝检测装置和方法:中国, CN104005325A[P].2014-08-27. LI Qingquan, MAO Qingzhou, XIONG Zhimin, et al. Pavement Crack Detecting Device and Method Based on Depth and Gray Level Images:China, CN104005325A[P]. 2014-08-27.
[21] 李清泉, 毛庆洲, 靳华中, 等. 融合影像梯度信息和分水岭方法的裂缝检测方法:中国, CN104008553B[P]. 2017-03-22. LI Qingquan, MAO Qingzhou, JIN Huazhong, et al. Crack Detection Method with Image Gradient Direction Histogram and Watershed Method Conflated:China, CN104008553B[P]. 2017-03-22.
[22] 刘瑶. 基于激光雷达的三维典型地面目标识别技术研究[D]. 武汉:华中科技大学, 2011. LIU Yao. Study of Typical Three-Dimensional Ground Target Recognition Based on Laser Radar[D]. Wuhan:Huazhong University of Science and Technology, 2011.
[23] 蔚晓丹. 国际平整度指数IRI作为路面平整度评价指标的研究[J]. 公路交通科技, 1999, 16(S1):9-13. WEI Xiaodan. Research on the International Roughness Index (IRI) as the Pavement Roughness Index[J]. Journal of Highway and Transportation Research and Development, 1999, 16(S1):9-13.
[24] 毛庆洲, 叶浩, 丁诗雄, 等. 基于小波变换的路面平整度自适应提取算法[J]. 中国公路学报, 2015, 28(10):11-17. MAO Qingzhou, YE Hao, DING Shixiong, et al. Adaptive Extraction Algorithm of Pavement Roughness Based on Wavelet Transformation[J]. China Journal of Highway and Transport, 2015, 28(10):11-17.
[25] 李清泉, 雷波, 毛庆洲, 等. 利用激光三角法进行快速车辙检测[J]. 武汉大学学报(信息科学版), 2010, 35(3):302-307. LI Qingquan, LEI Bo, MAO Qingzhou, et al. A Fast Method for Pavement Ruts Measuring with Laser Triangulation[J]. Geomatics and Information Science of Wuhan University, 2010, 35(3):302-307.
[26] 张德津, 李清泉, 何莉. 一种新的激光车辙深度测量方法研究[J]. 光学学报, 2013, 33(1):115-121. ZHANG Dejin, LI Qingquan, HE Li. A New Method for Laser Rut Depth Measurement[J]. Acta Optica Sinica, 2013, 33(1):115-121.
[27] LI Qingquan, ZOU Qin, MAO Qingzhou, et al. Efficient Calibration of a Laser Dynamic Deflectometer[J]. IEEE Transactions on Instrumentation and Measurement, 2013, 62(4):806-813.
[28] 张德津. 基于路面变形速度的弯沉测量方法[D]. 武汉:武汉大学, 2012. ZHANG Dejin. Deflection Measurement Method Based on the Velocities of Pavement Deflections[D]. Wuhan:Wuhan University, 2012.
[29] ZOU Qin, CAO Yu, LI Qingquan, et al. CrackTree:Automatic Crack Detection from Pavement Images[J]. Pattern Recognition Letters, 2012, 33(3):227-238.
[30] LI Qingquan, ZOU Qin, ZHANG Daqiang, et al. FoSA:F* Seed-Growing Approach for Crack-Line Detection from Pavement Images[J]. Image and Vision Computing, 2011, 29(12):861-872.
[31] 李清泉, 胡庆武. 基于图像自动匀光的路面裂缝图像分析方法[J]. 公路交通科技, 2010, 27(4):1-5, 27. LI Qingquan, HU Qingwu. A Pavement Crack Image Analysis Approach Based on Automatic Image Dodging[J]. Journal of Highway and Transportation Research and Development, 2010, 27(4):1-5, 27.
[32] 李清泉, 邹勤, 毛庆洲. 基于最小代价路径搜索的路面裂缝检测[J]. 中国公路学报, 2010, 23(6):28-33. LI Qingquan, ZOU Qin, MAO Qingzhou. Pavement Crack Detection Based on Minimum Cost Path Searching[J]. China Journal of Highway and Transport, 2010, 23(6):28-33.
[33] 邹勤, 李清泉, 毛庆洲, 等. 利用目标点最小生成树的路面裂缝检测[J]. 武汉大学学报(信息科学版), 2011, 36(1):71-75. ZOU Qin, LI Qingquan, MAO Qingzhou, et al. Target-Points MST for Pavement Crack Detection[J]. Geomatics and Information Science of Wuhan University, 2011, 23(6):71-75.
[34] LI Qingquan, ZHANG Dequan, ZOU Qin, et al. 3D Laser Imaging and Sparse Points Grouping for Pavement Crack Detection[C]//25th European Signal Processing Conference (EUSIPCO). 2017.
[35] ZHANG Dejin, LI Qingquan, CHEN Ying, et al. An Efficient and Reliable Coarse-to-fine Approach for Asphalt Pavement Crack Detection[J]. Image and Vision Computing, 2017, 57:130-146.
[36] 李清泉, 刘向龙. 路面裂缝影像几何特征提取算法[J]. 中国科技论文在线, 2007, 2(7):517-522. LI Qingquan, LIU Xianglong. An Algorithm to Image-based Pavement Cracks Geometry Features Extraction[J]. Sciencepaper Online, 2007, 2(7):517-522.
[37] 刘向龙, 李清泉. 基于金字塔结构的海量路面影像破损统计模型[J]. 武汉大学学报(信息科学版), 2008, 33(4):430-432, 436. LIU Xianglong, LI Qingquan. A Pyramid-based Cracks Statistical Modal for Massive Pavement Images[J]. Geomatics and Information Science of Wuhan University, 2008, 33(4):430-432, 436.
[38] 冯超. 接触轨几何参数高精度动态测量方法研究[D]. 成都:西南交通大学, 2014. FENG Chao. Study of the High Precision Dynamic Measurement Method of Contact Rail Geometric Parameters[D]. Chengdu:Southwest Jiao Tong University, 2014.
[39] 毛庆洲. 铁路弹条扣件紧固状态自动检查方法及其装置:中国, CN103061219A[P].2013-04-24. MAO Qingzhou. Method and Device for Automatically Checking Fastening States of Railway Fasteners And Spring Fasteners:China, CN103061219A[P]. 2013-04-24.
[40] 吴梦, 陈建政, 林森. 基于图像处理的轨道扣件状态检测[J]. 信息与电脑, 2013(1):46-47. WU Meng, CHEN Jianzheng, LIN Sen. Track Fastener State Detection Based on Image Processing[J]. China Computer & Communication, 2013(1):46-47.
[41] 张利斌. 基于结构光技术的高速铁路道岔三维检测及应用研究[D]. 成都:西南交通大学, 2016. ZHANG Libin. Research on 3D Detection and Application of High-speed Railway Turnout Based on Structured Light Technology[D]. Chengdu:Southwest Jiao Tong University, 2016.
[42] 王旺华, 毛庆洲, 熊智敏. 钢轨几何尺寸自动测量系统研究[J]. 中国铁道科学, 2012, 33(S1):117-121. WANG Wanghua, MAO Qingzhou, XIONG Zhimin. Research on the Automatic Measurement System for Rail Physical Dimension[J]. China Railway Science, 2012, 33(S1):117-121.
[43] 徐其瑞, 许建明, 黎国清. 轨道检查车技术的发展与应用[J]. 中国铁路, 2005(9):37-39. XU Qirui, XU Jianming, LI Guoqing. Development and Application of the Technology of Track Inspection Car[J]. Chinese Railways, 2005(9):37-39.
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