一种基于多传感器数据融合探测地下管网的方案
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摘要
城市的发展使得地下管网体系愈加复杂,研究更高效、精确的非开挖探测技术显得愈加重要.基于一辆带有被动电磁感应线圈和探地雷达的传感器的探测车设备,本文提出了一种快速探测方案能探测地下管网且在地图上实时地绘制最新结果.其中,本文设计了一套智能的数据处理过程,该过程分两步:第一步是解释传感器返回的原始数据,转换成地下管道可能存在的空间点;第二步是融合这些点得到地下管道的走势曲线.在第一步中,本文对两种传感器分别设计了解释算法.在第二步中,本文提出了一种基于自修正和筛选的管道数据融合算法,此算法可以处理传感器数据噪声引起的虚警空间点问题.其核心思想是枚举多个假设管道,通过某种筛选机制选择最好的假设管道作为输出.此算法能迭代更新结果,即在探测设备完成新的一段扫描路径后,用新的数据优化上一次迭代的管道估计.模拟实验和真实环境实验的结果表明本文的探测方案能够有效探测地下管网,同时能较好地处理虚警空间点问题.
The development of cities makes the buried pipe network more and more complex,thus it is more important to research more efficient and more accurate technologies for trenchless detection. Based on a car shaped device with passive magnetic field coils and a ground penetrating radar,a detecting solution is proposed in this paper,which could realize fast detection and present the newest map of buried pipes in real time. As a part of it,a series of data processing methods are designed. The whole procedure could be divided into two steps. The first step is to interpret original data of sensors into possible spatial points of buried utilities and the second one is to fuse these points to evaluate the trend curve of pipes. For the first step,specific interpreting methods are designed for each sensor. For the second step,a novel data fusion method,named Pipe Revising and Evolving( PRE),is proposed to deal with the false-alarmed problem caused by the noise of data. The theme of it is to enumerate many hypothetical pipes and to output some reliable ones with a selection mechanism. The method could iteratively update the result,that is,the finish of a new section of scanning route would trigger it to update the historical speculation to present a better one. Experimental results on synthetic and real data demonstrate that our solution could detect the buried network effectively and has the competitive performance in terms of solving false-alarmed problem of spatial points.
The development of cities makes the buried pipe network more and more complex,thus it is more important to research more efficient and more accurate technologies for trenchless detection. Based on a car shaped device with passive magnetic field coils and a ground penetrating radar,a detecting solution is proposed in this paper,which could realize fast detection and present the newest map of buried pipes in real time. As a part of it,a series of data processing methods are designed. The whole procedure could be divided into two steps. The first step is to interpret original data of sensors into possible spatial points of buried utilities and the second one is to fuse these points to evaluate the trend curve of pipes. For the first step,specific interpreting methods are designed for each sensor. For the second step,a novel data fusion method,named Pipe Revising and Evolving( PRE),is proposed to deal with the false-alarmed problem caused by the noise of data. The theme of it is to enumerate many hypothetical pipes and to output some reliable ones with a selection mechanism. The method could iteratively update the result,that is,the finish of a new section of scanning route would trigger it to update the historical speculation to present a better one. Experimental results on synthetic and real data demonstrate that our solution could detect the buried network effectively and has the competitive performance in terms of solving false-alarmed problem of spatial points.
引文
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[14] Zhao Min-da,Li Feng,Sun Tao. An approach to detect grid-like radars based on Harris corner recognition[J]. Journal of Chinese Computer Systems,2016,37(5):1039-1043.
[15] Zhou X,Chen H,Li J. An automatic GPR b-scan image interpreting model[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,56(6):3398-3412.
[11]王勇,陈伟.近间距平行地下管线探测方法研究[J].测绘通报,2011,3(40):22-25.
[14]赵敏达,李峰,孙涛.结合Harris角点的栅格状雷达的检测方法[J].小型微型计算机系统,2016,37(5):1039-1043.
[2] Costello S B,Chapman D N,Rogers C D F,et al. Underground asset location and condition assessment technologies[J]. Tunnelling and Underground Space Technology,2007,22(5):524-542.
[3] Eide E S,Hjelmstad J F. 3D utility mapping using electronically scanned antenna array[C]. Ninth International Conference on Ground Penetrating Radar,International Society for Optics and Photonics,2002,4758:192-197.
[4] Li S,Cai H,Kamat V R. Uncertainty-aware geospatial system for mapping and visualizing underground utilities[J]. Automation in Construction,2015,53:105-119.
[5] Hafsi M,Bolon P,Dapoigny R. Detection and localization of underground networks by fusion of electromagnetic signal and GPR images[C]. Thirteenth International Conference on Quality Control by Artificial Vision 2017,International Society for Optics and Photonics,2017,10338:1-3.
[6] Dou Q,Wei L,Magee D R,et al. 3D buried utility location using a marching-cross-section algorithm for multi-sensor data fusion[J].Sensors,2016,16(11):1827-1851.
[7] Maas C,Schmalzl J. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar[J]. Computers&Geosciences,2013,58(2):116-125.
[8] Mertens L,Persico R,Matera L,et al. Automated detection of reflection hyperbolas in complex GPR images with no a priori knowledge on the medium[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(1):580-596.
[9] Pasolli E,Melgani F,Donelli M. Automatic analysis of GPR images:a pattern-recognition approach[J]. IEEE Transactions on Geoscience and Remote Sensing,2009,47(7):2206-2217.
[10] Shihab S,Al-Nuaimy W. Radius estimation for cylindrical objects detected by ground penetrating radar[J]. Subsurface Sensing Technologies and Applications,2005,6(2):151-166.
[11] Wang Yong,Chen Wei. Research on detection of parallel underground pipeline with small intervals[J]. Bulletin of Surveying and M apping,2011,3(40):22-25.
[12] Deng X,Lu T,Chang X,et al. A structured total least squares algorithm for spatial straight line fitting[C]. International Conference on Intelligent Earth Observing and Applications 2015,International Society for Optics and Photonics,2015,9808:1-8.
[13] Liu R,Issa R R A. 3D visualization of sub-surface pipelines in connection with the building utilities:Integrating GIS and BIM for facility management[M]. Computing in Civil Engineering,2012:341-348.
[14] Zhao Min-da,Li Feng,Sun Tao. An approach to detect grid-like radars based on Harris corner recognition[J]. Journal of Chinese Computer Systems,2016,37(5):1039-1043.
[15] Zhou X,Chen H,Li J. An automatic GPR b-scan image interpreting model[J]. IEEE Transactions on Geoscience and Remote Sensing,2018,56(6):3398-3412.
[11]王勇,陈伟.近间距平行地下管线探测方法研究[J].测绘通报,2011,3(40):22-25.
[14]赵敏达,李峰,孙涛.结合Harris角点的栅格状雷达的检测方法[J].小型微型计算机系统,2016,37(5):1039-1043.
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