3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion
We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/11/1827 |
| _version_ | 1798026191410561024 |
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| author | Qingxu Dou Lijun Wei Derek R. Magee Phil R. Atkins David N. Chapman Giulio Curioni Kevin F. Goddard Farzad Hayati Hugo Jenks Nicole Metje Jennifer Muggleton Steve R. Pennock Emiliano Rustighi Steven G. Swingler Christopher D. F. Rogers Anthony G. Cohn |
| author_facet | Qingxu Dou Lijun Wei Derek R. Magee Phil R. Atkins David N. Chapman Giulio Curioni Kevin F. Goddard Farzad Hayati Hugo Jenks Nicole Metje Jennifer Muggleton Steve R. Pennock Emiliano Rustighi Steven G. Swingler Christopher D. F. Rogers Anthony G. Cohn |
| author_sort | Qingxu Dou |
| collection | DOAJ |
| description | We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation. |
| first_indexed | 2024-04-11T18:31:23Z |
| format | Article |
| id | doaj.art-d0f8726c032d4883afe1f26380432dd4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:31:23Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d0f8726c032d4883afe1f26380432dd42022-12-22T04:09:27ZengMDPI AGSensors1424-82202016-11-011611182710.3390/s16111827s161118273D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data FusionQingxu Dou0Lijun Wei1Derek R. Magee2Phil R. Atkins3David N. Chapman4Giulio Curioni5Kevin F. Goddard6Farzad Hayati7Hugo Jenks8Nicole Metje9Jennifer Muggleton10Steve R. Pennock11Emiliano Rustighi12Steven G. Swingler13Christopher D. F. Rogers14Anthony G. Cohn15School of Computing, University of Leeds, Leeds LS2 9JT, UKSchool of Computing, University of Leeds, Leeds LS2 9JT, UKSchool of Computing, University of Leeds, Leeds LS2 9JT, UKSchool of Electronic, Electrical and Computing Engineering, University of Birmingham, Birmingham B15 2TT, UKSchool of Civil Engineering, University of Birmingham B15 2TT, UKSchool of Civil Engineering, University of Birmingham B15 2TT, UKSchool of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKSchool of Electronic, Electrical and Computing Engineering, University of Birmingham, Birmingham B15 2TT, UKSchool of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UKSchool of Civil Engineering, University of Birmingham B15 2TT, UKInstitute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UKSchool of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UKInstitute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UKSchool of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UKSchool of Civil Engineering, University of Birmingham B15 2TT, UKSchool of Computing, University of Leeds, Leeds LS2 9JT, UKWe address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation.http://www.mdpi.com/1424-8220/16/11/1827buried utility locationmarching-cross-section algorithmmulti-sensor data fusion |
| spellingShingle | Qingxu Dou Lijun Wei Derek R. Magee Phil R. Atkins David N. Chapman Giulio Curioni Kevin F. Goddard Farzad Hayati Hugo Jenks Nicole Metje Jennifer Muggleton Steve R. Pennock Emiliano Rustighi Steven G. Swingler Christopher D. F. Rogers Anthony G. Cohn 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion Sensors buried utility location marching-cross-section algorithm multi-sensor data fusion |
| title | 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion |
| title_full | 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion |
| title_fullStr | 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion |
| title_full_unstemmed | 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion |
| title_short | 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion |
| title_sort | 3d buried utility location using a marching cross section algorithm for multi sensor data fusion |
| topic | buried utility location marching-cross-section algorithm multi-sensor data fusion |
| url | http://www.mdpi.com/1424-8220/16/11/1827 |
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