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3D buried utility location using a marching-cross-section algorithm for multi-sensor data fusion

3D buried utility location using a marching-cross-section algorithm for multi-sensor data fusion
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 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
1424-8220
1-24
Dou, Qingxu
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Wei, Lijun
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Magee, Derek R.
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Atkins, Phil R.
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Chapman, David N.
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Curioni, Giulio
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Goddard, Kevin F.
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Hayati, Farzad
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Jenks, Hugo
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Metje, Nicole
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Muggleton, Jennifer
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Pennock, Steve R.
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Rustighi, Emiliano
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Swingler, Steven G.
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Rogers, Christopher D. F.
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Cohn, Anthony G.
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Dou, Qingxu
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Wei, Lijun
f1a96ee8-7607-4fe8-9a3f-94e5223e8cfe
Magee, Derek R.
b03ba51d-08ae-4f0e-ba37-2783ac6d0555
Atkins, Phil R.
dc1d4ac2-635c-4ce2-bbc4-80186d042921
Chapman, David N.
f199a759-a616-4459-890e-07e87c568abb
Curioni, Giulio
b061a125-f492-4acd-85eb-4d2f07e2477f
Goddard, Kevin F.
fe2a2194-8b55-43c1-bdca-341691b71b2d
Hayati, Farzad
7cc33cca-b591-49fb-be97-787477d378da
Jenks, Hugo
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Metje, Nicole
de17d374-f685-4543-9d93-5317766d585a
Muggleton, Jennifer
2298700d-8ec7-4241-828a-1a1c5c36ecb5
Pennock, Steve R.
e9daf224-cb16-42e1-988e-cb60a6207b77
Rustighi, Emiliano
9544ced4-5057-4491-a45c-643873dfed96
Swingler, Steven G.
4f13fbb2-7d2e-480a-8687-acea6a4ed735
Rogers, Christopher D. F.
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Cohn, Anthony G.
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Dou, Qingxu, Wei, Lijun, Magee, Derek R., Atkins, Phil R., Chapman, David N., Curioni, Giulio, Goddard, Kevin F., Hayati, Farzad, Jenks, Hugo, Metje, Nicole, Muggleton, Jennifer, Pennock, Steve R., Rustighi, Emiliano, Swingler, Steven G., Rogers, Christopher D. F. and Cohn, Anthony G. (2016) 3D buried utility location using a marching-cross-section algorithm for multi-sensor data fusion. Sensors, 16 (11), 1-24, [1827]. (doi:10.3390/s16111827).

Record type: Article

Abstract

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

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Accepted/In Press date: 24 October 2016
e-pub ahead of print date: 2 November 2016
Published date: 2 November 2016
Organisations: Electronics & Computer Science, Dynamics Group

Identifiers

Local EPrints ID: 403069
URI: http://eprints.soton.ac.uk/id/eprint/403069
ISSN: 1424-8220
PURE UUID: 7c86dc6f-9432-4bf7-a8ce-ee609a9115f6
ORCID for Emiliano Rustighi: ORCID iD orcid.org/0000-0001-9871-7795

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Date deposited: 23 Nov 2016 14:31
Last modified: 08 May 2020 00:29

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