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.
School of Computing, University of Leeds, Leeds LS2 9JT, UK. Q.Dou@leeds.ac.uk.
Qingxu
Q
Dou
School of Computing, University of Leeds, Leeds LS2 9JT, UK. L.J.Wei@leeds.ac.uk.
Lijun
L
Wei
School of Computing, University of Leeds, Leeds LS2 9JT, UK. D.R.Magee@leeds.ac.uk.
Derek R
DR
Magee
School of Electronic, Electrical and Computing Engineering, University of Birmingham, Birmingham B15 2TT, UK. P.R.Atkins@bham.ac.uk.
Phil R
PR
Atkins
School of Civil Engineering, University of Birmingham B15 2TT, UK. D.N.Chapman@bham.ac.uk.
David N
DN
Chapman
School of Civil Engineering, University of Birmingham B15 2TT, UK. Giulio.Curioni@gmail.com.
Giulio
G
Curioni
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. kfg@ecs.soton.ac.uk.
Kevin F
KF
Goddard
School of Electronic, Electrical and Computing Engineering, University of Birmingham, Birmingham B15 2TT, UK. F.Hayati@bham.ac.uk.
Farzad
F
Hayati
School of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK. C.H.J.Jenks@bath.ac.uk.
Hugo
H
Jenks
School of Civil Engineering, University of Birmingham B15 2TT, UK. N.Metje@bham.ac.uk.
Nicole
N
Metje
Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK. jmm@isvr.soton.ac.uk.
Jennifer
J
Muggleton
School of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK. S.R.Pennock@bath.ac.uk.
Steve R
SR
Pennock
Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK. er@isvr.soton.ac.uk.
Emiliano
E
Rustighi
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. sgs@ecs.soton.ac.uk.
Steven G
SG
Swingler
School of Civil Engineering, University of Birmingham B15 2TT, UK. C.D.F.Rogers@bham.ac.uk.
Christopher D F
CD
Rogers
School of Computing, University of Leeds, Leeds LS2 9JT, UK. A.G.Cohn@leeds.ac.uk.
Anthony G
AG
Cohn
buried utility location; marching-cross-section algorithm; multi-sensor data fusion