3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion

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.

3D mapping from partial observations: An application to utility mapping

“…Second, this work is related to existing work in the area of buried utility mapping [12][13][14]. [12,13] focus on sensor data fusion and do not use any information from street furniture surveys in the mapping. The system proposed by [14] is for creating a map of buried utilities based on manhole observations.…”

Components of a Technology Roadmap for Automated Excavation

“…Excavator collision with buried utility networks and assets results in damage to pipes and cables, worker injuries and deaths. Various geophysical sensors have been utilised to locate buried utilities, such as passive magnetic fields for electrical cable detection, vibroacoustic methods for pipe detection, incorporated small sensors for water pipe detection, low-frequency electromagnetic sensors, and Ground Penetrating Radar [29]. Kolera & Bernold [30] reviewed current underground utility detection technologies and geophysical non-invasive methods.…”

“…Single geophysical techniques are not able to identify all types utility in varying soil conditions [32]. The multisensory approach is based on the combined application of geophysical technologies and if multisensor data is integrated appropriately, a more accurate and complete buried utility network representation can be built [29], [32].…”

Application of Kinematic GPR-TPS Model with High 3D Georeference Accuracy for Underground Utility Infrastructure Mapping: A Case Study from Urban Sites in Celje, Slovenia

“…As is seen from Table 2 some studies were focused on determining the positional and height accuracy of UUI in a test environment [5,18,19,27] and a few in a real urban site [23][24][25][26]. None of them, other than Šarlah et al [5] and Gabryś et al [27], use TPS to determine the position of the GPR antenna in kinematic mode.…”

“…None of them, other than Šarlah et al [5] and Gabryś et al [27], use TPS to determine the position of the GPR antenna in kinematic mode. In Dou et al [24] and Bilal et al [23], a unique marching cross-section algorithm and Bayesian mapping model with implementing various machine-learning techniques for automatically locating UUI segments by fusing data from multiple sensors are introduced. All profiles measured with a GPR and other sensors were later calibrated on a known previously established coordinate system determinate with TPS.…”