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Sensors 2017, 17(9), 2075; https://doi.org/10.3390/s17092075

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles

1
Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, MI 49930, USA
2
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3
School of Civil and Environmental Engineering, Urban Design and Studies, Chung-Ang University, Seoul 06974, Korea
*
Author to whom correspondence should be addressed.
Received: 8 August 2017 / Revised: 3 September 2017 / Accepted: 7 September 2017 / Published: 11 September 2017
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [6321 KB, uploaded 11 September 2017]   |  

Abstract

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach. View Full-Text
Keywords: structural health monitoring; system identification; computer vision; Unmanned Aerial Vehicles structural health monitoring; system identification; computer vision; Unmanned Aerial Vehicles
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Yoon, H.; Hoskere, V.; Park, J.-W.; Spencer, B.F., Jr. Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles. Sensors 2017, 17, 2075.

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