Drone-Based StereoDIC: System Development, Experimental Validation and Infrastructure Application

• Published in: Experimental mechanics Vol. 61; no. 6; pp. 981 - 996
• Main Authors: Kalaitzakis, M., Vitzilaios, N., Rizos, D. C., Sutton, M. A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 2021; Springer Nature B.V
• Subjects: Biomedical Engineering and Bioengineering; Characterization and Evaluation of Materials; Control; Data collection; Digital imaging; Drone aircraft; Drone vehicles; Dynamical Systems; Engineering; Image acquisition; Image transmission; Infrastructure; Inspection; Lasers; Nondestructive testing; Optical Devices; Optics; Photonics; Railroad ties; Research Paper; Solid Mechanics; Unmanned aircraft; Vibration; Vision systems; Drone; DroneDIC; Remote structural health monitoring; Digital image correlation
• DOI: 10.1007/s11340-021-00710-z
• ISSN: 0014-4851

Background Digital Image Correlation (DIC) is widely used for remote and non-destructive structural health evaluation of infrastructure. Current DIC applications are limited to relatively small areas of structures and require the use of stationary stereo vision camera systems that are not easy to transfer and deploy in remote areas. Objective The enclosed work describes the development and validation of an Unmanned Aircraft System (UAS, commonly known as drone) with an onboard stereo-vision system capable of acquiring, storing and transmitting images for analysis to obtain full-field, three-dimensional displacement and strain measurements. Methods The UAS equipped with a StereoDIC system has been developed and tested in the lab. The drone system, named DroneDIC, autonomously hovers in front of a prestressed railroad tie under pressure and DIC data are collected. A stationary DIC system is used in parallel to collect data for the railroad tie. We compare the data to validate the readings from the DroneDIC system. Results We present the analysis of the results obtained by both systems. Our study shows that the results we obtain from the DroneDIC system are similar to the ones gathered from the stationary DIC system. Conclusions This work serves as a proof of concept for the successful integration of DIC and drone technologies into the DroneDIC system. DroneDIC combines the high accuracy inspection capabilities of traditional stationary DIC systems with the mobility offered by drone platforms. This is a major step towards autonomous DIC inspection in portions of a structure where access is difficult via conventional methods.