Abstract
In the past few years, computer vision-based applications are widely used to determine damages in structural components. However, due to the involvement of certain levels of manual measurements, there still exists potential for improvements in the quantification of damage caused in a structure. Therefore, in this study, key-point point detection using fiducial markers is used to reduce the error in quantification of overall displacement and deformed profile of structural components (e.g., beams). This study demonstrates the use of a fiducial marker-based approach to quantify the deformation and bending profile in beam elements without physically disturbing the specimen. The visual fiducial marker (e.g., AprilTag) system has been extensively used in robotics for purposes ranging from localization of robots to increasing precision while tagging different objects in the robot’s surroundings. These AprilTag are attached to the surface of the specimen before loading. In this research, while the load is applied on the beam, the behavior of the specimen is then recorded by using a high-resolution digital camera during the experiment. Camera calibration using a checkerboard is performed to determine the extrinsic parameters (e.g., camera pose). With the help of attached AptilTags on the surface of the specimen, pixel values at the four corners and the center of each of the tags are detected during the experiment. These pixel locations are then used to get the corresponding world coordinate system by minimizing the loss function. Unlike normal bundle adjustment algorithms, camera parameters are treated as known parameters during the optimization process. Primary results show potential of proposed method in obtaining robust measurements.
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References
Hoult, N.A., Glisic, B.: Editorial: Structural health monitoring of bridges. Front. Built Environ. 6, 17 (2020)
Geoffrine, J.M.C., Geetha, V.: Energy optimization with higher information quality for SHM application in wireless sensor networks. IEEE Sensors J. 19, 3513–3520 (2019)
Kwiecień, A., Krajewski, P., Hojdys, Ł., Tekieli, M., Słoński, M.: Flexible adhesive in composite-to-brick strengthening–experimental and numerical study. Polymers 10(4), 356 (2018)
Choi, J.-H., No, Y.-S., So, J.-P., Lee, J.M., Kim, K.-H., Hwang, M.-S., Kwon, S.-H., Park, H.-G.: A high-resolution strain-gauge nanolaser. Nat. Commun. 7(1), 1–8 (2016)
Park, J.-W., Moon, D.-S., Yoon, H., Gomez, F., Spencer Jr., B.F., Kim, J.R.: Visual–inertial displacement sensing using data fusion of vision-based displacement with acceleration. Struct. Control Health Monit. 25(3), e2122 (2018) e2122 STC-17-0066.R2
Khuc, T., Catbas, F.N.: Computer vision-based displacement and vibration monitoring without using physical target on structures. Struct. Infrastruct. Eng. 13(4), 505–516 (2017)
Lee, J., Lee, K.-C., Cho, S., Sim, S.-H.: Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges. Sensors 17(10), 2317 (2017)
Li, W.: A Geometry Reconstruction And Motion Tracking System Using Multiple Commodity RGB-D Cameras. PhD Thesis, The George Washington University (2020)
Ch’ng, S.-F., Sogi, N., Purkait, P., Chin, T.-J., Fukui, K.: Resolving marker pose ambiguity by robust rotation averaging with clique constraints. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 9680–9686. IEEE, Piscataway (2020)
Hu, X., Jakob, J., Per, K., Jiang, W.: Accurate fiducial mapping for pose estimation using manifold optimization. In: 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 206–212 (2018)
Mateos, L.A. (2020). AprilTags 3D: Dynamic Fiducial Markers for Robust Pose Estimation in Highly Reflective Environments and Indirect Communication in Swarm Robotics. ArXiv, abs/2001.08622.
Gupta, O., Hebbalaguppe, R.: FingertipCubes: An inexpensive DIY wearable for 6-DoF per fingertip pose estimation using a single RGB camera. In: SIGGRAPH Asia 2018 Posters, pp. 1–2 (2018)
Abbas, S.M., Aslam, S., Berns, K., Muhammad, A.: Analysis and improvements in AprilTag based state estimation. Sensors 19(24), 5480 (2019)
Valença, J., Carmo, R.N.F.: Method for assessing beam column joints in RC structures using photogrammetric computer vision. Struct. Control Health Monitor. 24(11), e2013 (2017)
Saravanan, T.J., Nishio, M.: Estimation of in-plane strain field using computer vision to capture local damages in bridge structures. In: Gelman, L., Martin, N., Malcolm, A.A., (Edmund) Liew, C.K. (eds.) Advances in Condition Monitoring and Structural Health Monitoring, pp. 461–469, Singapore. Springer, Singapore (2021)
Kromanis, R., Xu, Y., Lydon, D., Martinez del Rincon, J., Al-Habaibeh, A.: Measuring structural deformations in the laboratory environment using smartphones. Front. Built Environ. 5, 44 (2019)
Xiang, J., Yang, Z., Aguilar, J.: Structural health monitoring for mechanical structures using multi-sensor data. Int. J. Distrib. Sens. Netw. 14, 155014771880201, 09 (2018)
Kromanis, R., Al-Habaibeh, A.: Low cost vision-based systems using smartphones for measuring deformation in structures for condition monitoring and asset management. In: The 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure (2017)
Han, Y.; Relations between bundle-adjustment and epipolar-geometry-based approaches, and their applications to efficient structure from motion. Real-Time Imag. 10(6), 389–402 (2004)
Mühlich, M., Feiden, D., Mester, R.: A note on error metrics and optimization criteria in 3d vision. In: Proceedings of the IEEE Workshop on Vision, Modeling and Visualization, Erlangen, pp. 149–156 (1999)
Jepson, A.D., Heeger, D.J.: Linear subspace methods for recovering translational direction. Spatial Vision in Humans and Robots, pp. 39–62 (1992)
Kanatani, K.: 3-d interpretation of optical flow by renormalization. Int. J. Comput. Vis. 11(3), 267–282 (1993)
McLauchlan, P.F., Murray, D.W.: A unifying framework for structure and motion recovery from image sequences. In: Proceedings of IEEE International Conference on Computer Vision, pp. 314–320. IEEE, Piscataway (1995)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Analy. Mach. Intell. 22, 1330–1334, 12 (2000)
Acknowledgements
The authors appreciate the support from the University of Central Florida with SEED fund (AWD00000896) and would like to thank Graduate Assistant Mr. Seyed Sina Shid Moosavi for the help with the data analysis.
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Hassan, S.Z., Sun, P.“., Wang, T., Apostolakis, G., Mackie, K. (2023). Measuring Full-Field Deformation in Ultra-High-Performance Concrete Structural Components Using Tag-Based Robotic Vision. In: Di Maio, D., Baqersad, J. (eds) Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-04098-6_13
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