Automated Bridge Inspection Using Mobile Ground Robotics
Publication: Journal of Structural Engineering
Volume 145, Issue 11
Abstract
The use of mobile ground and aerial robotics presents a powerful means to augment current visual inspection practice by overcoming some common weaknesses related to accessibility, repeatability, hidden defect detection, and quantification. In this paper, a novel ground robotic bridge inspection platform consisting of a rugged mobile platform equipped with an onboard computer and several calibrated and time-synchronized sensors is introduced. This platform, along with custom localization and mapping software, is shown to produce high-quality 3D point cloud maps for the underside of typical concrete bridges, which are often the most challenging to inspect. The quality of these maps is compared to ground truth using terrestrial laser scanner measurements and the maps are shown to have an overall scale error of just 1.3%. The maps from the proposed system can be generated in real time, while continuously scanning the bridge, resulting in a significant reduction in the inspection time compared to using a terrestrial laser scanner (TLS). Additionally, this work presents a novel procedure for fully automated point cloud colorization and semiautomated defect localization and quantification. The former allows for realistic visual renderings of the bridge for remote inspection by offsite inspectors. The latter allows for increased accuracy of defect quantification compared to traditional inspections, while eliminating subjectivity between inspections and inspectors.
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Acknowledgments
The authors acknowledge the support provided by the Region of Waterloo and Mr. John Stephenson for assisting us with the selection of and access to the two bridge sites. Funding for this work through the Canada Foundation for Innovation, Natural Sciences Engineering Research Council of Canada (through postgraduate scholarships and Canada Research Chairs programs), and Ontario Graduate Scholarship programs is gratefully acknowledged.
References
Abdel-Qader, I., O. Abudayyeh, and M. E. Kelly. 2003. “Analysis of edge-detection techniques for crack identification in bridges.” J. Comput. Civ. Eng. 17 (4): 255–263. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255).
Adhikari, R. S., O. Moselhi, and A. Bagchi. 2014. “Image-based retrieval of concrete crack properties for bridge inspection.” Autom. Constr. 39: 180–194. https://doi.org/10.1016/j.autcon.2013.06.011.
Barfoot, T. D. 2018. State estimation for robotics. Cambridge, UK: Cambridge University Press.
Bentley, J. L. 1975. “Multidimensional binary search trees used for associative searching.” Commun. ACM 18 (9): 509–517. https://doi.org/10.1145/361002.361007.
Besl, P., and N. McKay. 1992. “A method for registration of 3-D shapes.” IEEE Trans. Pattern Anal. Mach. Intell. 14 (2): 239–256. https://doi.org/10.1109/34.121791.
Blanco, J. 2014. A tutorial on SE (3) transformation parameterizations and on-manifold optimization. Málaga, Spain: Univ. Malaga.
Bouguet, J.-Y. 2015. “Camera calibration toolbox for MATLAB.” Accessed July 10, 2018. http://www.vision.caltech.edu/bouguetj/calib_doc/.
Cadena, C., L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. Leonard. 2016. “Past, present, and future of simultaneous localization and mapping: Towards the robust-perception age.” IEEE Trans. Rob. 32 (6): 1309–1332. https://doi.org/10.1109/TRO.2016.2624754.
Campion, G., G. Bastin, and B. Dandrea-Novel. 1996. “Structural properties and classification of kinematic and dynamic models of wheeled mobile robots.” IEE Trans. Rob. Autom. 12 (1): 47–62. https://doi.org/10.1109/70.481750.
Charron, N., S. Phillips, and S. L. Waslander. 2018. “De-noising of Lidar point clouds corrupted by snowfall.” In Proc., 2018 15th Conf. on Computer and Robot Vision (CRV), 254–261. New York: IEEE.
Clemena, G., and W. T. McKeel. 1978. Detection of delamination in bridge decks with infrared thermography. Charlottesville, VA: Virginia Transportation Research.
Durrant-Whyte, H., and T. Bailey. 2006. “Simultaneous localization and mapping: Part I.” Rob. Autom. Mag. 13 (2): 99–110. https://doi.org/10.1109/MRA.2006.1638022.
Ellenberg, A., A. Kontsos, F. Moon, and I. Bartoli. 2016. “Bridge deck delamination identification from unmanned aerial vehicle infrared imagery.” Autom. Constr. 72: 155–165. https://doi.org/10.1016/j.autcon.2016.08.024.
Gillins, M. N., D. T. Gillins, and C. Parrish. 2016. “Cost-effective bridge safety inspections using unmanned aircraft systems (UAS).” In Geotechnical and Structural Engineering Congress August 2016, 1931–1940. Reston, VA: ASCE.
Goorts, K., S. Phillips, A. Ashasi-Sorkhabi, and S. Narasimhan. 2017. “Structural control using a deployable autonomous control system.” Int. J. Intell. Rob. Appl. 1 (3): 306–326. https://doi.org/10.1007/s41315-017-0025-7.
Gucunski, N., B. Basily, J. Kim, J. Yi, T. Duong, K. Dinh, S.-H. Kee, and A. Maher. 2017. “RABIT: Implementation, performance validation and integration with other robotic platforms for improved management of bridge decks.” Int. J. Intell. Rob. Appl. 1 (3): 1–16.
Heikkila, J., and O. Silven. 1997. “A four-step camera calibration procedure with implicit image correction.” In Proc., IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1106–1112. New York: IEEE.
Jahanshahi, M. R., S. F. Masri, C. W. Padgett, and G. S. Sukhatme. 2013. “An innovative methodology for detection and quantification of cracks through incorporation of depth perception.” Mach. Vision Appl. 24 (2): 227–241. https://doi.org/10.1007/s00138-011-0394-0.
Khaloo, A., and D. Lattanzi. 2017. “Hierarchical dense structure-from-motion reconstructions for infrastructure condition assessment.” J. Comput. Civ. Eng. 31 (1): 04016047. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000616.
Khaloo, A., D. Lattanzi, K. Cunningham, R. Dell’Andrea, and M. Riley. 2018. “Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling.” Struct. Infrastruct. Eng. 14 (1): 124–136. https://doi.org/10.1080/15732479.2017.1330891.
Kim, P., J. Chen, and Y. K. Cho. 2018. “Autonomous mobile robot localization and mapping for unknown construction environments.” In Proc., ASCE Construction Research Congress (CRC) 2018, 147–156. Reston, VA: ASCE.
Kim, P., B. Jingdao Chen, and B. K. Yong Cho. 2017. “Robotic sensing and object recognition from thermal-mapped point clouds.” Int. J. Intell. Rob. Appl. 1 (3): 243–254. https://doi.org/10.1007/s41315-017-0023-9.
Koch, C., K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth. 2015. “A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure.” Adv. Eng. Inf. 29 (2): 196–210. https://doi.org/10.1016/j.aei.2015.01.008.
La, H. M., R. S. Lim, B. B. Basily, N. Gucunski, J. G. Yi, A. Maher, F. A. Romero, and H. Parvardeh. 2013. “Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation.” IEEE-ASME Trans. Mechatron. 18 (6): 1655–1664. https://doi.org/10.1109/TMECH.2013.2279751.
Laefer, D. F., L. Truong-Hong, H. Carr, and M. Singh. 2014. “Crack detection limits in unit based masonry with terrestrial laser scanning.” NDT & E Int. 62: 66–76. https://doi.org/10.1016/j.ndteint.2013.11.001.
Law, D. W., D. Silcock, and L. Holden. 2018. “Terrestrial laser scanner assessment of deteriorating concrete structures.” Struct. Control Health Monit. 25 (5): e2156. https://doi.org/10.1002/stc.2156.
Lehtola, V. V., et al. 2017. “Comparison of the selected state-of-the-art 3D indoor scanning and point cloud generation methods.” Remote Sens. 9 (8): 796. https://doi.org/10.3390/rs9080796.
Liu, W., S. Chen, and E. Hauser. 2011. “LiDAR-based bridge structure defect detection.” Exp. Tech. 35 (6): 27–34. https://doi.org/10.1111/j.1747-1567.2010.00644.x.
Liu, Y.-F., S. Cho, and J.-S. Fan. 2016. “Concrete crack assessment using digital image processing and 3D scene reconstruction.” J. Comput. Civ. Eng. 30 (1): 04014124. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000446.
Ministry of Transportation Ontario. 2008. Ontario structural inspection manual (OSIM). St. Catharines, ON, Canada: Publications Ontario.
Moller, P. 2008. CALTRANS bridge inspection aerial robot. Davis, CA: Univ. of California at Davis.
Moore, M., B. Phares, B. Graybeal, D. Rolander, and G. Washer. 2001. Reliability of visual inspection for highway bridges. McLean, VA: Federal Highway Administration.
Moore, T., and D. Stouch. 2014. “A generalized extended kalman filter implementation for the robot operating system.” In Proc., 13th Int. Conf. on Intelligent Autonomous Systems (IAS-13). New York: Springer.
Omar, T., M. L. Nehdi, and T. Zayed. 2018. “Infrared thermography model for automated detection of delamination in RC bridge decks.” Constr. Build. Mater. 168: 313–327. https://doi.org/10.1016/j.conbuildmat.2018.02.126.
Otsu, N. 1979. “A threshold selection method from gray-level histograms.” IEE Trans. Syst. Man Cybern. 9 (1): 62–66. https://doi.org/10.1109/TSMC.1979.4310076.
Parker, J. R. 2011. Algorithms for image processing and computer vision. Indianapolis: Wiley.
Prasanna, P., K. J. Dana, N. Gucunski, B. B. Basily, H. M. La, R. S. Lim, and H. Parvardeh. 2016. “Automated crack detection on concrete bridges.” IEEE Trans. Autom. Sci. Eng. 13 (2): 591–599. https://doi.org/10.1109/TASE.2014.2354314.
Rusu, R. B., and S. Cousins. 2011. “3D is here: Point cloud library (PCL).” In Proc., IEEE Int. Conf. on Robotics and Automation, 1–4. New York: IEEE.
Siegwart, R., and I. R. Nourbakhsh. 2004. Introduction to autonomous mobile robots. Cambridge, MA: MIT Press, Massachusetts Institute of Technology.
Sturm, P. 2014. “Pinhole camera model.” In Computer vision. Boston: Springer.
Thrun, S., Y. Liu, D. Koller, A. Y. Ng, Z. Ghahramani, and H. Durrant-Whyte. 2004. “Simultaneous localization and mapping with sparse extended information filters.” Int. J. Rob. Res. 23 (7–8): 693–716. https://doi.org/10.1177/0278364904045479.
Torok, M. M., M. Golparvar-Fard, and K. B. Kochersberger. 2014. “Image-based automated 3D crack detection for post-disaster building assessment.” J. Comput. Civ. Eng. 28 (5): A4014004. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000334.
Turkan, Y., S. Laflamme, and L. Tan. 2016. Terrestrial laser scanning-based bridge structural condition assessment. Ames, IA: Iowa State Univ.
Valença, J., I. Puente, E. Júlio, H. González-Jorge, and P. Arias-Sánchez. 2017. “Assessment of cracks on concrete bridges using image processing supported by laser scanning survey.” Constr. Build. Mater. 146: 668–678. https://doi.org/10.1016/j.conbuildmat.2017.04.096.
Yeum, C. M., and S. J. Dyke. 2015. “Vision-based automated crack detection for bridge inspection.” Comput.-Aided Civ. Infrastruct. Eng. 30 (10): 759–770. https://doi.org/10.1111/mice.12141.
Zhang, Z. 2002. “A flexible new technique for camera calibration.” IEEE Trans. Pattern Anal. Mach. Intell. 22 (11): 1330–1334. https://doi.org/10.1109/34.888718.
Zink, J., and B. Lovelace. 2015. Unmanned aerial vehicle bridge inspection demonstration project. Washington, DC: Transportation Research Board.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 145 • Issue 11 • November 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 24, 2018
Accepted: Mar 6, 2019
Published online: Sep 11, 2019
Published in print: Nov 1, 2019
Discussion open until: Feb 11, 2020
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