Abstract
This paper presents a robotic platform, capable of autonomous tunnel inspection, developed under ROBO-SPECT European union funded research project. The robotic vehicle consists of a robotized production boom lift, a high precision robotic arm, advanced computer vision systems, a 3D laser scanner and an ultrasonic sensor. The autonomous inspection of tunnels requires advanced capabilities of the robotic vehicle and the computer vision sub-system. The robot localization in underground spaces and on long linear paths is a challenging task, as well as the mm accurate positioning of a robotic tip installed on a five-ton crane vehicle. Moreover, the 2D and 3D vision tasks, which support the inspection process, should tackle with poor and variable lighting conditions, low textured lining surfaces and the need for high accuracy. This contribution describes the final robotic vehicle and the developments as designed for concrete lining tunnel inspection. Results from the validation and benchmarking of the system are also included following the final tests at the operating Egnatia Motorway tunnels in northern Greece.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning—a new frontier in artificial intelligence research [Research Frontier]. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)
Botelho, F.: A light at the end of the tunnel. Public Roads 65(1) (2001)
Brosnan, T., Sun, D.W.: Improving quality inspection of food products by computer vision—a review. J. Food Eng. 61(1), 3–16 (2004)
Brownjohn, J.M.W.: Structural health monitoring of civil infrastructure. Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci. 365(1851), 589–622 (2007)
Delatte, Jr., N.J.: Beyond failure. Forensic case studies for civil engineers (2009)
Frangopol, D.M., Liu, M.: Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost. Struct. Infrastruct. Eng. 3(1), 29–41 (2007)
Fujita, Y., Mitani, Y., Hamamoto, Y.: A method for crack detection on a concrete structure. Proc. 18th Int. Conf. Pattern Recogn. 3, 901–904 (2006)
Georgousis, S., Stentoumis, C., Doulamis, N., Voulodimos, A.: A hybrid algorithm for dense stereo correspondences in challenging indoor scenes. IEEE International Conference on Imaging Systems and Technology IST 2016, Oct., Chania, Greece (2016)
Gutchess, D., Trajkovics, M., Cohen-Solal, E., Lyons, D., Jain, A.K.: A background model initialization algorithm for video surveillance. IEEE Int Conf Comput Vis ICCV 1, 733–740 (2001)
Huet, C., Mastroddi, Franco: Autonomy for underwater robots—a European perspective. Auton. Robots 40(7), 1113–1118 (2016)
Ibrahim, Y.M., Lukins, T.C., Zhang, X., Trucco, E., Kaka, A.P.: Towards automated progress assessment of workpackage components in construction projects using computer vision. Adv. Eng. Inform. 23(1), 93–103 (2009)
Jahanshahi, M.R., Masri, S.F.: Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Autom. Constr. 22, 567–576 (2012)
Jeong, D.H., Kim, Y.R., Cho, I.-S., Kim, E.J., Lee, K.M., Jin, K.W., Song, C.G.: Real-time image scanning system for detecting tunnel cracks using linescan cameras. J. Korea Multimed. Soc. 10(6) (2007)
Klammer, D.M., Bauer, F., Dietzel, C., Köhler, M., Leis, S.: Thaumasite Formation from Sulphate Attack (TSA). Case Study at Austrian Tunnel Sites. www.dmg-home.de/DMG-CD/filedir/365_abstract.pdf. Accessed on 12/2/2012)
Koch, C., Paal, S.G., Rashidi, A., Zhu, Z., König, M., Brilakis, I.: Achievements and challenges in machine vision-based inspection of large concrete structures. Adv. Struct. Eng. 17(3), 303–318 (2014)
Loupos, K., Amditis, A., Chrobocinski, P., Montero, R., Belsito, L., Lopez, R., Doulamis, N.: Autonomous robot for tunnel inspection and assessment. 6th International Symposium on Tunnels and Underground Structures in See Urban, Underground Structures. In Karst, Radisson Blu Resort, Split, Croatia, March 16–18 (2016)
Loupos, K., Amditis, A., Stentoumis, C., Chrobocinski, P., Victores, J., Wietek, M., Panetsos, P., Roncaglia, A., Camarinopoulos, S., Kallidromitis, V., Bairaktaris, D., Komodakis, N., Lopez, R.: Robotic intelligent vision and control for tunnel inspection and evaluation—the ROBINSPECT EC Project. IEEE International Symposium on Robotic and Sensors Environments 16–18 October, 2014. Timisoara, Romania (2014)
Loupos, K., Amditis, A., Stentoumis, C.: Integrated robotic system for tunnel structural assessment—the ROBO-SPECT EC project. World Tunnel Congress (2015)
Makantasis, K., Protopapadakis, E., Doulamis, A., Doulamis, N., Loupos, C.: Deep convolutional neural networks for efficient vision based tunnel inspection. In: 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 335–342 (2015)
Metta, G., Fitzpatrick, P., Natale, L.: YARP: yet another robot platform. Int J Adv Robot Syst 3(1), 43–48 (2006)
Montero, R., Victores, J.G., Martínez, S., Jardón, A., Balaguer, C.: Past, present and future of robotic tunnel inspection. Autom. Constr. 59, 99–112 (2015)
Paar,G., Kontrus, H.: Three-dimensional tunnel reconstruction using photogrammetry and laser scanning. 3rd Nordost, 9. Anwendungsbezogener Workshop zur Erfassung, Modellierung, Verarbeitung und Auswertung von 3D-Daten, Berlin (2006)
Protopapadakis, E., Doulamis, N.: Image based approaches for tunnels’ defects recognition via robotic inspectors. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Pavlidis, I., Feris, R., McGraw, T., Elendt, M., Kopper, R., Ragan, E., Ye, Z., Weber, G. (eds.) Advances in visual computing, pp. 706–716. Springer, Berlin (2015)
Protopapadakis, E., Makantasis, K., Kopsiaftis, G., Doulamis, N.D., Amditis, A.: Crack identification via user feedback, convolutional neural networks and laser scanners for tunnel infrastructures. In: RGB-SpectralImaging, Rome (2016)
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, Vol. 3, No. 3.2, p. 5 (2009)
Rudol, P., Doherty, P.: Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery. IEEE Aerospace Conference (2008)
Soga, K., Chaiyasarn, K., Viola, F., Yan, J., Seshia, A., Cipolla, R.: Innovation in monitoring technologies for underground structures. In: Proceedings of the 1st Int. Conf. Information Technology in Geo-Engineering, (ICITG) Shangai, IOS Press, pp. 3–18 (2010)
Stentoumis, C., Amditis, A., Karras, G.: Census-based cost on gradients for matching under illumination differences. IEEE International Conference 3D Vision, Lyon, pp. 224–231 (2015)
Stentoumis, C., Protopapadakis, E., Doulamis, A., Doulamis, N.: A holistic approach for inspection of civil infrastructures based on computer vision techniques. In: ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information, pp 131–138 (2016)
Stentoumis, C., Grammatikopoulos, L., Kalisperakis, I., Karras, G.: On accurate dense stereo-matching using a local adaptive multi-cost approach. ISPRS J Photogramm. Remote Sens. 91, 29–49 (2014). doi:10.1016/j.isprsjprs.2014.02.006
Sulibhavi, G.R., Parks, W.A.: Advanced methods for tunnel assessment. In: Proceedings of the World Tunnel Congress 2007 and 33rd ITA/AITES Annual General Assembly, Prague (2007)
Sumitro, S., Okam, T., Inaudi, D.: Intelligent sensory technology for health monitoring based maintenance of infrastructures. 11th Spie’s Annual International Symposium On Smart Structures And Materials, March 14–18, San Diego, USA (2004)
Victores, J.G., Martínez, S., Balaguer, C.: Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration. Autom. Constr. 20(5), 629–636 (2011)
Voulodimos, A., Kosmopoulos, D., Vasileiou, G., Sardis, E., Anagnostopoulos, V., Lalos, C., Doulamis, A., Varvarigou, T.: Large-scale multimedia data collections: a threefold dataset for activity and workflow recognition in complex industrial environments. IEEE Multimedia Magazine, pp. 42–52, July–September (2012)
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3–19 (2013)
Yao, F.-H., Shao, G.-F., Yamada, H., Kato, K.: Development of an automatic concrete-tunnel inspection system by an autonomous mobile robot. 9th IEEE International Workshop on Robot and Human Interactive Communication (2009)
Yoon, J.-S., Sagong, M., Lee, J.S., Lee, K.-S.: Feature extraction of a concrete tunnel liner from 3D laser scanning data. NDT and E International, 42(2), March, pp. 97–105 (2009)
Yu, S., Jang, J.-H., Han, C.-S.: Auto Inspection System Using a Mobile Robot for Detecting Concrete Cracks in a Tunnel. Autom. Constr. 16, 255–261 (2007a)
Yu, S.-N., Jang, J.-H., Han, C.-S.: Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Autom. Constr. 16(3), 255–261 (2007b)
Acknowledgements
The research leading to the above described results has received funding from the EC FP7-ICT project ROBO-SPECT (Contract no. 611145). Authors would like to thank all partners within the ROBO-SPECT consortium.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Loupos, K., Doulamis, A.D., Stentoumis, C. et al. Autonomous robotic system for tunnel structural inspection and assessment. Int J Intell Robot Appl 2, 43–66 (2018). https://doi.org/10.1007/s41315-017-0031-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41315-017-0031-9