ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 167-174, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
06 Jun 2016
E. Protopapadakis1, C. Stentoumis2, N. Doulamis2, A. Doulamis1, K. Loupos1, K. Makantasis3, G. Kopsiaftis2, and A. Amditis1 1Institute of Communication and Computer Systems, Zografou 157 80, Athens, Greece
2Lab. of Photogrammetry, National Technical University of Athens, 15780, Greece
3Tech. Univ. of Crete, Chania, Greece
Keywords: Inspection, Robotics, Automation, Deep Learning, Scanner, Structural Assessment, Defects, Reconstruction Abstract. In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel’s lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.
Conference paper (PDF, 1225 KB)

Citation: Protopapadakis, E., Stentoumis, C., Doulamis, N., Doulamis, A., Loupos, K., Makantasis, K., Kopsiaftis, G., and Amditis, A.: AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 167-174,, 2016.

BibTeX EndNote Reference Manager XML