Volume III-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 167-174, 2016
https://doi.org/10.5194/isprs-annals-III-5-167-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-5, 167-174, 2016
https://doi.org/10.5194/isprs-annals-III-5-167-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2016

06 Jun 2016

AUTONOMOUS ROBOTIC INSPECTION IN TUNNELS

E. Protopapadakis1, C. Stentoumis2, N. Doulamis2, A. Doulamis1, K. Loupos1, K. Makantasis3, G. Kopsiaftis2, and A. Amditis1 E. Protopapadakis et al.
  • 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.