Abstract
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.
Highlights
Inspection and maintenance of transportation tunnels is a challenging and demanding process, due to the complex surveillance conditions and large scale requirements
Among them are the position of crack, semantic information on the state of the system and the required action/behaviour, laser status, the robotic tip trajectory estimations and other sensing information
A comparison between actual ground truth and model annotated images results in the confusion table creation; A 2×2 matrix that reports the number of false positives (FP), false negatives (FN), true positives (TP), and true negatives (TN)
Summary
Inspection and maintenance of transportation tunnels is a challenging and demanding process, due to the complex surveillance conditions and large scale requirements. Apart from being costly and time consuming, it is highly depended on the human subjectivity, resulting, in inaccuracies mainly regarding the positioning and the type of the problem. For this reason, several approaches have been proposed in the literature to increase the costefficiency in inspection through automation (Krisada, 2014). The presented work is a part an intelligent platform, with the focus on developing an integrated autonomous robotic system, which utilizes deep machine learning architectures and intelligent control tools to automatically detect tunnel defects and provide structural engineers with sufficient data to evaluate the stability of underground infrastructures and curry out the required maintenance procedures
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