Abstract
This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu’s threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.
This work has been partially funded by the European Unions Horizon 2020 Research and Innovation Programme under the Grant Agreement No. 730302 SIMS.
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Mansouri, S.S., Castaño, M., Kanellakis, C., Nikolakopoulos, G. (2019). Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_16
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