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Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0001-7631-002x
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0002-9992-7791
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0003-0126-1897
2019 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
Micro Aerial Vehicles (MAVs), Vision-based Navigation, Autonomous Drift Inspection, Otsu's Theshold, Moore-Neighborhood Tracing
HSV kategori
Forskningsprogram
Reglerteknik; Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-75270OAI: oai:DiVA.org:ltu-75270DiVA, id: diva2:1336607
Konferanse
12th International Conference on Computer Vision Systems (ICVS 2019)
Forskningsfinansiär
EU, Horizon 2020, 730302Tilgjengelig fra: 2019-07-09 Laget: 2019-07-09 Sist oppdatert: 2019-08-13

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Mansouri, Sina SharifArranz, Miguel CastanoKanellakis, ChristoforosNikolakopoulos, George

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