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Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-9992-7791
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0003-0126-1897
2019 (English)Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Micro Aerial Vehicles (MAVs), Vision-based Navigation, Autonomous Drift Inspection, Otsu's Theshold, Moore-Neighborhood Tracing
National Category
Control Engineering Other Civil Engineering
Research subject
Control Engineering; Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-75270OAI: oai:DiVA.org:ltu-75270DiVA, id: diva2:1336607
Conference
12th International Conference on Computer Vision Systems (ICVS 2019)
Funder
EU, Horizon 2020, 730302Available from: 2019-07-09 Created: 2019-07-09 Last updated: 2019-08-13

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Type fulltextMimetype application/pdf

Authority records BETA

Mansouri, Sina SharifArranz, Miguel CastanoKanellakis, ChristoforosNikolakopoulos, George

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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