Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection

  • Sina Sharif MansouriEmail author
  • Miguel Castaño
  • Christoforos Kanellakis
  • George Nikolakopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


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.


Micro Aerial Vehicles (MAVs) Vision-based navigation Autonomous drift inspection Otsu’s theshold Moore-Neighborhood tracing 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sina Sharif Mansouri
    • 1
    Email author
  • Miguel Castaño
    • 2
  • Christoforos Kanellakis
    • 1
  • George Nikolakopoulos
    • 1
  1. 1.Robotics Team Department of Computer, Electrical and Space EngineeringLuleå University of TechnologyLuleåSweden
  2. 2.eMaintenance Group Division of Operation, Maintenance and Acoustics Department of Civil, Environmental and Natural Resources EngineeringLuleå University of TechnologyLuleåSweden

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