  This article proposes a Deep Learning (DL) method to enable fully autonomous
flights for low-cost Micro Aerial Vehicles (MAVs) in unknown dark underground
mine tunnels. This kind of environments pose multiple challenges including lack
of illumination, narrow passages, wind gusts and dust. The proposed method does
not require accurate pose estimation and considers the flying platform as a
floating object. The Convolutional Neural Network (CNN) supervised image
classifier method corrects the heading of the MAV towards the center of the
mine tunnel by processing the image frames from a single on-board camera, while
the platform navigates at constant altitude and desired velocity references.
Moreover, the output of the CNN module can be used from the operator as means
of collision prediction information. The efficiency of the proposed method has
been successfully experimentally evaluated in multiple field trials in an
underground mine in Sweden, demonstrating the capability of the proposed method
in different areas and illumination levels.
