MAV Navigation in Unknown Dark Underground Mines Using Deep Learning
This addresses the problem of enabling low-cost drones to operate autonomously in hazardous, unlit mine environments, representing a domain-specific incremental advancement.
The paper tackles autonomous navigation for micro aerial vehicles in dark, unknown underground mines by using a convolutional neural network to correct heading based on a single camera, achieving successful experimental evaluation in field trials with varying illumination.
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.