MAV Navigation in Unknown Dark Underground Mines Using Deep Learning

TL;DR

This paper introduces a deep learning-based navigation system for micro aerial vehicles to autonomously explore dark, unknown underground mines without relying on precise pose estimation, using CNNs for collision avoidance and tunnel centering.

Contribution

It presents a novel CNN-based method for MAV navigation in dark underground mines that does not depend on accurate pose estimation and is validated through real-world field trials.

Findings

Successful autonomous navigation in dark mine tunnels

Effective collision prediction using CNN outputs

Robust performance across different illumination conditions

Abstract

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.