使用深度学习在未知的黑暗地下矿井中进行MAV导航
MAV Navigation in Unknown Dark Underground Mines Using Deep Learning
摘要：
本文提出了一种深度学习（DL）方法，使低成本微型飞行器（MAV）能够在未知的黑暗地下矿井隧道中实现完全自主飞行。这种环境带来了多重挑战，包括缺乏照明，狭窄的通道，阵风和灰尘。该方法不需要精确的位姿估计，并将飞行平台视为漂浮物体。卷积神经网络（CNN）监督图像分类器方法通过处理来自单个机载相机的图像帧来校正MAV朝向矿井隧道中心的航向，同时平台以恒定高度和期望的速度参考导航。此外，CNN模块的输出可以从操作者用作碰撞预测信息的手段。所提出的方法的效率已成功地在瑞典地下矿山的多个现场试验中进行了实验评估，证明了所提出的方法在不同区域和照明水平下的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
