Deep RC: Enabling Remote Control through Deep Learning
Deep RC: Enabling Remote Control through Deep Learning
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DOI：
10.1109/icuas.2019.8798325
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Jaron Ellingson;Gary Ellingson;Tim McLain
中科院分区：
文献类型：
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作者：
Jaron Ellingson;Gary Ellingson;Tim McLain
人类遥控（RC）飞行员有能力感知的位置和方向的飞机使用第三人称视角的视觉传感。虽然新手飞行员在学习控制遥控飞机时经常会遇到困难，但他们可以相对轻松地感知飞机的方向。在本文中，我们假设并证明了深度学习方法可用于模仿人类从单目图像感知飞机方向的能力。这项工作使用神经网络来直接感知飞机的姿态。该网络与更传统的图像处理方法相结合，用于飞机的视觉跟踪。来自卷积神经网络（CNN）的飞机轨迹和姿态测量值被组合在粒子滤波器中，该粒子滤波器提供飞机的完整状态估计。网络拓扑结构，训练和测试结果，以及过滤器的开发和结果。所提出的方法进行了测试，在仿真和硬件飞行演示。
Human remote-control (RC) pilots have the ability to perceive the position and orientation of an aircraft using only third-person-perspective visual sensing. While novice pilots often struggle when learning to control RC aircraft, they can sense the orientation of the aircraft with relative ease. In this paper, we hypothesize and demonstrate that deep learning methods can be used to mimic the human ability to perceive the orientation of an aircraft from monocular imagery. This work uses a neural network to directly sense the aircraft attitude. The network is combined with more conventional image processing methods for visual tracking of the aircraft. The aircraft track and attitude measurements from the convolutional neural network (CNN) are combined in a particle filter that provides a complete state estimate of the aircraft. The network topology, training, and testing results are presented as well as filter development and results. The proposed method was tested in simulation and hardware flight demonstrations.