论文标题
通过观察来学习：从人类导航员的视频中巡逻无人机的模仿学习
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator
论文作者
论文摘要
我们提出了一种仅基于原始视频的自动无人机巡逻的模仿学习方法。与以前的方法不同，我们建议通过观察和模仿人类导航员在地面上如何做到这一点，让无人机在空中学习巡逻。观察过程可以使用框架间的几何一致性自动收集和注释数据，从而减少了手动努力和高精度。然后，基于注释的数据，对新设计的神经网络进行了训练，以预测无人机以人类为小道巡逻的适当指示和翻译。我们的方法使无人机可以在高海拔地区飞行，风险较低。它还可以在十字路口检测所有可访问的方向，并进一步集成可用的用户说明和自主巡逻控制命令。进行了广泛的实验，以证明所提出的模仿学习过程的准确性以及自主无人机导航的整体系统的可靠性。可以在https://vsislab.github.io/uavpatrol上获得代码，数据集和视频演示。
We present an imitation learning method for autonomous drone patrolling based only on raw videos. Different from previous methods, we propose to let the drone learn patrolling in the air by observing and imitating how a human navigator does it on the ground. The observation process enables the automatic collection and annotation of data using inter-frame geometric consistency, resulting in less manual effort and high accuracy. Then a newly designed neural network is trained based on the annotated data to predict appropriate directions and translations for the drone to patrol in a lane-keeping manner as humans. Our method allows the drone to fly at a high altitude with a broad view and low risk. It can also detect all accessible directions at crossroads and further carry out the integration of available user instructions and autonomous patrolling control commands. Extensive experiments are conducted to demonstrate the accuracy of the proposed imitating learning process as well as the reliability of the holistic system for autonomous drone navigation. The codes, datasets as well as video demonstrations are available at https://vsislab.github.io/uavpatrol
