  In this paper, we propose a new pipeline of training a monocular UAV to fly a
collision-free trajectory along the dense forest trail. As gathering
high-precision images in the real world is expensive and the off-the-shelf
dataset has some deficiencies, we collect a new dense forest trail dataset in a
variety of simulated environment in Unreal Engine. Then we formulate visual
perception of forests as a classification problem. A ResNet-18 model is trained
to decide the moving direction frame by frame. To transfer the learned strategy
to the real world, we construct a ResNet-18 adaptation model via multi-kernel
maximum mean discrepancies to leverage the relevant labelled data and alleviate
the discrepancy between simulated and real environment. Simulation and
real-world flight with a variety of appearance and environment changes are both
tested. The ResNet-18 adaptation and its variant model achieve the best result
of 84.08% accuracy in reality.
