  Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly
straight-forward, as expensive sensors and monitoring devices can be employed.
In contrast, obstacle avoidance remains a challenging task for Micro Aerial
Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike
large vehicles, MAVs can only carry very light sensors, such as cameras, making
autonomous navigation through obstacles much more challenging. In this paper,
we describe a system that navigates a small quadrotor helicopter autonomously
at low altitude through natural forest environments. Using only a single cheap
camera to perceive the environment, we are able to maintain a constant velocity
of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent
state-of-the-art imitation learning techniques to train a controller that can
avoid trees by adapting the MAVs heading. We demonstrate the performance of our
system in a more controlled environment indoors, and in real natural forest
environments outdoors.
