Title: Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness,
Abstract: We present a micro aerial vehicle (MAV) system, built with inexpensive
off-the-shelf hardware, for autonomously following trails in unstructured,
outdoor environments such as forests. The system introduces a deep neural
network (DNN) called TrailNet for estimating the view orientation and lateral
offset of the MAV with respect to the trail center. The DNN-based controller
achieves stable flight without oscillations by avoiding overconfident behavior
through a loss function that includes both label smoothing and entropy reward.
In addition to the TrailNet DNN, the system also utilizes vision modules for
environmental awareness, including another DNN for object detection and a
visual odometry component for estimating depth for the purpose of low-level
obstacle detection. All vision systems run in real time on board the MAV via a
Jetson TX1. We provide details on the hardware and software used, as well as
implementation details. We present experiments showing the ability of our
system to navigate forest trails more robustly than previous techniques,
including autonomous flights of 1 km.