  Drone racing is a recreational sport in which the goal is to pass through a
sequence of gates in a minimum amount of time while avoiding collisions. In
autonomous drone racing, one must accomplish this task by flying fully
autonomously in an unknown environment by relying only on computer vision
methods for detecting the target gates. Due to the challenges such as
background objects and varying lighting conditions, traditional object
detection algorithms based on colour or geometry tend to fail. Convolutional
neural networks offer impressive advances in computer vision but require an
immense amount of data to learn. Collecting this data is a tedious process
because the drone has to be flown manually, and the data collected can suffer
from sensor failures. In this work, a semi-synthetic dataset generation method
is proposed, using a combination of real background images and randomised 3D
renders of the gates, to provide a limitless amount of training samples that do
not suffer from those drawbacks. Using the detection results, a line-of-sight
guidance algorithm is used to cross the gates. In several experimental
real-time tests, the proposed framework successfully demonstrates fast and
reliable detection and navigation.
