A Deep-learning-aided Automatic Vision-based Control Approach for Autonomous Drone Racing in Game of Drones Competition

Abstract

In Game of Drones - Competition at NeurIPS 2019, this autonomous drone racing requires the drone to maneuver through the series of the gates without crashing. To complete the track, the drone has to be able to perceive the gates in the challenging environment from the FPV image in real-time and adjust its attitude accordingly. By utilizing deep-learning-aided detection and vision-based control approach, Team USRG completed the tier 2 challenge track passing the whole 21 gates in 81.19 seconds, and complete the tier 3 challenge track passing the whole 22 gates in 110.73 seconds.