Autonomous drone race: A computationally efficient vision-based navigation and control strategy

• Published in: Robotics and autonomous systems 2020-11, Vol.133, p.103621, Article 103621
• Main Authors: Li, Shuo, Ozo, Michaël M.O.I., De Wagter, Christophe, de Croon, Guido C.H.E.
• Format: Article
• Language: English
• Subjects: Autonomous drone race; Micro aerial vehicle; Visual navigation
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2020.103621

Drone racing is becoming a popular sport where human pilots have to control their drones to fly at high speed through complex environments and pass a number of gates in a pre-defined sequence. In this paper, we develop an autonomous system for drones to race fully autonomously using only onboard resources. Instead of commonly used visual navigation methods, such as simultaneous localization and mapping and visual inertial odometry, which are computationally expensive for micro aerial vehicles (MAVs), we developed the highly efficient snake gate detection algorithm for visual navigation, which can detect the gate at 20 HZ on a Parrot Bebop drone. Then, with the gate detection result, we developed a robust pose estimation algorithm which has better tolerance to detection noise than a state-of-the-art perspective-n-point method. During the race, sometimes the gates are not in the drone’s field of view. For this case, a state prediction-based feed-forward control strategy is developed to steer the drone to fly to the next gate. Experiments show that the drone can fly a half-circle with 1.5 m radius within 2 s with only 30cm error at the end of the circle without any position feedback. Finally, the whole system is tested in a complex environment (a showroom in the faculty of Aerospace Engineering, TU Delft). The result shows that the drone can complete the track of 15 gates with a speed of 1.5m∕s which is faster than the speeds exhibited at the 2016 and 2017 IROS autonomous drone races. •A system including navigation and control for autonomous drone races.•A computational efficient gate detection method which helps the drone to navigation itself.•An Extended Kalman filter to fuse the low frequency detections and onboard IMU measurements to get accurate state estimation.•A feed-forward control approach to steer the drone to the next gate without a detection.