A simple vision-based navigation and control strategy for autonomous drone racing
This work addresses autonomous navigation for drone racing enthusiasts, but it is incremental as it builds on existing visual-based methods with specific hardware.
The paper tackles autonomous drone racing by developing a control system using a low-cost drone and visual feedback from ArUco tags, achieving real-time performance of about 40 fps on a laptop and 25 fps on an embedded GPU platform.
In this paper, we present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags. A simple and low-cost DJI Tello EDU quad-rotor platform was used. Based on the API provided by the manufacturer, we have created a Python application that enables the communication with the drone over WiFi, realises drone positioning based on visual feedback, and generates control. Two control strategies were proposed, compared, and critically analysed. In addition, the accuracy of the positioning method used was measured. The application was evaluated on a laptop computer (about 40 fps) and a Nvidia Jetson TX2 embedded GPU platform (about 25 fps). We provide the developed code on GitHub.