A simple vision-based navigation and control strategy for autonomous
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.