A simple vision-based navigation and control strategy for autonomous drone racing

• URL: http://arxiv.org/abs/2104.09815v1
• Date: Tue, 20 Apr 2021 08:02:02 GMT
• Title: A simple vision-based navigation and control strategy for autonomous drone racing
• Authors: Artur Cyba and Hubert Szolc and Tomasz Kryjak
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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