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

TL;DR

This paper introduces a simple, low-cost vision-based control system enabling a drone to autonomously navigate through gates using ArUco markers, with evaluations on different hardware platforms and publicly available code.

Contribution

The paper presents a novel, low-cost drone control system utilizing visual feedback for autonomous navigation through gates, including two control strategies and performance analysis.

Findings

Control system successfully navigates through gates with visual feedback.

System achieves real-time performance on laptop and embedded GPU.

Code is publicly available for replication and further development.

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.