FLAGDroneRacing: An Autonomous Drone Racing System

Abstract

This paper presents a complete system for autonomous drone racing combining image recognition, depth mapping, visual-inertial odometry (VIO), and collision-free trajectory planning. The proposed system focuses on simple, robust, and computationally efficient techniques to enable onboard hardware applications. A loosely coupled visual-inertial localization system is devised, to ensure real-time and robust localization. A lightweight CPU-based detection module is designed, which consists of autonomous mapping and gate detection components. We also introduce a robust and efficient trajectory planner to generate smooth and collision-free trajectories in real-time. The proposed methods are tested extensively through benchmark comparisons and challenging indoor flights, while simulation results show the validness and effectiveness of our proposed system. We release our implementation as an open-source ROS-package.