Framework and Evaluation Methodology for Autonomous Drone Racing

In recent years, autonomous drone races have become increasingly popular in the aerial robotics research community due to challenges in perception, localization, navigation, and control at high speeds, pushing forward the state-of-the-art every year. However, autonomous racing drones are still far from reaching human pilot performance, and a lot of research has to be done to accomplish that. In this work, a complete architecture system and an evaluation method for autonomous drone racing research are proposed, based on the Aerostack 5.0 open-source framework. To evaluate the performance of the whole system and of each algorithm used separately, this framework is validated not only with simulated flights, but also through real flights in an indoor drone race circuit using different configurations.