Fast-Racing: An Open-Source Strong Baseline for $\mathrm{SE}(3)$ Planning in Autonomous Drone Racing

With the autonomy of aerial robots advances in recent years, autonomous drone racing has drawn increasing attention. In a professional pilot competition, a skilled operator always controls the drone to agilely avoid obstacles in aggressive attitudes, for reaching the destination as fast as possible. Autonomous flight like elite pilots requires planning in $\mathrm{SE}(3)$ , whose non-triviality and complexity hindering a convincing solution in our community by now. To bridge this gap, this letter proposes an open-source baseline, which includes a high-performance $\mathrm{SE}(3)$ planner and a challenging simulation platform tailored for drone racing. We specify the $\mathrm{SE}(3)$ trajectory generation as a soft-penalty optimization problem, and speed up the solving process utilizing its underlying parallel structure. Moreover, to provide a testbed for challenging the planner, we develop delicate drone racing tracks which mimic real-world set-up and necessities planning in $\mathrm{SE}(3)$ . Besides, we provide necessary system components such as common map interfaces and a baseline controller, to make our work plug-in-and-use. With our baseline, we hope to future foster the research of $\mathrm{SE}(3)$ planning and the competition of autonomous drone racing.