  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 SE(3), whose
non-triviality and complexity hindering a convincing solution in our community
by now. To bridge this gap, this paper proposes an open-source baseline, which
includes a high-performance SE(3) planner and a challenging simulation platform
tailored for drone racing. We specify the 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 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 SE(3) planning and the competition of autonomous
drone racing.

Robotics