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.

翻译：随着近年来航空机器人的自主性,自主无人机竞赛引起了越来越多的注意。在专业试点竞争中,熟练的操作员总是控制无人驾驶飞机,灵活避免攻击性态度的障碍,尽可能快地到达目的地。像精英飞行员一样的自主飞行需要在SE(3)中进行规划,因为SE(3)中的非三角性和复杂性阻碍我们社区令人信服的解决办法。为了缩小这一差距,本文件提议了开放源基线,其中包括高性能的SE(3)规划员和专为无人驾驶飞机竞赛设计的具有挑战性的模拟平台。我们把SE(3)轨迹的生成指定为软性弹道优化问题,并加快使用其基本平行结构的解决进程。此外,为了提供一个测试台来挑战规划者,我们开发了微妙的无人驾驶飞机赛道,它模拟了现实世界的设置和需求规划。此外,我们提供了必要的系统组件,如共同的地图界面和基线控制员,使我们的工作能够插上。我们希望以我们的基线为基础,未来能够促进SE(3)计划研究和自动无人驾驶飞机赛的竞争。