Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ~10cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the IMAV 2022 Nanocopter AI Challenge. We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field.

翻译：自主无人机竞速比赛是提升无人飞行器感知、规划与控制能力的代理任务。近年来兴起的自主纳米级无人机竞速带来了新的挑战：由于机体尺寸约10厘米，其机载资源（包括内存、计算能力和传感器）受到严重限制。本文介绍了在首届自主纳米无人机竞速国际赛事——IMAV 2022纳米飞行器人工智能挑战赛中夺冠系统的技术方案与实现细节。为实现该目标，我们开发了一套完全基于仿真图像训练的机载深度学习视觉导航方法。该方法包含用于避障的卷积神经网络、仿真到真实数据集采集流程，以及通过仿真与实地实验筛选、表征并适配的导航策略。在比赛中，我们的系统在七支参赛队伍中排名第一。最佳成绩为：在限定的5分钟飞行中完成115米航程，从未碰撞且成功规避静态与动态障碍物。通过向学术界分享经验，我们期望为本领域的未来发展奠定坚实基础。