  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.
