  Over the last decade, the use of autonomous drone systems for surveying,
search and rescue, or last-mile delivery has increased exponentially. With the
rise of these applications comes the need for highly robust, safety-critical
algorithms which can operate drones in complex and uncertain environments.
Additionally, flying fast enables drones to cover more ground which in turn
increases productivity and further strengthens their use case. One proxy for
developing algorithms used in high-speed navigation is the task of autonomous
drone racing, where researchers program drones to fly through a sequence of
gates and avoid obstacles as quickly as possible using onboard sensors and
limited computational power. Speeds and accelerations exceed over 80 kph and 4
g respectively, raising significant challenges across perception, planning,
control, and state estimation. To achieve maximum performance, systems require
real-time algorithms that are robust to motion blur, high dynamic range, model
uncertainties, aerodynamic disturbances, and often unpredictable opponents.
This survey covers the progression of autonomous drone racing across
model-based and learning-based approaches. We provide an overview of the field,
its evolution over the years, and conclude with the biggest challenges and open
questions to be faced in the future.
