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.

翻译：在过去十年中，自主无人机系统在测绘、搜救和末端配送等领域的使用呈指数级增长。随着这些应用的兴起，亟需开发高度鲁棒、安全至上的算法，使无人机能够在复杂和不确定的环境中运行。此外，高速飞行使无人机能够覆盖更广区域，从而提升作业效率并进一步拓展其应用场景。自主无人机竞速任务可作为高速导航算法开发的试验平台，研究者通过机载传感器和有限的计算资源，编程控制无人机以最快速度穿越一系列门框并规避障碍物。飞行速度与加速度分别超过80公里/小时和4g，这对感知、规划、控制和状态估计提出了重大挑战。为实现最佳性能，系统需配备实时算法，能够有效应对运动模糊、高动态范围、模型不确定性、空气动力扰动以及常具不可预测性的对手。本综述涵盖基于模型和基于学习两类方法在自主无人机竞速领域的发展脉络，系统梳理该领域的整体概况与演进历程，并总结未来面临的核心挑战与开放性问题。