The first Autonomous Drone Racing (ADR) was launched in the IEEE IROS 2016, and continued to be organised in IROS 2017, 2018 and 2019. Inspired by this competition, other international competitions were launched: the AlphaPilot organised by Lockheed Martin in collaboration with the Drone Racing League, and the Game of Drones, organised by Microsoft and Stanford University. A distinctive feature in the IROS ADR and AlphaPilot competitions is that competing drones had to perform on-board processing only. Hence, along these years, teams have presented novel solutions for on-board processing based on a Graphic Processing Unit (GPU), a Field-Programmable Gate Array (FPGA), microcontrollers and embedded computers such as the Odroid or Intel Stick computers. Motivated by the variety of these solutions, the goal of this work is that of providing the reader with a detail description of the hardware used for the competitions, their benefits as much as limitations, including those microchips used in specialised sensors such as the RGB-D and stereo cameras whose data processing is carried out on the sensor itself. It is expected to conclude that GPU will stand out as the best hardware to compute complex processing on-board, in particular due to the use of deep learning not only to address the gate detection problem, but also to address the control and planning tasks involved in this challenge.

首届自主无人机竞赛 (ADR) 在 IEEE IROS 2016 上推出，并在 IROS 2017、2018 和 2019 年继续举办。受本次比赛的启发，还推出了其他国际比赛：由洛克希德马丁公司与无人机赛车联盟和无人机游戏，由微软和斯坦福大学组织。IROS ADR 和 AlphaPilot 比赛的一个显着特点是参赛无人机只能进行机载处理。因此，这些年来，团队提出了基于图形处理单元 (GPU)、现场可编程门阵列 (FPGA)、微控制器和嵌入式计算机（如 Odroid 或 Intel Stick 计算机）的板载处理的新颖解决方案。由于这些解决方案的多样性，这项工作的目标是为读者提供用于比赛的硬件的详细描述，它们的好处和局限性，包括用于专用传感器的微芯片，如 RGB-D 和立体相机，其数据处理是在传感器本身上进行。预计 GPU 将成为计算复杂机载处理的最佳硬件，特别是由于使用深度学习不仅可以解决门检测问题，还可以解决所涉及的控制和规划任务在这个挑战中。