Drone racing is becoming a popular e-sport all over the world, and beating
the best human drone race pilots has quickly become a new major challenge for
artificial intelligence and robotics. In this paper, we propose a strategy for
autonomous drone racing which is computationally more efficient than navigation
methods like visual inertial odometry and simultaneous localization and
mapping. This fast light-weight vision-based navigation algorithm estimates the
position of the drone by fusing race gate detections with model dynamics
predictions. Theoretical analysis and simulation results show the clear
advantage compared to Kalman filtering when dealing with the relatively low
frequency visual updates and occasional large outliers that occur in fast drone
racing. Flight tests are performed on a tiny racing quadrotor named "Trashcan",
which was equipped with a Jevois smart-camera for a total of 72g. The test
track consists of 3 laps around a 4-gate racing track. The gates spaced 4
meters apart and can be displaced from their supposed position. An average
speed of 2m/s is achieved while the maximum speed is 2.6m/s. To the best of our
knowledge, this flying platform is the smallest autonomous racing drone in the
world and is 6 times lighter than the existing lightest autonomous racing drone
setup (420g), while still being one of the fastest autonomous racing drones in
the world.

本文提出了一种视觉惯性算法，该算法比如视觉惯性测距和同时定位和映射等导航方法更加高效，可以用于计算机无人机竞速，该算法通过将比赛门检测与模型动态预测融合来估计无人机的位置。实际测试中，飞行测试在一架 72g 的迷你无人机上完成，得到了最高速度为 2.6m/s，从而使其成为世界上最小的自主竞速无人机之一。