  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.
