  Unmanned aerial vehicles, and multi-rotors in particular, can now perform
dexterous tasks in impervious environments, from infrastructure monitoring to
emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark
to develop and evaluate these capabilities. Its challenges include accurate and
robust visual-inertial odometry during aggressive maneuvers, complex
aerodynamics, and constrained computational resources. As researchers
increasingly channel their efforts into it, they also need the tools to timely
and equitably compare their results and advances. With this dataset, we want to
(i) support the development of new methods and (ii) establish quantitative
comparisons for approaches originating from the broader robotics and artificial
intelligence communities. We want to provide a one-stop resource that is
comprehensive of (i) aggressive autonomous and piloted flight, (ii)
high-resolution, high-frequency visual, inertial, and motion capture data,
(iii) commands and control inputs, (iv) multiple light settings, and (v)
corner-level labeling of drone racing gates. We also release the complete
specifications to recreate our flight platform, using commercial off-the-shelf
components and the open-source flight controller Betaflight, to democratize
drone racing research. Our dataset, open-source scripts, and drone design are
available at: https://github.com/tii-racing/drone-racing-dataset
