  The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,
aggressive indoor flight dataset collected using a custom-built quadrotor
platform for use in evaluation of agile perception.Inspired by the potential of
future high-speed fully-autonomous drone racing, the Blackbird dataset contains
over 10 hours of flight data from 168 flights over 17 flight trajectories and 5
environments at velocities up to $7.0ms^-1$. Each flight includes sensor data
from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz
IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion
capture ground truth. Camera images for each flight were photorealistically
rendered using FlightGoggles across a variety of environments to facilitate
easy experimentation of high performance perception algorithms. The dataset is
available for download at http://blackbird-dataset.mit.edu/
