The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
This dataset addresses the need for high-speed perception evaluation in UAVs, particularly for applications like autonomous drone racing, but it is incremental as it builds on existing data collection methods.
The authors introduced the Blackbird dataset, a large-scale collection of aggressive indoor flight data from a custom quadrotor, containing over 10 hours of data across 168 flights with velocities up to 7.0 m/s, to evaluate agile perception algorithms for UAVs.
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/