Papers › The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight
Amado Antonini, Winter Guerra, Varun Murali, Thomas Sayre-McCord, Sertac Karaman
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, ∼190Hz 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/.
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