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/
@article{arxiv.1810.01987,
title = {The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight},
author = {Amado Antonini and Winter Guerra and Varun Murali and Thomas Sayre-McCord and Sertac Karaman},
journal= {arXiv preprint arXiv:1810.01987},
year = {2018}
}