@inproceedings{antonini2018blackbird,
title= {The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight},
author= {Antonini, Amado and Guerra, Winter and Murali, Varun and Sayre-McCord, Thomas and Karaman, Sertac},
booktitle= {2018 International Symposium on Experimental Robotics (ISER)},
year= {2018},
abstract= {The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale indoor dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception. The dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to 8.0 m/s. Each flight includes sensor data from 120 Hz stereo and downward-facing photorealistic virtual cameras, 100 Hz IMU, 190 Hz motor speed sensors, and 360 Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles across a variety of environments to facilitate experimentation of perception algorithms. The dataset is available at http://blackbird-dataset.mit.edu.},
keywords= {UAV, aggressive, dataset, drone racing, VIO, SLAM, perception},
terms= {Copyright 2018 Sertac Karaman
    
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    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.},
license= {MIT License},
superseded= {},
url= {http://blackbird-dataset.mit.edu}
}