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The Blackbird Dataset: A Large-Scale Dataset for UAV Perception in Aggressive Flight

Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 11)

Abstract

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 h of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to 7.0 m \(\mathrm{s}^{-1}\). Each flight includes sensor data from 120 Hz stereo and downward-facing photorealistic virtual cameras, 100 Hz IMU, \(\sim \)190 Hz motor speed sensors, and 360 Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles [1] 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/.

Supplementary material

Supplementary material 1 (mp4 39740 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Laboratory for Information and Decision SystemsMassachusetts Institute of TechnologyCambridgeUSA

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