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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{s}^{-1}$$\end{document}. Each flight includes sensor data from 120 Hz stereo and downward-facing photorealistic virtual cameras, 100 Hz IMU, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim $$\end{document}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/.
The Blackbird Dataset: A Large-Scale Dataset for UAV Perception in Aggressive Flight
Springer Proceedings in Advanced Robotics
International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018
Proceedings of the 2018 International Symposium on Experimental Robotics ; Kapitel : 12 ; 130-139
2020-01-23
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Rising Star - Mike Sheerin takes flight at Blackbird.
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