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Type | Conference or Workshop Paper |
Scope | Discipline-based scholarship |
Published in Proceedings | Yes |
Title | Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset |
Organization Unit | |
Authors |
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Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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ISBN | 978-1-5386-6027-0 |
Page Range | 6713 - 6719 |
Event Title | 2019 International Conference on Robotics and Automation (ICRA) |
Event Type | conference |
Event Location | Montreal, QC, Canada |
Event Start Date | June 20 - 2019 |
Event End Date | June 24 - 2019 |
Publisher | IEEE |
Abstract Text | Despite impressive results in visual-inertial state estimation in recent years, high speed trajectories with six degree of freedom motion remain challenging for existing estimation algorithms. Aggressive trajectories feature large accelerations and rapid rotational motions, and when they pass close to objects in the environment, this induces large apparent motions in the vision sensors, all of which increase the difficulty in estimation. Existing benchmark datasets do not address these types of trajectories, instead focusing on slow speed or constrained trajectories, targeting other tasks such as inspection or driving. We introduce the UZH-FPV Drone Racing dataset, consisting of over 27 sequences, with more than 10 km of flight distance, captured on a first-person-view (FPV) racing quadrotor flown by an expert pilot. The dataset features camera images, inertial measurements, event-camera data, and precise ground truth poses. These sequences are faster and more challenging, in terms of apparent scene motion, than any existing dataset. Our goal is to enable advancement of the state of the art in aggressive motion estimation by providing a dataset that is beyond the capabilities of existing state estimation algorithms. |
Digital Object Identifier | 10.1109/icra.2019.8793887 |
Other Identification Number | merlin-id:20287 |
PDF File | Download from ZORA |
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