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Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset


Delmerico, Jeffrey; Cieslewski, Titus; Rebecq, Henri; Faessler, Matthias; Scaramuzza, Davide (2019). Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset. In: 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20 June 2019 - 24 June 2019. IEEE, 6713-6719.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Software
Physical Sciences > Control and Systems Engineering
Physical Sciences > Artificial Intelligence
Physical Sciences > Electrical and Electronic Engineering
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:24 June 2019
Deposited On:26 Jan 2021 10:47
Last Modified:06 Mar 2024 14:33
Publisher:IEEE
ISBN:978-1-5386-6027-0
OA Status:Green
Publisher DOI:https://doi.org/10.1109/icra.2019.8793887
Other Identification Number:merlin-id:20287
  • Content: Accepted Version