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

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(NB. This video is narrated). 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.

J. Delmerico, T. Cieslewski, H. Rebecq, M. Faessler, D. Scaramuzza
Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset
IEEE International Conference on Robotics and Automation, 2019

Project Webpage and Datasets:

Our research page on vision based navigation for micro aerial vehicles (MAVs) :

Our research page on aggressive vision-based flight with quadrotors:

Our research page on event-based vision:

Affiliations: The authors are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland


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