graviti
Products
Resources
About us
The UZH-FPV Drone Racing
License: Unknown

Overview

We introduce the UZH-FPV Drone Racing dataset, which is the most aggressive visual-inertial
odometry dataset to date. Large accelerations, rotations, and apparent motion in vision sensors
make aggressive trajectories difficult for state estimation. However, many compelling applications,
such as autonomous drone racing, require high speed state estimation, but existing datasets
do not address this. These sequences were recorded with a first-person-view (FPV) drone racing
quadrotor fitted with sensors and flown aggressively by an expert pilot. The trajectories include
fast laps around a racetrack with drone racing gates, as well as free-form trajectories around
obstacles, both indoor and out. We present the camera images and IMU data from a Qualcomm Snapdragon
Flight board, ground truth from a Leica Nova MS60 laser tracker, as well as event data from
an mDAVIS 346 event camera, and high-resolution RGB images from the pilot’s FPV camera. With
this dataset, our goal is to help advance the state of the art in high speed state estimation.

Citation

@InProceedings{Delmerico19icra,
 author = {Jeffrey Delmerico and Titus Cieslewski and Henri Rebecq and Matthias Faessler and
Davide Scaramuzza},
 title = {Are We Ready for Autonomous Drone Racing? The {UZH-FPV} Drone Racing Dataset},
 booktitle = {{IEEE} Int. Conf. Robot. Autom. ({ICRA})},
 year = 2019
}
Data Summary
Type
Image,
Amount
--
Size
--
| Amount -- | Size --
The UZH-FPV Drone Racing
License: Unknown

Overview

We introduce the UZH-FPV Drone Racing dataset, which is the most aggressive visual-inertial
odometry dataset to date. Large accelerations, rotations, and apparent motion in vision sensors
make aggressive trajectories difficult for state estimation. However, many compelling applications,
such as autonomous drone racing, require high speed state estimation, but existing datasets
do not address this. These sequences were recorded with a first-person-view (FPV) drone racing
quadrotor fitted with sensors and flown aggressively by an expert pilot. The trajectories include
fast laps around a racetrack with drone racing gates, as well as free-form trajectories around
obstacles, both indoor and out. We present the camera images and IMU data from a Qualcomm Snapdragon
Flight board, ground truth from a Leica Nova MS60 laser tracker, as well as event data from
an mDAVIS 346 event camera, and high-resolution RGB images from the pilot’s FPV camera. With
this dataset, our goal is to help advance the state of the art in high speed state estimation.

Citation

@InProceedings{Delmerico19icra,
 author = {Jeffrey Delmerico and Titus Cieslewski and Henri Rebecq and Matthias Faessler and
Davide Scaramuzza},
 title = {Are We Ready for Autonomous Drone Racing? The {UZH-FPV} Drone Racing Dataset},
 booktitle = {{IEEE} Int. Conf. Robot. Autom. ({ICRA})},
 year = 2019
}
0
Start building your AI now
graviti
wechat-QR
Long pressing the QR code to follow wechat official account

Copyright@Graviti