The UZH-FPV Drone Racing
许可协议: 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
}
数据概要
数据格式
Image,
数据量
--
文件大小
--
数据集反馈
| 6 | 数据量 -- | 大小 --
The UZH-FPV Drone Racing
许可协议: 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
}
数据集反馈
0
立即开始构建AI
graviti
wechat-QR
长按保存识别二维码,关注Graviti公众号