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Boreas: A Multi-Season Autonomous Driving Dataset

Authors :
Burnett, Keenan
Yoon, David J.
Wu, Yuchen
Li, Andrew Zou
Zhang, Haowei
Lu, Shichen
Qian, Jingxing
Tseng, Wei-Kang
Lambert, Andrew
Leung, Keith Y. K.
Schoellig, Angela P.
Barfoot, Timothy D.
Publication Year :
2022

Abstract

The Boreas dataset was collected by driving a repeated route over the course of one year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset includes over 350km of driving data featuring a 128-channel Velodyne Alpha Prime lidar, a 360$^\circ$ Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. Our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at https://www.boreas.utias.utoronto.ca<br />Comment: Accepted in IJRR as a data paper

Subjects

Subjects :
Computer Science - Robotics

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2203.10168
Document Type :
Working Paper