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Boreas: A Multi-Season Autonomous Driving Dataset
March 18, 2022 Β· Declared Dead Β· π Int. J. Robotics Res.
"No code URL or promise found in abstract"
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Authors
Keenan Burnett, David J. Yoon, Yuchen Wu, Andrew Zou Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith Y. K. Leung, Angela P. Schoellig, Timothy D. Barfoot
arXiv ID
2203.10168
Category
cs.RO: Robotics
Citations
164
Venue
Int. J. Robotics Res.
Last Checked
4 months ago
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
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