Boreas: A Multi-Season Autonomous Driving Dataset
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
TL;DR
Boreas is a comprehensive multi-season autonomous driving dataset collected over a year, featuring diverse weather conditions and sensor data, designed to advance research in localization, odometry, and object detection.
Contribution
This paper introduces Boreas, a novel multi-season autonomous driving dataset with extensive sensor data and challenging environmental variations, supporting multiple research tasks.
Findings
Includes over 350km of driving data with diverse weather conditions
Provides high-resolution sensor data including lidar, radar, and cameras
Supports live leaderboards for key autonomous driving tasks
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 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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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Neural Network Applications · Remote Sensing and LiDAR Applications