Boreas: A multi-season autonomous driving dataset

2023128 citationsJournal Articlehybrid Open Access

Authors

Keenan Burnett · University of Toronto

David J. Yoon · University of Toronto

Yuchen Wu · University of Toronto

Andrew Z. Li · University of Toronto

Haowei Zhang · University of Toronto

Shichen Lu · University of Toronto

Jingxing Qian ·

Abstract

The Boreas dataset was collected by driving a repeated route over the course of 1 year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset includes over 350 km 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 boreas.utias.utoronto.ca.

Topics & Keywords

Remote Sensing and LiDAR Applications Robotics and Sensor-Based Localization Autonomous Vehicle Technology and Safety

UN Sustainable Development Goals

Climate action

Publication Details

Published in: The International Journal of Robotics Research

Volume 42, Issue 1-2, pp. 33-42

DOI: 10.1177/02783649231160195

Field-Weighted Citation Impact: 14.96