Boreas: A multi-season autonomous driving dataset-Reference-Cited by-同舟云学术

Boreas: A multi-season autonomous driving dataset

Published:2023-01 Issue:1-2 Volume:42 Page:33-42
ISSN:0278-3649
Container-title:The International Journal of Robotics Research
language:en
Short-container-title:The International Journal of Robotics Research

Author:

Burnett Keenan¹^ORCID,Yoon David J¹,Wu Yuchen¹,Li Andrew Z¹^ORCID,Zhang Haowei¹,Lu Shichen¹,Qian Jingxing¹,Tseng Wei-Kang¹,Lambert Andrew²,Leung Keith YK²,Schoellig Angela P¹^ORCID,Barfoot Timothy D¹^ORCID

Affiliation:

1. Aerospace, University of Toronto, Toronto, ON, Canada

2. Trimble, Applanix, Richmond Hill, ON, Canada

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.

Publisher

SAGE Publications

Subject

Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software

Link

http://journals.sagepub.com/doi/pdf/10.1177/02783649231160195

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