Boreas: A Multi-Season Autonomous Driving Dataset

Autor:	Keenan Burnett, David J Yoon, Yuchen Wu, Andrew Z Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith YK Leung, Angela P Schoellig, Timothy D Barfoot
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	FOS: Computer and information sciences Computer Science - Robotics Artificial Intelligence Applied Mathematics Mechanical Engineering Modeling and Simulation Electrical and Electronic Engineering Robotics (cs.RO) Software
Popis:	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 Accepted in IJRR as a data paper
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09380dd13e27e7c9fc7dbdcaa79b90ce http://arxiv.org/abs/2203.10168 Zobrazit plný text záznamu