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The Boreas Datase
The Boreas dataset encompasses a year-long collection of driving data along a repeated route, exhibiting noticeable seasonal changes. The dataset comprises more than 350km of driving data, encompassing various instances of challenging weather conditions like rain and heavy snow. Notably, the Boreas data-taking platform showcases an exceptional sensor suite, including a 128-channel Velodyne Alpha Prime lidar, a 360-degree Navtech radar, and precise ground truth poses acquired from an Applanix POSLV GPS/IMU. Presently, the platform provides active and inclusive benchmarks for odometry, metric localization, and 3D object detection.OSDAR23
Within the Rail industry, this is the first freely available multi-sensor data set for machine learning for the development of fully automated driving
Homepage OSDaR23-multi-sensor data set for machine learning (digitale-schiene-deutschland.de)
The multi-sensor data and the associated annotations contained in the OSDaR23 dataset can be downloaded from the following link:[ref: https://doi.org/10.57806/9mv146r0]
For easy use of the dataset, DB Netz AG has also published a suitable Python software development environment:
[ref: https://github.com/DSD-DBS/raillabel%5D
To visualize the dataset, the WebLabel Player of the Vicomtech Research Foundation can be used:
[ref: https://github.com/Vicomtech/weblabel%5D ]
The Radar Doppler Dataset provides 25 km of data from a prototype Navtech CTS350-X Millimetre-Wave FMCW radar containing raw Doppler information, alongside LiDAR, camera and RTK GPS. The new Doppler configuration provides raw return discrepancies, which can be used to detect moving objects and estimate their velocity. We are excited to share this data with the community and we intend that this dataset will help accelerate research in this interesting modality,