boreas.yaml

Name: Boreas Autonomous Driving Dataset
Description: This autonomous driving dataset includes data from a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360-degree Navtech radar, and post-processed Applanix POS LV GNSS data. This dataset was collect in various weather conditions (sun, rain, snow) over the course of a year. The intended purpose of this dataset is to enable benchmarking of long-term all-weather odometry and metric localization across various sensor types. In the future, we hope to also support an object detection benchmark.
Documentation: https://github.com/utiasASRL/pyboreas/blob/master/DATA_REFERENCE.md
Contact: boreas@robotics.utias.utoronto.ca
ManagedBy: "[ASRL](http://asrl.utias.utoronto.ca)"
UpdateFrequency: New driving sequences will be added as they are collected.
Tags:
  - autonomous vehicles
  - robotics
  - computer vision
  - lidar
License: "[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)"
Resources:
  - Description: Boreas dataset
    ARN: arn:aws:s3:::boreas
    Region: us-west-2
    Type: S3 Bucket
DataAtWork:
  Tutorials:
    - Title: Introduction to Visualizing Sensor Types (Jupyter notebook)
      URL: https://github.com/utiasASRL/pyboreas/blob/master/pyboreas/tutorials/intro.ipynb
      AuthorName: Keenan Burnett
      Services:
        - SageMaker
    - Title: Project Lidar onto Camera Frames (Jupyter notebook)
      URL: https://github.com/utiasASRL/pyboreas/blob/master/pyboreas/tutorials/lidar_camera_projection.ipynb
      AuthorName: Keenan Burnett 
      Services:
        - SageMaker
  Publications:
    - Title: Do we need to compensate for motion distortion and doppler effects in spinning radar navigation?
      URL: https://www.dynsyslab.org/wp-content/papercite-data/pdf/burnett-ral21.pdf
      AuthorName: K. Burnett, A. P. Schoellig, T. D. Barfoot
    - Title: Radar odometry combining probabilistic estimation and unsupervised feature learning
      URL: https://arxiv.org/pdf/2105.14152.pdf
      AuthorName: K. Burnett, D. J. Yoon, A. P. Schoellig, T. D. Barfoot