asrl-pyboreas 2.0.0

A toolkit for working with the Boreas and Boreas-RT datasets in Python

pyboreas

This devkit provides tools for working with the Boreas and Boreas Road Trip (Boreas-RT) datasets. Boreas is an all-weather autonomous driving dataset which includes a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360 degree Navtech radar, a wheel enoder, and post-processed Applanix POS LV GNSS data. Boreas-RT is an an `all-route' autonomous driving dataset, which additionall includes an Aeries II FMCW lidar and a stand-alone Silicon Sensing DMU41 IMU. Both datasets currently suport benchmarking odometry and localization, with Boreas also benchmarking 3D object detection.

A leaderboard for each dataset is now live!

If you find our datasets useful in your research, please cite our dataset papers:

Boreas: A Multi-Season Autonomous Driving Dataset

@article{burnett_ijrr23,
author = {Keenan Burnett and David J Yoon and Yuchen Wu and Andrew Z Li and Haowei Zhang and Shichen Lu and Jingxing Qian and Wei-Kang Tseng and Andrew Lambert and Keith YK Leung and Angela P Schoellig and Timothy D Barfoot},
title ={Boreas: A multi-season autonomous driving dataset},
journal = {The International Journal of Robotics Research},
volume = {42},
number = {1-2},
pages = {33-42},
year = {2023},
doi = {10.1177/02783649231160195},
}

Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads

@misc{lisus_brrt26,
author={Daniil Lisus and Katya M. Papais and Cedric Le Gentil and Elliot Preston-Krebs and Andrew Lambert and Keith Y. K. Leung and Timothy D. Barfoot},
title={Boreas Road Trip: A Multi-Sensor Autonomous Driving Dataset on Challenging Roads}, 
year={2026},
eprint={2602.16870},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2602.16870},
}

Installation

Using pip

pip install asrl-pyboreas

From source

git clone https://github.com/utiasASRL/pyboreas.git
pip install -e pyboreas

Download Instructions

1. Create an AWS account (OPTIONAL)
2. Install the AWS CLI
3. Create a root folder to store the dataset, example: /path/to/data/boreas/ Each sequence will then be a folder under root.
4. Use the AWS CLI to download either the entire dataset or only the desired sequences and sensors.

Don't have an AWS Account? Add --no-sign-request after each AWS CLI command.

The following command will download the entire Boreas dataset:

root=/path/to/data/boreas/
aws s3 sync s3://boreas $root

The following command will list all the top-level prefixes (sequences):

root=/path/to/data/boreas/
aws s3 ls s3://boreas

Alternatively, boreas.utias.utoronto.ca can be used to browse through sequences so as to pick and choose what data to download. The website will then generate a list of AWS CLI commands that can be run as a bash script. These commands will look something like:

root=/path/to/data/boreas/
cd $root
aws s3 sync s3://boreas/boreas-2020-11-26-13-58 boreas-2020-11-26-13-58 --exclude "*" \
    --include "lidar/*" --include "radar/*" \
    --include "applanix/*" --include "calib/*"

Example Usage

import numpy as np
from pyboreas import BoreasDataset

root = '/path/to/data/boreas/'
bd = BoreasDataset(root)

# Note: The Boreas dataset differs from others (KITTI) in that camera,
# lidar, and radar measurements are not synchronous. However, each
# sensor message has an accurate timestamp and pose instead.
# See our tutorials for how to work with multiple sensors.

# Loop through each frame in order (odometry)
for seq in bd.sequences:
    # Iterator examples:
    for camera_frame in seq.camera:
        img = camera_frame.img  # np.ndarray
        # do something
        camera_frame.unload_data() # Memory reqs will keep increasing without this
    for lidar_frame in seq.lidar:
        pts = lidar_frame.points  # np.ndarray (x,y,z,i,r,t)
        # do something
        lidar_frame.unload_data() # Memory reqs will keep increasing without this
    # Retrieve frames based on their index:
    N = len(seq.radar_frames)
    for i in range(N):
        radar_frame = seq.get_radar(i)
        # do something
        radar_frame.unload_data() # Memory reqs will keep increasing without this

# Iterator example:
cam_iter = bd.sequences[0].get_camera_iter()
cam0 = next(cam_iter)  # First camera frame
cam1 = next(cam_iter)  # Second camera frame

# Randomly access frames (deep learning, localization):
N = len(bd.lidar_frames)
indices = np.random.permutation(N)
for idx in indices:
    lidar_frame = bd.get_lidar(idx)
    # do something
    lidar_frame.unload_data() # Memory reqs will keep increasing without this

# Each sequence contains a calibration object:
calib = bd.sequences[0].calib
point_lidar = np.array([1, 0, 0, 1]).reshape(4, 1)
point_camera = np.matmul(calib.T_camera_lidar, point_lidar)

# Each sensor frame has a timestamp, groundtruth pose
# (4x4 homogeneous transform) wrt a global coordinate frame (ENU),
# and groundtruth velocity information. Unless it's a part of the test set,
# in that case, ground truth poses will be missing. However we still provide IMU
# data (in the applanix frame) through the imu.csv files.
lidar_frame = bd.get_lidar(0)
t = lidar_frame.timestamp  # timestamp in seconds
T_enu_lidar = lidar_frame.pose  # 4x4 homogenous transform [R t; 0 0 0 1]
vbar = lidar_frame.velocity  # 6x1 vel in ENU frame [v_se_in_e; w_se_in_e]
varpi = lidar_frame.body_rate  # 6x1 vel in sensor frame [v_se_in_s; w_se_in_s]

Tutorials

Note that we provide a few simple tutorials for getting started with the Boreas dataset. We also provide instructions at pyboreas/tutorials/aws/README.md for using this dataset with an AWS SageMaker instance.

NOTE: ground truth poses have dtype=np.float64, but PyTorch defaults to float32. Avoid using implicit type conversion as this will result in significant quantization error. Implicit conversion is only safe when the translation values are small, such as a pose with respect to a sensor frame or with respect to a starting position, but NOT with respect to ENU (very large).

TODO:

• Pointcloud voxelization