A toolkit for working with the Boreas dataset in Python
Project description
pyboreas
This devkit provides tools for working with the Boreas Dataset, an all-weather autonomous driving dataset which includes a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360 degree Navtech radar, and post-processed Applanix POS LV GNSS data. Our dataset currently suports benchmarking odometry. We are working towards providing an online benchmark for odometry, localization, and more. We plan to provide an HD map of each driven route. We are also in the process of acquiring 3D labels and hope to be able to provide a challenging object detection benchmark in the future.
Please note that our website is currently under construction. A live benchmark and a browser for downloading sequences will be available via the website soon.
Installation
Using pip
pip install asrl-pyboreas
From source
git clone https://github.com/utiasASRL/pyboreas.git
pip install -e pyboreas
Download Instructions
- Create an AWS account (optional)
- Install the AWS CLI
- Create a
root
folder to store the dataset, example:/path/to/data/boreas/
Each sequence will then be a folder underroot
. - Use the AWS CLI to download either the entire dataset or only the desired sequences and sensors. Add
--no-sign-request
after each of the following commands if you're not going to use an AWS account. For example, 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, our website (Work-In-Progress) 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. Also note that we provide instructions for using this dataset using an AWS SageMaker instance, instructions at: pyboreas/tutorials/aws/README.md.
TODO:
- Tutorials (pose interp)
- Ground plane removal
- Pointcloud voxelization
- 3D Bounding boxes
Project details
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