GitHub repository
A Python devkit for working with the Boreas and Boreas Road Trip all-weather autonomous driving datasets.
What is GitHub repository?
pyboreas is a Python development kit for the Boreas and Boreas Road Trip autonomous driving datasets. It provides tools to load, access, and process multi-sensor data including lidar, camera, radar, and GNSS/IMU data collected in all-weather and challenging road conditions. The kit supports benchmarking tasks like odometry, localization, and 3D object detection for autonomous vehicle research.
Researchers and developers working on autonomous driving, robotics, computer vision, and sensor fusion who need access to high-quality, multi-sensor datasets for algorithm development and evaluation.
It offers a standardized and efficient interface to two comprehensive autonomous driving datasets with accurate timestamps and poses, supporting both sequential and random data access patterns. The inclusion of all-weather and challenging road data makes it valuable for robust perception and localization research.
Overview
Devkit for the Boreas autonomous driving dataset.
Use Cases
Best For
- Developing and benchmarking visual-lidar-radar odometry algorithms
- Training and evaluating 3D object detection models in diverse weather conditions
- Researching sensor fusion techniques for autonomous driving
- Testing localization systems on challenging road routes
- Studying the impact of weather on autonomous vehicle perception
- Comparing performance of different sensor modalities (lidar, camera, radar)
Not Ideal For
- Projects requiring synchronous multi-sensor data out-of-the-box (like KITTI), as sensors are asynchronous and need manual timestamp-based alignment.
- Small teams or hobbyists with limited storage and bandwidth, due to large dataset sizes (requiring AWS CLI downloads and significant disk space).
- Real-time applications or live data streaming, since the kit is designed for offline research with pre-recorded sequences.
- Plug-and-play datasets with built-in preprocessing, as users must handle memory management (e.g., calling unload_data()) and data synchronization manually.
Pros & Cons
Pros
Includes high-end sensors like 128-beam Velodyne lidar, 5MP camera, and Navtech radar, providing comprehensive data for multi-modal perception and fusion research.
Covers all-weather conditions in Boreas and challenging roads in Boreas-RT, enabling robustness testing for autonomous systems in varied environments.
Supports live leaderboards for odometry, localization, and 3D object detection, facilitating direct comparison with state-of-the-art methods.
Each sensor frame has precise timestamps and ground truth poses in ENU coordinates, essential for handling asynchronous measurements and accurate localization.
Cons
Downloading requires AWS CLI setup and large storage space, with no lightweight or alternative download options, making initial access barrier-heavy.
Users must explicitly call unload_data() on frames to avoid memory bloat, adding complexity and potential for errors in data processing pipelines.
Test sequences lack ground truth poses, limiting evaluation for tasks like odometry without additional workarounds or assumptions.
Sensors are not synchronous (unlike datasets like KITTI), requiring extra effort for data alignment, which can be non-trivial for beginners.
Frequently Asked Questions
Related Projects
GitHub repositoryAutonomous Vehicle Seasonal Dataset
Brno Urban DatasetNavigation and localisation dataset for self driving cars and autonomous robots
Ford DatasetThe dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. The data is Robot Operating System (ROS) compatible
Oxford RobotCarThe Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year
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