GitHub repository
A target-less, automatic toolbox for LiDAR-camera extrinsic calibration that works with various sensor models without requiring calibration targets.
What is GitHub repository?
Direct Visual LiDAR Calibration is a toolbox for extrinsic calibration between LiDAR and camera sensors. It automatically aligns 3D point clouds with 2D images without requiring calibration targets, using environmental features instead. The method supports various LiDAR and camera models and can work with minimal data.
Robotics engineers, autonomous vehicle developers, and researchers working on sensor fusion, 3D perception, or multi-modal sensor systems.
It eliminates the need for calibration targets and manual intervention while supporting diverse sensor configurations, making it more practical and accessible than traditional calibration methods.
Overview
A toolbox for target-less LiDAR-camera calibration [ROS1/ROS2]
Use Cases
Best For
- Calibrating LiDAR-camera systems in autonomous vehicles
- Sensor fusion for robotics applications without calibration targets
- Extrinsic calibration for multi-modal perception systems
- Research projects requiring flexible LiDAR-camera calibration
- Deploying calibration in environments where targets are impractical
- Integrating various LiDAR and camera models into unified systems
Not Ideal For
- Projects not using ROS1 or ROS2, as the toolbox is tightly integrated with the ROS ecosystem.
- Real-time applications requiring instantaneous calibration updates, due to computational overhead from optimization algorithms.
- Environments with minimal texture or structure (e.g., empty rooms), where target-based methods might be more reliable.
- Systems with strict embedded compute or memory constraints, given heavy dependencies like PCL and Ceres.
Pros & Cons
Pros
Handles various LiDAR types (spinning and non-repetitive scan) and camera models (pinhole, fisheye, omnidirectional), making it versatile for different sensor setups.
Uses environmental features instead of specialized targets, eliminating manual setup and making it practical for field deployments where targets are impractical.
Can calibrate with a single LiDAR-camera pair, reducing data collection effort, with optional multi-pair support for improved accuracy.
Operates without an initial guess, automating calibration and reducing manual intervention, as highlighted in the README.
Cons
Requires installation of ROS, PCL, OpenCV, GTSAM, Ceres, and other libraries, which can be challenging and time-consuming for users without prior experience.
Designed specifically for ROS, limiting use to ROS-based projects and lacking a standalone version for broader integration.
Pixel-level direct registration algorithms may be computationally intensive, slowing down processing with high-resolution data or in real-time scenarios.
Frequently Asked Questions
Related Projects
OpenCalibOpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving
direct_visual_lidar_calibrationGeneral, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox
PaperPaperFound a gem we're missing?
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