ros2-calib 0.1.4

A manual LiDAR-Camera calibration tool for ROS 2

ros2_calib

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                          Multi-Sensor Calibration Tool for ROS 2
                          🎯 Precise • 🚀 Fast • 🔧 Interactive
                             >>>  pip install ros2-calib  <<<
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ros2_calib is a multi-sensor calibration tool for ROS 2 that provides intuitive graphical interfaces for performing precise extrinsic calibration between different sensor types. The tool supports both LiDAR-to-Camera and LiDAR-to-LiDAR calibration workflows. Built with PySide6, it operates on recorded rosbag data without requiring a live ROS 2 environment. It supports reading /tf_static transforms from rosbags and allows users to quickly calibrate and export the resulting transformation directly into URDF format. Although it is a manual calibration tool, we made the expierence that it is faster to use than a target-based calibration method and is more accurate than automatic methods.

Screenshots

Start Window with Calibration Type Selection

Rosbag Loading and Topic Selection

TF Tree Visualization and Initial Transform Selection

LiDAR-to-Camera Calibration Interface

LiDAR-to-LiDAR Calibration Interface (Open3D)

Target Link Selection and URDF Export

Features

LiDAR-to-Camera Calibration

• 🎯 Interactive Calibration: Point-and-click interface for 2D-3D correspondences
• 🔄 Real-time Visualization: Live point cloud projection with adjustable parameters
• 🧠 Smart Algorithms: RANSAC-based PnP solver with Scipy least-squares refinement
• 🧹 Point Cloud Cleaning: Advanced occlusion removal using the RePLAy algorithm
• ⌨️ Keyboard Shortcuts: ESC to cancel, Backspace to delete, Enter to confirm

LiDAR-to-LiDAR Calibration

• 🎮 3D Interactive Interface: Open3D-based point cloud visualization
• 🔧 Manual Adjustment: Precise transformation controls with step-size configuration
• 🤖 Automatic Registration: Point-to-point and point-to-plane ICP algorithms
• 👁️ Dual Visualization: Independent control over source and target point cloud visibility
• 📊 Calibration View: Live transformation matrix updates and registration metrics

General Features

• 🌳 TF Tree Integration: Visual transform chain management and URDF export
• 💾 Offline Processing: Works with .mcap rosbag files - no live ROS 2 required
• 🎨 Easy-to-Use UI: Organized sections with responsive design
• 📱 Multi-Calibration Types: Seamless switching between different calibration workflows

Installation

Prerequisites

• Tested with Python 3.12.3 and Ubuntu 24.04
• Compatible rosbag files in .mcap format

Rosbag Requirements

Your rosbag file (.mcap format, tested with Jazzy and Humble) should contain the following topics:

For LiDAR-to-Camera Calibration:

• Camera topics: /camera/image_raw or /camera/image_rect
  • sensor_msgs/Image
  • sensor_msgs/CompressedImage
• Camera info: /camera/camera_info (sensor_msgs/CameraInfo)
• LiDAR topics: /lidar/points or similar (sensor_msgs/PointCloud2)

For LiDAR-to-LiDAR Calibration:

• Source LiDAR topic: /lidar1/points (sensor_msgs/PointCloud2)
• Target LiDAR topic: /lidar2/points (sensor_msgs/PointCloud2)

Optional but Recommended:

• Transform topics: /tf_static (tf2_msgs/TFMessage)
  • Contains static transformations between sensor frames
  • If not available, you'll need to manually specify initial transforms

Furthermore, the metadata file (metadata.yaml) must be present in the same directory as the .mcap file (usually automatically created by ROS 2 when recording). Topics can be named differently; the tool allows you to select the correct topics during setup.

Install from PyPI

pip install ros2-calib

Install from Source

# Clone the repository
git clone https://github.com/ika-rwth-aachen/ros2_calib.git
cd ros2_calib

# Create a virtual environment
python -m venv .venv
source ./venv/bin/activate

# Install in development mode
python -m pip install .

Quick Start

1. Launch the application:

   ros2_calib
   

2. Select calibration type: Choose between LiDAR-to-Camera or LiDAR-to-LiDAR calibration

3. Load your rosbag: Click "Load Rosbag" and select your .mcap file

4. Select topics: Choose your sensor topics based on calibration type

5. Set initial transform: Configure the transformation between sensor frames

6. Perform calibration:

   • LiDAR-to-Camera: Select frame, create 2D-3D correspondences, run calibration
   • LiDAR-to-LiDAR: Use interactive 3D interface with manual adjustment and/or ICP

7. Export results: View transformation chains and export URDF-ready transforms

Workflow Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ Select Calib    │ -> │   Load Rosbag   │ -> │  Select Topics  │ -> │ Set Initial TF  │
│ Type (L2C/L2L)  │    │   (.mcap file)  │    │   (sensors)     │    │  (manual/auto)  │
└─────────────────┘    └─────────────────┘    └─────────────────┘    └─────────────────┘
                                                                             │
                       ┌─────────────────┐    ┌─────────────────┐            │
                       │ Frame Selection │ -> │   LiDAR2Camera  │ <----------┘
                       │  (L2C only)     │    │  Calibration    │
                       └─────────────────┘    └─────────────────┘
                                                       │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐            │
│   Export URDF   │ <- │ Transform Chain │ <- │ View Results &  │ <----------┘
│   Transform     │    │  Visualization  │    │  TF Integration │            │
└─────────────────┘    └─────────────────┘    └─────────────────┘            │
                                                       ▲                     │
                       ┌─────────────────┐    ┌─────────────────┐            │
                       │   LiDAR2LiDAR   │ -> │                 │ <----------┘
                       │ Calibration O3D │    │                 │
                       └─────────────────┘    └─────────────────┘

Core Architecture

• main.py: Application entry point with PySide6 QApplication setup
• main_window.py: Multi-view interface with stacked widget navigation and calibration type selection
• calibration_widget.py: Interactive LiDAR-to-Camera calibration view with 2D/3D visualization
• lidar2lidar_o3d_widget.py: Open3D-based LiDAR-to-LiDAR calibration interface
• frame_selection_widget.py: Frame selection interface for synchronized sensor data
• calibration.py: Core mathematical algorithms using OpenCV and Scipy
• bag_handler.py: Rosbag processing and message extraction utilities
• ros_utils.py: Mock ROS 2 message types for offline operation
• tf_graph_widget.py: Interactive TF tree visualization using NodeGraphQt
• lidar_cleaner.py: Point cloud cleaning based on RePLAy Algorithm (ECCV 2024)

Algorithm Details

LiDAR-to-Camera Calibration

Two-Stage Calibration Process:

1. Initial Estimation: OpenCV's solvePnPRansac for robust pose estimation
2. Refinement: Scipy's least_squares optimization minimizing reprojection error
3. Quality Assessment: Automatic outlier detection and correspondence validation

Point Cloud Processing:

• Occlusion Removal: RePLAy algorithm removes projective artifacts
• Intensity-based Coloring: Configurable colormap visualization
• Real-time Projection: Live updates during manual adjustments

LiDAR-to-LiDAR Calibration

Interactive 3D Calibration:

• Manual Adjustment: Precise translation and rotation controls with configurable step sizes
• Automatic Registration: Point-to-point and point-to-plane ICP algorithms
• Real-time Visualization: Open3D-based 3D rendering with dual point cloud display
• Quality Metrics: Live fitness and RMSE feedback during ICP registration

Configuration

The tool automatically handles:

• Message Format Detection: Supports Image and CompressedImage types
• Coordinate Frame Resolution: TF tree parsing and path finding
• Camera Model Integration: Full camera info and distortion support

Development

Code Quality

# Run linter
ruff check

# Format code
ruff format

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Troubleshooting

Common Issues

• "No topics found": Ensure your .mcap file contains the required sensor topics
• "TF tree empty": Check that your rosbag includes transform messages
• Calibration fails: Verify you have at least 4 correspondence points

Getting Help

• Open an issue for bug reports

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this tool in your research, please cite:

@software{ros2_calib,
  title={ros2\_calib: A Multi-Sensor Calibration Tool for ROS2},
  author={Till Beemelmanns},
  year={2025},
  url={https://github.com/ika-rwth-aachen/ros2_calib},
  publisher={Zenodo},
  doi={10.5281/zenodo.17119720},
  url={https://doi.org/10.5281/zenodo.17119720},
  swhid={swh:1:dir:0ac40351ccecf475772e49b7578fde53d224f6c4
         ;origin=https://doi.org/10.5281/zenodo.17119720;vi
         sit=swh:1:snp:98106a33aaa26f183d05dce909dc2da8c53b
         dab7;anchor=swh:1:rel:519b58ff48f20a36f6c36d6b3d06
         4bf1e7418432;path=ika-rwth-aachen-
         ros2\_calib-6fb095d
         },
}

Acknowledgments

Point Cloud Cleaning Algorithm

We integrate the RePLAy algorithm for removing projective LiDAR artifacts:

@inproceedings{zhu2024replay,
  title={RePLAy: Remove Projective LiDAR Depthmap Artifacts via Exploiting Epipolar Geometry},
  author={Zhu, Shengjie and Ganesan, Girish Chandar and Kumar, Abhinav and Liu, Xiaoming},
  booktitle={ECCV},
  year={2024},
}

Dependencies

• PySide6 - Cross-platform GUI toolkit
• OpenCV - Computer vision algorithms
• NumPy - Numerical computing
• SciPy - Scientific computing
• Open3D - 3D visualization and point cloud processing
• NodeGraphQt - Node graph visualization
• rosbags - Pure Python rosbag processing

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Notice

[!IMPORTANT]
This repository is open-sourced and maintained by the Institute for Automotive Engineering (ika) at RWTH Aachen University.
We cover a wide variety of research topics within our Vehicle Intelligence & Automated Driving domain.
If you would like to learn more about how we can support your automated driving or robotics efforts, feel free to reach out to us!
:email: [e-mail]