easy_handeye
Automated, hardware-independent hand-eye calibration for ROS1 with GUI support for robot vision tasks.
What is easy_handeye?
easy_handeye is a ROS1 package that automates hand-eye calibration, computing the transformation between a robot's coordinate frame and a vision tracking system. It solves the problem of accurately aligning robots with cameras or other sensors for tasks like vision-guided manipulation and collision avoidance. The package provides a GUI and supports both eye-in-hand and eye-on-base calibration configurations.
Robotics engineers and researchers working with ROS1 who need to integrate vision systems with robots for precise perception and manipulation tasks.
Developers choose easy_handeye because it offers a hardware-independent, automated solution with a user-friendly GUI, multiple calibration algorithms, and seamless integration with MoveIt! and tf, reducing manual effort and potential errors.
Overview
Automated, hardware-independent Hand-Eye Calibration
Use Cases
Best For
- Calibrating RGBD cameras (like Kinect) with robots for MoveIt! collision avoidance
- Performing eye-in-hand calibration for vision-guided robotic tasks
- Setting up eye-on-base calibration with stationary tracking systems
- Automating calibration data collection via robotic movement
- Managing multiple calibrations between different robots and tracking systems
- Simplifying hand-eye calibration in educational or research robotics projects
Not Ideal For
- Projects exclusively using ROS2, as the ROS2 version is a separate, less mature development branch
- Non-ROS robotic systems that do not publish tf frames or use launch files
- Applications requiring real-time, adaptive calibration during dynamic operation
- Teams needing a fully scriptable, headless calibration process without GUI interaction
Pros & Cons
Pros
Integrates with MoveIt! to automatically generate and execute pose sequences for sampling, reducing manual effort and ensuring consistent data collection, as highlighted in the robot movement integration feature.
Offers a choice of OpenCV hand-eye calibration algorithms (Tsai-Lenz, Park, Horaud, etc.), allowing users to optimize for different scenarios, as noted in the multiple algorithms section.
Provides rqt_easy_handeye for interactive sample management, calibration computation, and accuracy evaluation, making the process user-friendly and accessible, as described in the GUI and evaluator features.
Works with any robot and tracking system that publish tf frames, ensuring broad compatibility without hardware-specific modifications, per the hardware independence philosophy.
Cons
Primarily supports ROS1; the ROS2 version is a separate, development project with potential stability issues, limiting use in modern ROS2-based systems.
Requires users to correctly specify tf frames and integrate multiple launch files, which can be error-prone and has a steep learning curve, as evident from the detailed usage instructions.
Designed for static transforms published on startup; not suitable for dynamic environments where calibration needs continuous updates during operation.
Frequently Asked Questions
Related Projects
kalibrThe Kalibr visual-inertial calibration toolbox
lidar_camera_calibrationROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"
imu_utilsA ROS package tool to analyze the IMU performance.
lidar_alignA simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor
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