kalibr
A toolbox for calibrating multi-camera, visual-inertial, and rolling shutter sensor systems in robotics.
What is kalibr?
Kalibr is a toolbox for calibrating multi-sensor systems, specifically addressing multi-camera, visual-inertial, and rolling shutter camera calibration. It solves spatial, temporal, and intrinsic calibration problems to ensure accurate sensor fusion, which is essential for robotics applications like SLAM and autonomous navigation.
Robotics researchers, engineers, and developers working with visual-inertial systems, autonomous vehicles, or drones who need precise sensor calibration for reliable state estimation and perception.
Developers choose Kalibr for its comprehensive, research-backed calibration methods, support for a wide range of camera models, and ability to handle complex multi-sensor setups with rolling shutter cameras, all available as open-source with Docker and ROS integration.
Overview
The Kalibr visual-inertial calibration toolbox
Use Cases
Best For
- Calibrating multi-camera rigs for stereo vision or surround-view systems
- Performing visual-inertial calibration for SLAM or odometry pipelines
- Calibrating rolling shutter cameras in dynamic robotics applications
- Multi-IMU calibration in complex sensor suites for drones or robots
- Academic research requiring reproducible, state-of-the-art calibration methods
- Integrating and calibrating heterogeneous sensor systems in autonomous vehicles
Not Ideal For
- Projects requiring real-time, online calibration during continuous robot operation
- Simple setups with only a single static camera and no inertial sensors
- Teams not using ROS or unable to deploy Docker containers in their workflow
- Applications where a graphical user interface is preferred over command-line tools
Pros & Cons
Pros
Solves multi-camera, visual-inertial, and multi-IMU calibration in a unified toolbox, as detailed in the README's list of addressed problems.
Supports a wide range of camera models, including double sphere models, which were added via community contributions and are highlighted in the news section.
Based on peer-reviewed papers from conferences like ICRA and CVPR, ensuring methods are validated and academically sound, as referenced in the README.
Specifically addresses full intrinsic calibration for rolling shutter cameras, a feature added via PR and supported by cited research.
Cons
Requires setting up via Docker or a ROS workspace, which the wiki guides but can be time-consuming and prone to dependency issues, especially with older Ubuntu versions.
Tightly integrated with ROS 1 and Docker, making it unsuitable for projects outside these ecosystems or with strict deployment constraints.
Uses continuous-time batch estimation methods, which are not designed for real-time calibration and require pre-recorded data, limiting dynamic applications.
Frequently Asked Questions
Related Projects
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.
easy_handeyeAutomated, hardware-independent Hand-Eye Calibration
lidar_alignA simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor
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