Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |
Dependant Packages
Launch files
Messages
Services
Plugins
Recent questions tagged direct_visual_lidar_calibration at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.0.0 |
License | MIT |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- k.koide
Authors
direct_visual_lidar_calibration
This package provides a toolbox for LiDAR-camera calibration that is:
- Generalizable: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.
- Target-less: It does not require a calibration target but uses the environment structure and texture for calibration.
- Single-shot: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.
- Automatic: The calibration process is automatic and does not require an initial guess.
- Accurate and robust: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.
Documentation: https://koide3.github.io/direct_visual_lidar_calibration/
Docker hub: koide3/direct_visual_lidar_calibration
Dependencies
Getting started
License
This package is released under the MIT license.
Publication
Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [PDF]
Contact
Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan
Package Dependencies
Deps | Name |
---|---|
catkin | |
ament_cmake | |
ament_cmake_python | |
roscpp | |
rosbag | |
rclcpp | |
rosbag2_cpp | |
cv_bridge | |
pcl_ros | |
sensor_msgs |
System Dependencies
Name |
---|
fmt |