This repository contains a single catkin package, called srrg2_apriltag_calibration. This package lives inside the srrg2 multiverse.

Note that this package has internally the original AprilTag3 library inside (commit number cad1009f9fbf457e2a5393c0fb6c8a05042a0611). This is entirely developed and engineered by AprilRobotics. Further information can be found in the official AprilTag3 repository.

AprilTag3-based calibration for 3D LiDAR and monocular camera. The core idea is quite simple:

• project lidar cloud to get an intensity image
• use AprilTag3 library to detect tag on the intensity image
• use AprilTag3 library to detect tag on the camera image
• if a tag is detected in both sensors, you have associations between 3D points (lidar) and 2D ones (camera)
• setup a LS posit solver to minimize the reprojection error between 3D and 2D points.

The things that you need are the following:

• lidar cloud and lidar specifications (to do the projection)
• rectified camera images and camera matrix.

The srrg2_apriltag_calibration package is developed using our srrg2 framework. All our software is tested both with Ubuntu 18.04 and 16.04 (GCC 5 and 7), still the remaining of this guide refers to Ubuntu 18.04. Please follow this guide to build and run srrg2_apriltag_calibration on your machine:

1. initialize the srrg2 Catkin workspace following the guide here. As indicated in the aforementioned guide, we suggest to have a directory in which you clone all the srrg2 repositories (referred here as SRRG2_SOURCE_ROOT) and a directory that contains the Catkin workspace (referred here as SRRG2_WS_ROOT)

2. clone all the srrg2 dependencies of this package

cd <SRRG2_SOURCE_ROOT>
git clone https://github.com/srrg-sapienza/srrg2_cmake_modules.git # basic cmake-modules
git clone https://github.com/srrg-sapienza/srrg2_core.git # core data-structures and
git clone https://github.com/srrg-sapienza/srrg2_solver.git # solver (both for registration and global optimization)
git clone https://github.com/srrg-sapienza/srrg2_qgl_viewport.git # opengl viewport

3. clone this repository

cd <SRRG2_SOURCE_ROOT>
git clone https://github.com/srrg-sapienza/srrg2_apriltag_calibration.git

4. link all the required packages in your Catkin workspace

cd <SRRG2_WS_ROOT>/src
ln -s <SRRG2_SOURCE_ROOT>/srrg2_cmake_modules .
ln -s <SRRG2_SOURCE_ROOT>/srrg2_core/srrg2_core .
ln -s <SRRG2_SOURCE_ROOT>/srrg2_solver/srrg2_solver .
ln -s <SRRG2_SOURCE_ROOT>/srrg2_qgl_viewport/srrg2_qgl_viewport .
ln -s <SRRG2_SOURCE_ROOT>/srrg2_apriltag_calibration .

5. build using Catkin

cd <SRRG2_WS_ROOT>
catkin build srrg2_apriltag_calibration

6. [OPTIONAL] build unit-tests using catkin

cd <SRRG2_WS_ROOT>
catkin build srrg2_apriltag_calibration --catkin-make-args tests

This package provides two main executables:

1. app_offline_calibration: reads data from either a bag or a .boss file and performs offline calibration. The output is the trasform T_lidar_in_camera written as [x y z qx qy qz] - i.e. translation and unit quaternion. To execute this app type:

rosrun srrg2_apriltag_calibration app_offline_calibration -h

2. app_extrinsic_visualization: given the output file of the offline calibration, it visually shows the reprojections of the lidar points in the camera image, to visually evaluate the data. To execute this app type:

rosrun srrg2_apriltag_calibration app_extrinsic_visualization -h

Example of calibration output. In red is reported the tag detection from camera data, in blue the reprojection of the lidar detection.

• Irvin Aloise

Thanks to the IPB team for their support during the development of this package :D

BSD 3.0