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The ArUco module can also be used to calibrate a camera. Camera calibration consists in obtaining the camera intrinsic parameters and distortion coefficients. This parameters remain fixed unless the camera optic is modified, thus camera calibration only need to be done once.

Camera calibration is usually performed using the OpenCV calibrateCamera() function. This function requires some correspondences between environment points and their projection in the camera image from different viewpoints. In general, these correspondences are obtained from the corners of chessboard patterns. See calibrateCamera() function documentation or the OpenCV calibration tutorial for more detailed information.

Using the ArUco module, calibration can be performed based on ArUco markers corners or ChArUco corners. Calibrating using ArUco is much more versatile than using traditional chessboard patterns, since it allows occlusions or partial views.

As it can be stated, calibration can be done using both, marker corners or ChArUco corners. However, it is highly recommended using the ChArUco corners approach since the provided corners are much more accurate in comparison to the marker corners. Calibration using a standard Board should only be employed in those scenarios where the ChArUco boards cannot be employed because of any kind of restriction.

Calibration with ChArUco Boards

To calibrate using a ChArUco board, it is necessary to detect the board from different viewpoints, in the same way that the standard calibration does with the traditional chessboard pattern. However, due to the benefits of using ChArUco, occlusions and partial views are allowed, and not all the corners need to be visible in all the viewpoints.

ChArUco calibration viewpoints

The function to calibrate is cv::calibrateCamera(). Example:

Ptr<aruco::CharucoBoard> board = ... // create charuco board
Size imageSize = ... // camera image size
 
vector<vector<Point2f>> allCharucoCorners;
vector<vector<int>> allCharucoIds;
vector<vector<Point2f>> allImagePoints;
vector<vector<Point3f>> allObjectPoints;
 
// Detect charuco board from several viewpoints and fill
// allCharucoCorners, allCharucoIds, allImagePoints and allObjectPoints
while(inputVideo.grab()) {
 detector.detectBoard(
 image, currentCharucoCorners, currentCharucoIds
 );
 board.matchImagePoints(
 currentCharucoCorners, currentCharucoIds,
 currentObjectPoints, currentImagePoints
 );
 
 ...
}
 
// After capturing in several viewpoints, start calibration
Mat cameraMatrix, distCoeffs;
vector<Mat> rvecs, tvecs;
 
// Set calibration flags (same than in calibrateCamera() function)
int calibrationFlags = ...
 
double repError = calibrateCamera(
 allObjectPoints, allImagePoints, imageSize,
 cameraMatrix, distCoeffs, rvecs, tvecs, noArray(),
 noArray(), noArray(), calibrationFlags
);

The ChArUco corners and ChArUco identifiers captured on each viewpoint are stored in the vectors allCharucoCorners and allCharucoIds, one element per viewpoint.

The calibrateCamera() function will fill the cameraMatrix and distCoeffs arrays with the camera calibration parameters. It will return the reprojection error obtained from the calibration. The elements in rvecs and tvecs will be filled with the estimated pose of the camera (respect to the ChArUco board) in each of the viewpoints.

Finally, the calibrationFlags parameter determines some of the options for the calibration.

A full working example is included in the calibrate_camera_charuco.cpp inside the samples folder.

Note: The samples now take input via commandline via the OpenCV Commandline Parser. For this file the example parameters will look like

"camera_calib.txt" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10
-v="path_aruco/tutorials/aruco_calibration/images/img_%02d.jpg
-c=path_aruco/samples/tutorial_camera_params.yml

The camera calibration parameters from samples/tutorial_camera_charuco.yml were obtained by aruco_calibration/images/img_00.jpg-img_03.jpg.

Calibration with ArUco Boards

As it has been stated, it is recommended the use of ChAruco boards instead of ArUco boards for camera calibration, since ChArUco corners are more accurate than marker corners. However, in some special cases it must be required to use calibration based on ArUco boards. As in the previous case, it requires the detections of an ArUco board from different viewpoints.

ArUco calibration viewpoints

Example of calibrateCameraAruco() use:

Ptr<aruco::Board> board = ... // create aruco board
Size imgSize = ... // camera image size
 
vector<vector<vector<Point2f>>> allMarkerCorners;
vector<vector<int>> allMarkerIds;
 
// Detect aruco board from several viewpoints and fill allMarkerCorners, allMarkerIds
detector.detectMarkers(image, markerCorners, markerIds, rejectedMarkers);
...
 
// After capturing in several viewpoints, match image points and start calibration
board->matchImagePoints(
 allMarkerCorners[frame], allMarkerIds[frame],
 currentObjPoints, currentImgPoints
);
 
Mat cameraMatrix, distCoeffs;
vector<Mat> rvecs, tvecs;
int calibrationFlags = ... // Set calibration flags (same than in calibrateCamera() function)
 
double repError = calibrateCamera(
 processedObjectPoints, processedImagePoints, imageSize,
 cameraMatrix, distCoeffs, rvecs, tvecs, noArray(),
 noArray(), noArray(), calibrationFlags
);

A full working example is included in the calibrate_camera.cpp inside the samples folder.

Note: The samples now take input via commandline via the OpenCV Commandline Parser. For this file the example parameters will look like

"camera_calib.txt" -w=5 -h=7 -l=100 -s=10 -d=10