OpenCV  4.1.0 Open Source Computer Vision
Calibration with ArUco and ChArUco

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 calibrateCameraCharuco(). Example:

cv::Ptr<aruco::CharucoBoard> board = ... // create charuco board
cv::Size imgSize = ... // camera image size
std::vector<std::vector<cv::Point2f>> allCharucoCorners;
std::vector<std::vector<int>> allCharucoIds;
// Detect charuco board from several viewpoints and fill allCharucoCorners and allCharucoIds
...
...
// After capturing in several viewpoints, start calibration
cv::Mat cameraMatrix, distCoeffs;
std::vector<cv::Mat> rvecs, tvecs;
int calibrationFlags = ... // Set calibration flags (same than in calibrateCamera() function)
double repError = cv::aruco::calibrateCameraCharuco(allCharucoCorners, allCharucoIds, board, imgSize, cameraMatrix, distCoeffs, rvecs, tvecs, calibrationFlags);

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

The calibrateCameraCharuco() 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. Its format is equivalent to the flags parameter in the OpenCV calibrateCamera() function.

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

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

_output path_" -dp="_path_/detector_params.yml" -w=5 -h=7 -sl=0.04 -ml=0.02 -d=10

## 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. For these cases, the calibrateCameraAruco() function is provided. As in the previous case, it requires the detections of an ArUco board from different viewpoints.

ArUco calibration viewpoints

Example of calibrateCameraAruco() use:

cv::Ptr<aruco::Board> board = ... // create aruco board
cv::Size imgSize = ... // camera image size
std::vector<std::vector<cv::Point2f>> allCornersConcatenated;
std::vector<int> allIdsConcatenated;
std::vector<int> markerCounterPerFrame;
// Detect aruco board from several viewpoints and fill allCornersConcatenated, allIdsConcatenated and markerCounterPerFrame
...
...
// After capturing in several viewpoints, start calibration
cv::Mat cameraMatrix, distCoeffs;
std::vector<cv::Mat> rvecs, tvecs;
int calibrationFlags = ... // Set calibration flags (same than in calibrateCamera() function)
double repError = cv::aruco::calibrateCameraAruco(allCornersConcatenated, allIdsConcatenated, markerCounterPerFrame, board, imgSize, cameraMatrix, distCoeffs, rvecs, tvecs, calibrationFlags);

In this case, and contrary to the calibrateCameraCharuco() function, the detected markers on each viewpoint are concatenated in the arrays allCornersConcatenated and allCornersConcatenated (the first two parameters). The third parameter, the array markerCounterPerFrame, indicates the number of marker detected on each viewpoint. The rest of parameters are the same than in calibrateCameraCharuco(), except the board layout object which does not need to be a CharucoBoard object, it can be any Board object.

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

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

"_path_/calib.txt" -w=5 -h=7 -l=100 -s=10 -d=10