Calibrate a camera using aruco markers

[cameraMatrix, distCoeffs, reprojErr] = cv.calibrateCameraAruco(corners, ids, counter, board, imageSize)
[cameraMatrix, distCoeffs, reprojErr, rvecs, tvecs, stdDevsIntrinsics, stdDevsExtrinsics, perViewErrors] = cv.calibrateCameraAruco(...)
[...] = cv.calibrateCameraAruco(..., 'OptionName',optionValue, ...)

Input

• corners vector of detected marker corners in all frames. The corners should have the same format returned by cv.detectMarkers {{[x,y],..}, ..}.
• ids vector of identifiers for each marker in corners (0-based).
• counter number of markers in each frame so that corners and ids can be split.
• board Marker board layout.
• imageSize Size of the image [w,h] used only to initialize the intrinsic camera matrix.

Output

• cameraMatrix Output 3x3 floating-point camera matrix A = [fx 0 cx; 0 fy cy; 0 0 1]. If any of UseIntrinsicGuess, FixAspectRatio, or FixFocalLength are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
• distCoeffs Output vector of distortion coefficients [k1,k2,p1,p2,k3,k4,k5,k6,s1,s2,s3,s4] of 4, 5, 8 or 12 elements.
• reprojErr the overall RMS re-projection error.
• rvecs Output cell-array of rotation vectors estimated for each board view. That is, each k-th rotation vector together with the corresponding k-th translation vector (see the next output parameter description) brings the board pattern from the model coordinate space (in which object points are specified) to the world coordinate space, that is, a real position of the board pattern in the k-th pattern view (k=0..M-1).
• tvecs Output cell-array of translation vectors estimated for each pattern view.
• stdDevsIntrinsics Output vector of standard deviations estimated for intrinsic parameters. Order of deviations values: (fx,fy,cx,cy,k1,k2,p1,p2,k3,k4,k5,k6,s1,s2,s3,s4,taux,tauy). If one of parameters is not estimated, its deviation is equals to zero.
• stdDevsExtrinsics Output vector of standard deviations estimated for extrinsic parameters. Order of deviations values: (R1, T1, ..., RM, TM) where M is number of pattern views, Ri, Ti are concatenated 1x3 vectors.
• perViewErrors Output vector of average re-projection errors estimated for each pattern view.

Options

• CameraMatrix, DistCoeffs, UseIntrinsicGuess, FixPrincipalPoint, FixFocalLength, FixAspectRatio, ZeroTangentDist, FixTangentDist, FixK1, ..., FixK6, RationalModel, ThinPrismModel, FixS1S2S3S4, TiltedModel, FixTauXTauY, UseLU, UseQR Different flags for the calibration process. See cv.calibrateCamera for details.
• Criteria Termination criteria for the iterative optimization algorithm. default struct('type','Count+EPS', 'maxCount',30, 'epsilon',eps)

This function calibrates a camera using an Aruco Board. The function receives a list of detected markers from several views of the board. The process is similar to the chessboard calibration in cv.calibrateCamera.