diff --git a/doc/opencv.bib b/doc/opencv.bib index aa2bac5062..e2af456532 100644 --- a/doc/opencv.bib +++ b/doc/opencv.bib @@ -17,6 +17,21 @@ number = {7}, url = {http://www.bmva.org/bmvc/2013/Papers/paper0013/paper0013.pdf} } +@inproceedings{Andreff99, + author = {Andreff, Nicolas and Horaud, Radu and Espiau, Bernard}, + title = {On-line Hand-eye Calibration}, + booktitle = {Proceedings of the 2Nd International Conference on 3-D Digital Imaging and Modeling}, + series = {3DIM'99}, + year = {1999}, + isbn = {0-7695-0062-5}, + location = {Ottawa, Canada}, + pages = {430--436}, + numpages = {7}, + url = {http://dl.acm.org/citation.cfm?id=1889712.1889775}, + acmid = {1889775}, + publisher = {IEEE Computer Society}, + address = {Washington, DC, USA}, +} @inproceedings{Arandjelovic:2012:TTE:2354409.2355123, author = {Arandjelovic, Relja}, title = {Three Things Everyone Should Know to Improve Object Retrieval}, @@ -180,6 +195,14 @@ volume = {9}, publisher = {Walter de Gruyter} } +@article{Daniilidis98, + author = {Konstantinos Daniilidis}, + title = {Hand-Eye Calibration Using Dual Quaternions}, + journal = {International Journal of Robotics Research}, + year = {1998}, + volume = {18}, + pages = {286--298} +} @inproceedings{DM03, author = {Drago, Fr{\'e}d{\'e}ric and Myszkowski, Karol and Annen, Thomas and Chiba, Norishige}, title = {Adaptive logarithmic mapping for displaying high contrast scenes}, @@ -431,6 +454,24 @@ publisher = {Cambridge university press}, url = {http://cds.cern.ch/record/1598612/files/0521540518_TOC.pdf} } +@article{Horaud95, + author = {Horaud, Radu and Dornaika, Fadi}, + title = {Hand-eye Calibration}, + journal = {Int. J. Rob. Res.}, + issue_date = {June 1995}, + volume = {14}, + number = {3}, + month = jun, + year = {1995}, + issn = {0278-3649}, + pages = {195--210}, + numpages = {16}, + url = {http://dx.doi.org/10.1177/027836499501400301}, + doi = {10.1177/027836499501400301}, + acmid = {208622}, + publisher = {Sage Publications, Inc.}, + address = {Thousand Oaks, CA, USA} +} @article{Horn81, author = {Horn, Berthold KP and Schunck, Brian G}, title = {Determining Optical Flow}, @@ -667,6 +708,18 @@ number = {2}, publisher = {Elsevier} } +@article{Park94, + author = {F. C. Park and B. J. Martin}, + journal = {IEEE Transactions on Robotics and Automation}, + title = {Robot sensor calibration: solving AX=XB on the Euclidean group}, + year = {1994}, + volume = {10}, + number = {5}, + pages = {717-721}, + doi = {10.1109/70.326576}, + ISSN = {1042-296X}, + month = {Oct} +} @inproceedings{PM03, author = {P{\'e}rez, Patrick and Gangnet, Michel and Blake, Andrew}, title = {Poisson image editing}, @@ -841,6 +894,18 @@ number = {1}, publisher = {Taylor \& Francis} } +@article{Tsai89, + author = {R. Y. Tsai and R. K. Lenz}, + journal = {IEEE Transactions on Robotics and Automation}, + title = {A new technique for fully autonomous and efficient 3D robotics hand/eye calibration}, + year = {1989}, + volume = {5}, + number = {3}, + pages = {345-358}, + doi = {10.1109/70.34770}, + ISSN = {1042-296X}, + month = {June} +} @inproceedings{UES01, author = {Uyttendaele, Matthew and Eden, Ashley and Skeliski, R}, title = {Eliminating ghosting and exposure artifacts in image mosaics}, diff --git a/modules/calib3d/doc/pics/hand-eye_figure.png b/modules/calib3d/doc/pics/hand-eye_figure.png new file mode 100644 index 0000000000..ff41d33f8c Binary files /dev/null and b/modules/calib3d/doc/pics/hand-eye_figure.png differ diff --git a/modules/calib3d/include/opencv2/calib3d.hpp b/modules/calib3d/include/opencv2/calib3d.hpp index c5b3ffd4d3..773ba51e75 100644 --- a/modules/calib3d/include/opencv2/calib3d.hpp +++ b/modules/calib3d/include/opencv2/calib3d.hpp @@ -277,7 +277,7 @@ enum { CALIB_USE_INTRINSIC_GUESS = 0x00001, // for stereo rectification CALIB_ZERO_DISPARITY = 0x00400, CALIB_USE_LU = (1 << 17), //!< use LU instead of SVD decomposition for solving. much faster but potentially less precise - CALIB_USE_EXTRINSIC_GUESS = (1 << 22), //!< for stereoCalibrate + CALIB_USE_EXTRINSIC_GUESS = (1 << 22) //!< for stereoCalibrate }; //! the algorithm for finding fundamental matrix @@ -287,6 +287,14 @@ enum { FM_7POINT = 1, //!< 7-point algorithm FM_RANSAC = 8 //!< RANSAC algorithm. It needs at least 15 points. 7-point algorithm is used. }; +enum HandEyeCalibrationMethod +{ + CALIB_HAND_EYE_TSAI = 0, //!< A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/Eye Calibration @cite Tsai89 + CALIB_HAND_EYE_PARK = 1, //!< Robot Sensor Calibration: Solving AX = XB on the Euclidean Group @cite Park94 + CALIB_HAND_EYE_HORAUD = 2, //!< Hand-eye Calibration @cite Horaud95 + CALIB_HAND_EYE_ANDREFF = 3, //!< On-line Hand-Eye Calibration @cite Andreff99 + CALIB_HAND_EYE_DANIILIDIS = 4 //!< Hand-Eye Calibration Using Dual Quaternions @cite Daniilidis98 +}; /** @brief Converts a rotation matrix to a rotation vector or vice versa. @@ -1402,6 +1410,139 @@ CV_EXPORTS_W Mat getOptimalNewCameraMatrix( InputArray cameraMatrix, InputArray CV_OUT Rect* validPixROI = 0, bool centerPrincipalPoint = false); +/** @brief Computes Hand-Eye calibration: \f$_{}^{g}\textrm{T}_c\f$ + +@param[in] R_gripper2base Rotation part extracted from the homogeneous matrix that transforms a point +expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$). +This is a vector (`vector`) that contains the rotation matrices for all the transformations +from gripper frame to robot base frame. +@param[in] t_gripper2base Translation part extracted from the homogeneous matrix that transforms a point +expressed in the gripper frame to the robot base frame (\f$_{}^{b}\textrm{T}_g\f$). +This is a vector (`vector`) that contains the translation vectors for all the transformations +from gripper frame to robot base frame. +@param[in] R_target2cam Rotation part extracted from the homogeneous matrix that transforms a point +expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$). +This is a vector (`vector`) that contains the rotation matrices for all the transformations +from calibration target frame to camera frame. +@param[in] t_target2cam Rotation part extracted from the homogeneous matrix that transforms a point +expressed in the target frame to the camera frame (\f$_{}^{c}\textrm{T}_t\f$). +This is a vector (`vector`) that contains the translation vectors for all the transformations +from calibration target frame to camera frame. +@param[out] R_cam2gripper Estimated rotation part extracted from the homogeneous matrix that transforms a point +expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$). +@param[out] t_cam2gripper Estimated translation part extracted from the homogeneous matrix that transforms a point +expressed in the camera frame to the gripper frame (\f$_{}^{g}\textrm{T}_c\f$). +@param[in] method One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod + +The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the +rotation then the translation (separable solutions) and the following methods are implemented: + - R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89 + - F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94 + - R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95 + +Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), +with the following implemented method: + - N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99 + - K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98 + +The following picture describes the Hand-Eye calibration problem where the transformation between a camera ("eye") +mounted on a robot gripper ("hand") has to be estimated. + +![](pics/hand-eye_figure.png) + +The calibration procedure is the following: + - a static calibration pattern is used to estimate the transformation between the target frame + and the camera frame + - the robot gripper is moved in order to acquire several poses + - for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for + instance the robot kinematics +\f[ + \begin{bmatrix} + X_b\\ + Y_b\\ + Z_b\\ + 1 + \end{bmatrix} + = + \begin{bmatrix} + _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ + 0_{1 \times 3} & 1 + \end{bmatrix} + \begin{bmatrix} + X_g\\ + Y_g\\ + Z_g\\ + 1 + \end{bmatrix} +\f] + - for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using + for instance a pose estimation method (PnP) from 2D-3D point correspondences +\f[ + \begin{bmatrix} + X_c\\ + Y_c\\ + Z_c\\ + 1 + \end{bmatrix} + = + \begin{bmatrix} + _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ + 0_{1 \times 3} & 1 + \end{bmatrix} + \begin{bmatrix} + X_t\\ + Y_t\\ + Z_t\\ + 1 + \end{bmatrix} +\f] + +The Hand-Eye calibration procedure returns the following homogeneous transformation +\f[ + \begin{bmatrix} + X_g\\ + Y_g\\ + Z_g\\ + 1 + \end{bmatrix} + = + \begin{bmatrix} + _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ + 0_{1 \times 3} & 1 + \end{bmatrix} + \begin{bmatrix} + X_c\\ + Y_c\\ + Z_c\\ + 1 + \end{bmatrix} +\f] + +This problem is also known as solving the \f$\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\f$ equation: +\f[ + \begin{align*} + ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= + \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ + + (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= + \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ + + \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ + \end{align*} +\f] + +\note +Additional information can be found on this [website](http://campar.in.tum.de/Chair/HandEyeCalibration). +\note +A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. +So at least 3 different poses are required, but it is strongly recommended to use many more poses. + + */ +CV_EXPORTS_W void calibrateHandEye( InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base, + InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam, + OutputArray R_cam2gripper, OutputArray t_cam2gripper, + HandEyeCalibrationMethod method=CALIB_HAND_EYE_TSAI ); + /** @brief Converts points from Euclidean to homogeneous space. @param src Input vector of N-dimensional points. diff --git a/modules/calib3d/src/calibration_handeye.cpp b/modules/calib3d/src/calibration_handeye.cpp new file mode 100644 index 0000000000..18561c77fe --- /dev/null +++ b/modules/calib3d/src/calibration_handeye.cpp @@ -0,0 +1,770 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "precomp.hpp" +#include "opencv2/calib3d.hpp" + +namespace cv { + +static Mat homogeneousInverse(const Mat& T) +{ + CV_Assert(T.rows == 4 && T.cols == 4); + + Mat R = T(Rect(0, 0, 3, 3)); + Mat t = T(Rect(3, 0, 1, 3)); + Mat Rt = R.t(); + Mat tinv = -Rt * t; + Mat Tinv = Mat::eye(4, 4, T.type()); + Rt.copyTo(Tinv(Rect(0, 0, 3, 3))); + tinv.copyTo(Tinv(Rect(3, 0, 1, 3))); + + return Tinv; +} + +// q = rot2quatMinimal(R) +// +// R - 3x3 rotation matrix, or 4x4 homogeneous matrix +// q - 3x1 unit quaternion +// q = sin(theta/2) * v +// theta - rotation angle +// v - unit rotation axis, |v| = 1 +static Mat rot2quatMinimal(const Mat& R) +{ + CV_Assert(R.type() == CV_64FC1 && R.rows >= 3 && R.cols >= 3); + + double m00 = R.at(0,0), m01 = R.at(0,1), m02 = R.at(0,2); + double m10 = R.at(1,0), m11 = R.at(1,1), m12 = R.at(1,2); + double m20 = R.at(2,0), m21 = R.at(2,1), m22 = R.at(2,2); + double trace = m00 + m11 + m22; + + double qx, qy, qz; + if (trace > 0) { + double S = sqrt(trace + 1.0) * 2; // S=4*qw + qx = (m21 - m12) / S; + qy = (m02 - m20) / S; + qz = (m10 - m01) / S; + } else if ((m00 > m11)&(m00 > m22)) { + double S = sqrt(1.0 + m00 - m11 - m22) * 2; // S=4*qx + qx = 0.25 * S; + qy = (m01 + m10) / S; + qz = (m02 + m20) / S; + } else if (m11 > m22) { + double S = sqrt(1.0 + m11 - m00 - m22) * 2; // S=4*qy + qx = (m01 + m10) / S; + qy = 0.25 * S; + qz = (m12 + m21) / S; + } else { + double S = sqrt(1.0 + m22 - m00 - m11) * 2; // S=4*qz + qx = (m02 + m20) / S; + qy = (m12 + m21) / S; + qz = 0.25 * S; + } + + return (Mat_(3,1) << qx, qy, qz); +} + +static Mat skew(const Mat& v) +{ + CV_Assert(v.type() == CV_64FC1 && v.rows == 3 && v.cols == 1); + + double vx = v.at(0,0); + double vy = v.at(1,0); + double vz = v.at(2,0); + return (Mat_(3,3) << 0, -vz, vy, + vz, 0, -vx, + -vy, vx, 0); +} + +// R = quatMinimal2rot(q) +// +// q - 3x1 unit quaternion +// R - 3x3 rotation matrix +// q = sin(theta/2) * v +// theta - rotation angle +// v - unit rotation axis, |v| = 1 +static Mat quatMinimal2rot(const Mat& q) +{ + CV_Assert(q.type() == CV_64FC1 && q.rows == 3 && q.cols == 1); + + Mat p = q.t()*q; + double w = sqrt(1 - p.at(0,0)); + + Mat diag_p = Mat::eye(3,3,CV_64FC1)*p.at(0,0); + return 2*q*q.t() + 2*w*skew(q) + Mat::eye(3,3,CV_64FC1) - 2*diag_p; +} + +// q = rot2quat(R) +// +// q - 4x1 unit quaternion +// R - 3x3 rotation matrix +static Mat rot2quat(const Mat& R) +{ + CV_Assert(R.type() == CV_64FC1 && R.rows >= 3 && R.cols >= 3); + + double m00 = R.at(0,0), m01 = R.at(0,1), m02 = R.at(0,2); + double m10 = R.at(1,0), m11 = R.at(1,1), m12 = R.at(1,2); + double m20 = R.at(2,0), m21 = R.at(2,1), m22 = R.at(2,2); + double trace = m00 + m11 + m22; + + double qw, qx, qy, qz; + if (trace > 0) { + double S = sqrt(trace + 1.0) * 2; // S=4*qw + qw = 0.25 * S; + qx = (m21 - m12) / S; + qy = (m02 - m20) / S; + qz = (m10 - m01) / S; + } else if ((m00 > m11)&(m00 > m22)) { + double S = sqrt(1.0 + m00 - m11 - m22) * 2; // S=4*qx + qw = (m21 - m12) / S; + qx = 0.25 * S; + qy = (m01 + m10) / S; + qz = (m02 + m20) / S; + } else if (m11 > m22) { + double S = sqrt(1.0 + m11 - m00 - m22) * 2; // S=4*qy + qw = (m02 - m20) / S; + qx = (m01 + m10) / S; + qy = 0.25 * S; + qz = (m12 + m21) / S; + } else { + double S = sqrt(1.0 + m22 - m00 - m11) * 2; // S=4*qz + qw = (m10 - m01) / S; + qx = (m02 + m20) / S; + qy = (m12 + m21) / S; + qz = 0.25 * S; + } + + return (Mat_(4,1) << qw, qx, qy, qz); +} + +// R = quat2rot(q) +// +// q - 4x1 unit quaternion +// R - 3x3 rotation matrix +static Mat quat2rot(const Mat& q) +{ + CV_Assert(q.type() == CV_64FC1 && q.rows == 4 && q.cols == 1); + + double qw = q.at(0,0); + double qx = q.at(1,0); + double qy = q.at(2,0); + double qz = q.at(3,0); + + Mat R(3, 3, CV_64FC1); + R.at(0, 0) = 1 - 2*qy*qy - 2*qz*qz; + R.at(0, 1) = 2*qx*qy - 2*qz*qw; + R.at(0, 2) = 2*qx*qz + 2*qy*qw; + + R.at(1, 0) = 2*qx*qy + 2*qz*qw; + R.at(1, 1) = 1 - 2*qx*qx - 2*qz*qz; + R.at(1, 2) = 2*qy*qz - 2*qx*qw; + + R.at(2, 0) = 2*qx*qz - 2*qy*qw; + R.at(2, 1) = 2*qy*qz + 2*qx*qw; + R.at(2, 2) = 1 - 2*qx*qx - 2*qy*qy; + + return R; +} + +// Kronecker product or tensor product +// https://stackoverflow.com/a/36552682 +static Mat kron(const Mat& A, const Mat& B) +{ + CV_Assert(A.channels() == 1 && B.channels() == 1); + + Mat1d Ad, Bd; + A.convertTo(Ad, CV_64F); + B.convertTo(Bd, CV_64F); + + Mat1d Kd(Ad.rows * Bd.rows, Ad.cols * Bd.cols, 0.0); + for (int ra = 0; ra < Ad.rows; ra++) + { + for (int ca = 0; ca < Ad.cols; ca++) + { + Kd(Range(ra*Bd.rows, (ra + 1)*Bd.rows), Range(ca*Bd.cols, (ca + 1)*Bd.cols)) = Bd.mul(Ad(ra, ca)); + } + } + + Mat K; + Kd.convertTo(K, A.type()); + return K; +} + +// quaternion multiplication +static Mat qmult(const Mat& s, const Mat& t) +{ + CV_Assert(s.type() == CV_64FC1 && t.type() == CV_64FC1); + CV_Assert(s.rows == 4 && s.cols == 1); + CV_Assert(t.rows == 4 && t.cols == 1); + + double s0 = s.at(0,0); + double s1 = s.at(1,0); + double s2 = s.at(2,0); + double s3 = s.at(3,0); + + double t0 = t.at(0,0); + double t1 = t.at(1,0); + double t2 = t.at(2,0); + double t3 = t.at(3,0); + + Mat q(4, 1, CV_64FC1); + q.at(0,0) = s0*t0 - s1*t1 - s2*t2 - s3*t3; + q.at(1,0) = s0*t1 + s1*t0 + s2*t3 - s3*t2; + q.at(2,0) = s0*t2 - s1*t3 + s2*t0 + s3*t1; + q.at(3,0) = s0*t3 + s1*t2 - s2*t1 + s3*t0; + + return q; +} + +// dq = homogeneous2dualQuaternion(H) +// +// H - 4x4 homogeneous transformation: [R | t; 0 0 0 | 1] +// dq - 8x1 dual quaternion: +static Mat homogeneous2dualQuaternion(const Mat& H) +{ + CV_Assert(H.type() == CV_64FC1 && H.rows == 4 && H.cols == 4); + + Mat dualq(8, 1, CV_64FC1); + Mat R = H(Rect(0, 0, 3, 3)); + Mat t = H(Rect(3, 0, 1, 3)); + + Mat q = rot2quat(R); + Mat qt = Mat::zeros(4, 1, CV_64FC1); + t.copyTo(qt(Rect(0, 1, 1, 3))); + Mat qprime = 0.5 * qmult(qt, q); + + q.copyTo(dualq(Rect(0, 0, 1, 4))); + qprime.copyTo(dualq(Rect(0, 4, 1, 4))); + + return dualq; +} + +// H = dualQuaternion2homogeneous(dq) +// +// H - 4x4 homogeneous transformation: [R | t; 0 0 0 | 1] +// dq - 8x1 dual quaternion: +static Mat dualQuaternion2homogeneous(const Mat& dualq) +{ + CV_Assert(dualq.type() == CV_64FC1 && dualq.rows == 8 && dualq.cols == 1); + + Mat q = dualq(Rect(0, 0, 1, 4)); + Mat qprime = dualq(Rect(0, 4, 1, 4)); + + Mat R = quat2rot(q); + q.at(1,0) = -q.at(1,0); + q.at(2,0) = -q.at(2,0); + q.at(3,0) = -q.at(3,0); + + Mat qt = 2*qmult(qprime, q); + Mat t = qt(Rect(0, 1, 1, 3)); + + Mat H = Mat::eye(4, 4, CV_64FC1); + R.copyTo(H(Rect(0, 0, 3, 3))); + t.copyTo(H(Rect(3, 0, 1, 3))); + + return H; +} + +//Reference: +//R. Y. Tsai and R. K. Lenz, "A new technique for fully autonomous and efficient 3D robotics hand/eye calibration." +//In IEEE Transactions on Robotics and Automation, vol. 5, no. 3, pp. 345-358, June 1989. +//C++ code converted from Zoran Lazarevic's Matlab code: +//http://lazax.com/www.cs.columbia.edu/~laza/html/Stewart/matlab/handEye.m +static void calibrateHandEyeTsai(const std::vector& Hg, const std::vector& Hc, + Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + //Number of unique camera position pairs + int K = static_cast((Hg.size()*Hg.size() - Hg.size()) / 2.0); + //Will store: skew(Pgij+Pcij) + Mat A(3*K, 3, CV_64FC1); + //Will store: Pcij - Pgij + Mat B(3*K, 1, CV_64FC1); + + std::vector vec_Hgij, vec_Hcij; + vec_Hgij.reserve(static_cast(K)); + vec_Hcij.reserve(static_cast(K)); + + int idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + //Defines coordinate transformation from Gi to Gj + //Hgi is from Gi (gripper) to RW (robot base) + //Hgj is from Gj (gripper) to RW (robot base) + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; //eq 6 + vec_Hgij.push_back(Hgij); + //Rotation axis for Rgij which is the 3D rotation from gripper coordinate frame Gi to Gj + Mat Pgij = 2*rot2quatMinimal(Hgij); + + //Defines coordinate transformation from Ci to Cj + //Hci is from CW (calibration target) to Ci (camera) + //Hcj is from CW (calibration target) to Cj (camera) + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); //eq 7 + vec_Hcij.push_back(Hcij); + //Rotation axis for Rcij + Mat Pcij = 2*rot2quatMinimal(Hcij); + + //Left-hand side: skew(Pgij+Pcij) + skew(Pgij+Pcij).copyTo(A(Rect(0, idx*3, 3, 3))); + //Right-hand side: Pcij - Pgij + Mat diff = Pcij - Pgij; + diff.copyTo(B(Rect(0, idx*3, 1, 3))); + } + } + + Mat Pcg_; + //Rotation from camera to gripper is obtained from the set of equations: + // skew(Pgij+Pcij) * Pcg_ = Pcij - Pgij (eq 12) + solve(A, B, Pcg_, DECOMP_SVD); + + Mat Pcg_norm = Pcg_.t() * Pcg_; + //Obtained non-unit quaternion is scaled back to unit value that + //designates camera-gripper rotation + Mat Pcg = 2 * Pcg_ / sqrt(1 + Pcg_norm.at(0,0)); //eq 14 + + Mat Rcg = quatMinimal2rot(Pcg/2.0); + + idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + //Defines coordinate transformation from Gi to Gj + //Hgi is from Gi (gripper) to RW (robot base) + //Hgj is from Gj (gripper) to RW (robot base) + Mat Hgij = vec_Hgij[static_cast(idx)]; + //Defines coordinate transformation from Ci to Cj + //Hci is from CW (calibration target) to Ci (camera) + //Hcj is from CW (calibration target) to Cj (camera) + Mat Hcij = vec_Hcij[static_cast(idx)]; + + //Left-hand side: (Rgij - I) + Mat diff = Hgij(Rect(0,0,3,3)) - Mat::eye(3,3,CV_64FC1); + diff.copyTo(A(Rect(0, idx*3, 3, 3))); + + //Right-hand side: Rcg*Tcij - Tgij + diff = Rcg*Hcij(Rect(3, 0, 1, 3)) - Hgij(Rect(3, 0, 1, 3)); + diff.copyTo(B(Rect(0, idx*3, 1, 3))); + } + } + + Mat Tcg; + //Translation from camera to gripper is obtained from the set of equations: + // (Rgij - I) * Tcg = Rcg*Tcij - Tgij (eq 15) + solve(A, B, Tcg, DECOMP_SVD); + + R_cam2gripper = Rcg; + t_cam2gripper = Tcg; +} + +//Reference: +//F. Park, B. Martin, "Robot Sensor Calibration: Solving AX = XB on the Euclidean Group." +//In IEEE Transactions on Robotics and Automation, 10(5): 717-721, 1994. +//Matlab code: http://math.loyola.edu/~mili/Calibration/ +static void calibrateHandEyePark(const std::vector& Hg, const std::vector& Hc, + Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + Mat M = Mat::zeros(3, 3, CV_64FC1); + + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat Rgij = Hgij(Rect(0, 0, 3, 3)); + Mat Rcij = Hcij(Rect(0, 0, 3, 3)); + + Mat a, b; + Rodrigues(Rgij, a); + Rodrigues(Rcij, b); + + M += b * a.t(); + } + } + + Mat eigenvalues, eigenvectors; + eigen(M.t()*M, eigenvalues, eigenvectors); + + Mat v = Mat::zeros(3, 3, CV_64FC1); + for (int i = 0; i < 3; i++) { + v.at(i,i) = 1.0 / sqrt(eigenvalues.at(i,0)); + } + + Mat R = eigenvectors.t() * v * eigenvectors * M.t(); + R_cam2gripper = R; + + int K = static_cast((Hg.size()*Hg.size() - Hg.size()) / 2.0); + Mat C(3*K, 3, CV_64FC1); + Mat d(3*K, 1, CV_64FC1); + Mat I3 = Mat::eye(3, 3, CV_64FC1); + + int idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat Rgij = Hgij(Rect(0, 0, 3, 3)); + + Mat tgij = Hgij(Rect(3, 0, 1, 3)); + Mat tcij = Hcij(Rect(3, 0, 1, 3)); + + Mat I_tgij = I3 - Rgij; + I_tgij.copyTo(C(Rect(0, 3*idx, 3, 3))); + + Mat A_RB = tgij - R*tcij; + A_RB.copyTo(d(Rect(0, 3*idx, 1, 3))); + } + } + + Mat t; + solve(C, d, t, DECOMP_SVD); + t_cam2gripper = t; +} + +//Reference: +//R. Horaud, F. Dornaika, "Hand-Eye Calibration" +//In International Journal of Robotics Research, 14(3): 195-210, 1995. +//Matlab code: http://math.loyola.edu/~mili/Calibration/ +static void calibrateHandEyeHoraud(const std::vector& Hg, const std::vector& Hc, + Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + Mat A = Mat::zeros(4, 4, CV_64FC1); + + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat Rgij = Hgij(Rect(0, 0, 3, 3)); + Mat Rcij = Hcij(Rect(0, 0, 3, 3)); + + Mat qgij = rot2quat(Rgij); + double r0 = qgij.at(0,0); + double rx = qgij.at(1,0); + double ry = qgij.at(2,0); + double rz = qgij.at(3,0); + + // Q(r) Appendix A + Matx44d Qvi(r0, -rx, -ry, -rz, + rx, r0, -rz, ry, + ry, rz, r0, -rx, + rz, -ry, rx, r0); + + Mat qcij = rot2quat(Rcij); + r0 = qcij.at(0,0); + rx = qcij.at(1,0); + ry = qcij.at(2,0); + rz = qcij.at(3,0); + + // W(r) Appendix A + Matx44d Wvi(r0, -rx, -ry, -rz, + rx, r0, rz, -ry, + ry, -rz, r0, rx, + rz, ry, -rx, r0); + + // Ai = (Q(vi') - W(vi))^T (Q(vi') - W(vi)) + A += (Qvi - Wvi).t() * (Qvi - Wvi); + } + } + + Mat eigenvalues, eigenvectors; + eigen(A, eigenvalues, eigenvectors); + + Mat R = quat2rot(eigenvectors.row(3).t()); + R_cam2gripper = R; + + int K = static_cast((Hg.size()*Hg.size() - Hg.size()) / 2.0); + Mat C(3*K, 3, CV_64FC1); + Mat d(3*K, 1, CV_64FC1); + Mat I3 = Mat::eye(3, 3, CV_64FC1); + + int idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat Rgij = Hgij(Rect(0, 0, 3, 3)); + + Mat tgij = Hgij(Rect(3, 0, 1, 3)); + Mat tcij = Hcij(Rect(3, 0, 1, 3)); + + Mat I_tgij = I3 - Rgij; + I_tgij.copyTo(C(Rect(0, 3*idx, 3, 3))); + + Mat A_RB = tgij - R*tcij; + A_RB.copyTo(d(Rect(0, 3*idx, 1, 3))); + } + } + + Mat t; + solve(C, d, t, DECOMP_SVD); + t_cam2gripper = t; +} + +// sign function, return -1 if negative values, +1 otherwise +static int sign_double(double val) +{ + return (0 < val) - (val < 0); +} + +//Reference: +//N. Andreff, R. Horaud, B. Espiau, "On-line Hand-Eye Calibration." +//In Second International Conference on 3-D Digital Imaging and Modeling (3DIM'99), pages 430-436, 1999. +//Matlab code: http://math.loyola.edu/~mili/Calibration/ +static void calibrateHandEyeAndreff(const std::vector& Hg, const std::vector& Hc, + Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + int K = static_cast((Hg.size()*Hg.size() - Hg.size()) / 2.0); + Mat A(12*K, 12, CV_64FC1); + Mat B(12*K, 1, CV_64FC1); + + Mat I9 = Mat::eye(9, 9, CV_64FC1); + Mat I3 = Mat::eye(3, 3, CV_64FC1); + Mat O9x3 = Mat::zeros(9, 3, CV_64FC1); + Mat O9x1 = Mat::zeros(9, 1, CV_64FC1); + + int idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat Rgij = Hgij(Rect(0, 0, 3, 3)); + Mat Rcij = Hcij(Rect(0, 0, 3, 3)); + + Mat tgij = Hgij(Rect(3, 0, 1, 3)); + Mat tcij = Hcij(Rect(3, 0, 1, 3)); + + //Eq 10 + Mat a00 = I9 - kron(Rgij, Rcij); + Mat a01 = O9x3; + Mat a10 = kron(I3, tcij.t()); + Mat a11 = I3 - Rgij; + + a00.copyTo(A(Rect(0, idx*12, 9, 9))); + a01.copyTo(A(Rect(9, idx*12, 3, 9))); + a10.copyTo(A(Rect(0, idx*12 + 9, 9, 3))); + a11.copyTo(A(Rect(9, idx*12 + 9, 3, 3))); + + O9x1.copyTo(B(Rect(0, idx*12, 1, 9))); + tgij.copyTo(B(Rect(0, idx*12 + 9, 1, 3))); + } + } + + Mat X; + solve(A, B, X, DECOMP_SVD); + + Mat R = X(Rect(0, 0, 1, 9)); + int newSize[] = {3, 3}; + R = R.reshape(1, 2, newSize); + //Eq 15 + double det = determinant(R); + R = pow(sign_double(det) / abs(det), 1.0/3.0) * R; + + Mat w, u, vt; + SVDecomp(R, w, u, vt); + R = u*vt; + + if (determinant(R) < 0) + { + Mat diag = (Mat_(3,3) << 1.0, 0.0, 0.0, + 0.0, 1.0, 0.0, + 0.0, 0.0, -1.0); + R = u*diag*vt; + } + + R_cam2gripper = R; + + Mat t = X(Rect(0, 9, 1, 3)); + t_cam2gripper = t; +} + +//Reference: +//K. Daniilidis, "Hand-Eye Calibration Using Dual Quaternions." +//In The International Journal of Robotics Research,18(3): 286-298, 1998. +//Matlab code: http://math.loyola.edu/~mili/Calibration/ +static void calibrateHandEyeDaniilidis(const std::vector& Hg, const std::vector& Hc, + Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + int K = static_cast((Hg.size()*Hg.size() - Hg.size()) / 2.0); + Mat T = Mat::zeros(6*K, 8, CV_64FC1); + + int idx = 0; + for (size_t i = 0; i < Hg.size(); i++) + { + for (size_t j = i+1; j < Hg.size(); j++, idx++) + { + Mat Hgij = homogeneousInverse(Hg[j]) * Hg[i]; + Mat Hcij = Hc[j] * homogeneousInverse(Hc[i]); + + Mat dualqa = homogeneous2dualQuaternion(Hgij); + Mat dualqb = homogeneous2dualQuaternion(Hcij); + + Mat a = dualqa(Rect(0, 1, 1, 3)); + Mat b = dualqb(Rect(0, 1, 1, 3)); + + Mat aprime = dualqa(Rect(0, 5, 1, 3)); + Mat bprime = dualqb(Rect(0, 5, 1, 3)); + + //Eq 31 + Mat s00 = a - b; + Mat s01 = skew(a + b); + Mat s10 = aprime - bprime; + Mat s11 = skew(aprime + bprime); + Mat s12 = a - b; + Mat s13 = skew(a + b); + + s00.copyTo(T(Rect(0, idx*6, 1, 3))); + s01.copyTo(T(Rect(1, idx*6, 3, 3))); + s10.copyTo(T(Rect(0, idx*6 + 3, 1, 3))); + s11.copyTo(T(Rect(1, idx*6 + 3, 3, 3))); + s12.copyTo(T(Rect(4, idx*6 + 3, 1, 3))); + s13.copyTo(T(Rect(5, idx*6 + 3, 3, 3))); + } + } + + Mat w, u, vt; + SVDecomp(T, w, u, vt); + Mat v = vt.t(); + + Mat u1 = v(Rect(6, 0, 1, 4)); + Mat v1 = v(Rect(6, 4, 1, 4)); + Mat u2 = v(Rect(7, 0, 1, 4)); + Mat v2 = v(Rect(7, 4, 1, 4)); + + //Solves Eq 34, Eq 35 + Mat ma = u1.t()*v1; + Mat mb = u1.t()*v2 + u2.t()*v1; + Mat mc = u2.t()*v2; + + double a = ma.at(0,0); + double b = mb.at(0,0); + double c = mc.at(0,0); + + double s1 = (-b + sqrt(b*b - 4*a*c)) / (2*a); + double s2 = (-b - sqrt(b*b - 4*a*c)) / (2*a); + + Mat sol1 = s1*s1*u1.t()*u1 + 2*s1*u1.t()*u2 + u2.t()*u2; + Mat sol2 = s2*s2*u1.t()*u1 + 2*s2*u1.t()*u2 + u2.t()*u2; + double s, val; + if (sol1.at(0,0) > sol2.at(0,0)) + { + s = s1; + val = sol1.at(0,0); + } + else + { + s = s2; + val = sol2.at(0,0); + } + + double lambda2 = sqrt(1.0 / val); + double lambda1 = s * lambda2; + + Mat dualq = lambda1 * v(Rect(6, 0, 1, 8)) + lambda2*v(Rect(7, 0, 1, 8)); + Mat X = dualQuaternion2homogeneous(dualq); + + Mat R = X(Rect(0, 0, 3, 3)); + Mat t = X(Rect(3, 0, 1, 3)); + R_cam2gripper = R; + t_cam2gripper = t; +} + +void calibrateHandEye(InputArrayOfArrays R_gripper2base, InputArrayOfArrays t_gripper2base, + InputArrayOfArrays R_target2cam, InputArrayOfArrays t_target2cam, + OutputArray R_cam2gripper, OutputArray t_cam2gripper, + HandEyeCalibrationMethod method) +{ + CV_Assert(R_gripper2base.isMatVector() && t_gripper2base.isMatVector() && + R_target2cam.isMatVector() && t_target2cam.isMatVector()); + + std::vector R_gripper2base_, t_gripper2base_; + R_gripper2base.getMatVector(R_gripper2base_); + t_gripper2base.getMatVector(t_gripper2base_); + + std::vector R_target2cam_, t_target2cam_; + R_target2cam.getMatVector(R_target2cam_); + t_target2cam.getMatVector(t_target2cam_); + + CV_Assert(R_gripper2base_.size() == t_gripper2base_.size() && + R_target2cam_.size() == t_target2cam_.size() && + R_gripper2base_.size() == R_target2cam_.size()); + CV_Assert(R_gripper2base_.size() >= 3); + + //Notation used in Tsai paper + //Defines coordinate transformation from G (gripper) to RW (robot base) + std::vector Hg; + Hg.reserve(R_gripper2base_.size()); + for (size_t i = 0; i < R_gripper2base_.size(); i++) + { + Mat m = Mat::eye(4, 4, CV_64FC1); + Mat R = m(Rect(0, 0, 3, 3)); + R_gripper2base_[i].convertTo(R, CV_64F); + + Mat t = m(Rect(3, 0, 1, 3)); + t_gripper2base_[i].convertTo(t, CV_64F); + + Hg.push_back(m); + } + + //Defines coordinate transformation from CW (calibration target) to C (camera) + std::vector Hc; + Hc.reserve(R_target2cam_.size()); + for (size_t i = 0; i < R_target2cam_.size(); i++) + { + Mat m = Mat::eye(4, 4, CV_64FC1); + Mat R = m(Rect(0, 0, 3, 3)); + R_target2cam_[i].convertTo(R, CV_64F); + + Mat t = m(Rect(3, 0, 1, 3)); + t_target2cam_[i].convertTo(t, CV_64F); + + Hc.push_back(m); + } + + Mat Rcg = Mat::eye(3, 3, CV_64FC1); + Mat Tcg = Mat::zeros(3, 1, CV_64FC1); + + switch (method) + { + case CALIB_HAND_EYE_TSAI: + calibrateHandEyeTsai(Hg, Hc, Rcg, Tcg); + break; + + case CALIB_HAND_EYE_PARK: + calibrateHandEyePark(Hg, Hc, Rcg, Tcg); + break; + + case CALIB_HAND_EYE_HORAUD: + calibrateHandEyeHoraud(Hg, Hc, Rcg, Tcg); + break; + + case CALIB_HAND_EYE_ANDREFF: + calibrateHandEyeAndreff(Hg, Hc, Rcg, Tcg); + break; + + case CALIB_HAND_EYE_DANIILIDIS: + calibrateHandEyeDaniilidis(Hg, Hc, Rcg, Tcg); + break; + + default: + break; + } + + Rcg.copyTo(R_cam2gripper); + Tcg.copyTo(t_cam2gripper); +} +} diff --git a/modules/calib3d/test/test_calibration_hand_eye.cpp b/modules/calib3d/test/test_calibration_hand_eye.cpp new file mode 100644 index 0000000000..d2cef969b3 --- /dev/null +++ b/modules/calib3d/test/test_calibration_hand_eye.cpp @@ -0,0 +1,381 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html. + +#include "test_precomp.hpp" +#include "opencv2/calib3d.hpp" + +namespace opencv_test { namespace { + +class CV_CalibrateHandEyeTest : public cvtest::BaseTest +{ +public: + CV_CalibrateHandEyeTest() { + eps_rvec[CALIB_HAND_EYE_TSAI] = 1.0e-8; + eps_rvec[CALIB_HAND_EYE_PARK] = 1.0e-8; + eps_rvec[CALIB_HAND_EYE_HORAUD] = 1.0e-8; + eps_rvec[CALIB_HAND_EYE_ANDREFF] = 1.0e-8; + eps_rvec[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-8; + + eps_tvec[CALIB_HAND_EYE_TSAI] = 1.0e-8; + eps_tvec[CALIB_HAND_EYE_PARK] = 1.0e-8; + eps_tvec[CALIB_HAND_EYE_HORAUD] = 1.0e-8; + eps_tvec[CALIB_HAND_EYE_ANDREFF] = 1.0e-8; + eps_tvec[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-8; + + eps_rvec_noise[CALIB_HAND_EYE_TSAI] = 2.0e-2; + eps_rvec_noise[CALIB_HAND_EYE_PARK] = 2.0e-2; + eps_rvec_noise[CALIB_HAND_EYE_HORAUD] = 2.0e-2; + eps_rvec_noise[CALIB_HAND_EYE_ANDREFF] = 1.0e-2; + eps_rvec_noise[CALIB_HAND_EYE_DANIILIDIS] = 1.0e-2; + + eps_tvec_noise[CALIB_HAND_EYE_TSAI] = 5.0e-2; + eps_tvec_noise[CALIB_HAND_EYE_PARK] = 5.0e-2; + eps_tvec_noise[CALIB_HAND_EYE_HORAUD] = 5.0e-2; + eps_tvec_noise[CALIB_HAND_EYE_ANDREFF] = 5.0e-2; + eps_tvec_noise[CALIB_HAND_EYE_DANIILIDIS] = 5.0e-2; + } +protected: + virtual void run(int); + void generatePose(RNG& rng, double min_theta, double max_theta, + double min_tx, double max_tx, + double min_ty, double max_ty, + double min_tz, double max_tz, + Mat& R, Mat& tvec, + bool randSign=false); + void simulateData(RNG& rng, int nPoses, + std::vector &R_gripper2base, std::vector &t_gripper2base, + std::vector &R_target2cam, std::vector &t_target2cam, + bool noise, Mat& R_cam2gripper, Mat& t_cam2gripper); + Mat homogeneousInverse(const Mat& T); + std::string getMethodName(HandEyeCalibrationMethod method); + double sign_double(double val); + + double eps_rvec[5]; + double eps_tvec[5]; + double eps_rvec_noise[5]; + double eps_tvec_noise[5]; +}; + +void CV_CalibrateHandEyeTest::run(int) +{ + ts->set_failed_test_info(cvtest::TS::OK); + + RNG& rng = ts->get_rng(); + + std::vector > vec_rvec_diff(5); + std::vector > vec_tvec_diff(5); + std::vector > vec_rvec_diff_noise(5); + std::vector > vec_tvec_diff_noise(5); + + std::vector methods; + methods.push_back(CALIB_HAND_EYE_TSAI); + methods.push_back(CALIB_HAND_EYE_PARK); + methods.push_back(CALIB_HAND_EYE_HORAUD); + methods.push_back(CALIB_HAND_EYE_ANDREFF); + methods.push_back(CALIB_HAND_EYE_DANIILIDIS); + + const int nTests = 100; + for (int i = 0; i < nTests; i++) + { + const int nPoses = 10; + { + //No noise + std::vector R_gripper2base, t_gripper2base; + std::vector R_target2cam, t_target2cam; + Mat R_cam2gripper_true, t_cam2gripper_true; + + const bool noise = false; + simulateData(rng, nPoses, R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, noise, R_cam2gripper_true, t_cam2gripper_true); + + for (size_t idx = 0; idx < methods.size(); idx++) + { + Mat rvec_cam2gripper_true; + cv::Rodrigues(R_cam2gripper_true, rvec_cam2gripper_true); + + Mat R_cam2gripper_est, t_cam2gripper_est; + calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper_est, t_cam2gripper_est, methods[idx]); + + Mat rvec_cam2gripper_est; + cv::Rodrigues(R_cam2gripper_est, rvec_cam2gripper_est); + + double rvecDiff = cvtest::norm(rvec_cam2gripper_true, rvec_cam2gripper_est, NORM_L2); + double tvecDiff = cvtest::norm(t_cam2gripper_true, t_cam2gripper_est, NORM_L2); + + vec_rvec_diff[idx].push_back(rvecDiff); + vec_tvec_diff[idx].push_back(tvecDiff); + + const double epsilon_rvec = eps_rvec[idx]; + const double epsilon_tvec = eps_tvec[idx]; + + //Maybe a better accuracy test would be to compare the mean and std errors with some thresholds? + if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec) + { + ts->printf(cvtest::TS::LOG, "Invalid accuracy (no noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n", + getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + } + } + } + + { + //Gaussian noise on transformations between calibration target frame and camera frame and between gripper and robot base frames + std::vector R_gripper2base, t_gripper2base; + std::vector R_target2cam, t_target2cam; + Mat R_cam2gripper_true, t_cam2gripper_true; + + const bool noise = true; + simulateData(rng, nPoses, R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, noise, R_cam2gripper_true, t_cam2gripper_true); + + for (size_t idx = 0; idx < methods.size(); idx++) + { + Mat rvec_cam2gripper_true; + cv::Rodrigues(R_cam2gripper_true, rvec_cam2gripper_true); + + Mat R_cam2gripper_est, t_cam2gripper_est; + calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper_est, t_cam2gripper_est, methods[idx]); + + Mat rvec_cam2gripper_est; + cv::Rodrigues(R_cam2gripper_est, rvec_cam2gripper_est); + + double rvecDiff = cvtest::norm(rvec_cam2gripper_true, rvec_cam2gripper_est, NORM_L2); + double tvecDiff = cvtest::norm(t_cam2gripper_true, t_cam2gripper_est, NORM_L2); + + vec_rvec_diff_noise[idx].push_back(rvecDiff); + vec_tvec_diff_noise[idx].push_back(tvecDiff); + + const double epsilon_rvec = eps_rvec_noise[idx]; + const double epsilon_tvec = eps_tvec_noise[idx]; + + //Maybe a better accuracy test would be to compare the mean and std errors with some thresholds? + if (rvecDiff > epsilon_rvec || tvecDiff > epsilon_tvec) + { + ts->printf(cvtest::TS::LOG, "Invalid accuracy (noise) for method: %s, rvecDiff: %f, epsilon_rvec: %f, tvecDiff: %f, epsilon_tvec: %f\n", + getMethodName(methods[idx]).c_str(), rvecDiff, epsilon_rvec, tvecDiff, epsilon_tvec); + ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); + } + } + } + } + + for (size_t idx = 0; idx < methods.size(); idx++) + { + { + double max_rvec_diff = *std::max_element(vec_rvec_diff[idx].begin(), vec_rvec_diff[idx].end()); + double mean_rvec_diff = std::accumulate(vec_rvec_diff[idx].begin(), + vec_rvec_diff[idx].end(), 0.0) / vec_rvec_diff[idx].size(); + double sq_sum_rvec_diff = std::inner_product(vec_rvec_diff[idx].begin(), vec_rvec_diff[idx].end(), + vec_rvec_diff[idx].begin(), 0.0); + double std_rvec_diff = std::sqrt(sq_sum_rvec_diff / vec_rvec_diff[idx].size() - mean_rvec_diff * mean_rvec_diff); + + double max_tvec_diff = *std::max_element(vec_tvec_diff[idx].begin(), vec_tvec_diff[idx].end()); + double mean_tvec_diff = std::accumulate(vec_tvec_diff[idx].begin(), + vec_tvec_diff[idx].end(), 0.0) / vec_tvec_diff[idx].size(); + double sq_sum_tvec_diff = std::inner_product(vec_tvec_diff[idx].begin(), vec_tvec_diff[idx].end(), + vec_tvec_diff[idx].begin(), 0.0); + double std_tvec_diff = std::sqrt(sq_sum_tvec_diff / vec_tvec_diff[idx].size() - mean_tvec_diff * mean_tvec_diff); + + std::cout << "\nMethod " << getMethodName(methods[idx]) << ":\n" + << "Max rvec error: " << max_rvec_diff << ", Mean rvec error: " << mean_rvec_diff + << ", Std rvec error: " << std_rvec_diff << "\n" + << "Max tvec error: " << max_tvec_diff << ", Mean tvec error: " << mean_tvec_diff + << ", Std tvec error: " << std_tvec_diff << std::endl; + } + { + double max_rvec_diff = *std::max_element(vec_rvec_diff_noise[idx].begin(), vec_rvec_diff_noise[idx].end()); + double mean_rvec_diff = std::accumulate(vec_rvec_diff_noise[idx].begin(), + vec_rvec_diff_noise[idx].end(), 0.0) / vec_rvec_diff_noise[idx].size(); + double sq_sum_rvec_diff = std::inner_product(vec_rvec_diff_noise[idx].begin(), vec_rvec_diff_noise[idx].end(), + vec_rvec_diff_noise[idx].begin(), 0.0); + double std_rvec_diff = std::sqrt(sq_sum_rvec_diff / vec_rvec_diff_noise[idx].size() - mean_rvec_diff * mean_rvec_diff); + + double max_tvec_diff = *std::max_element(vec_tvec_diff_noise[idx].begin(), vec_tvec_diff_noise[idx].end()); + double mean_tvec_diff = std::accumulate(vec_tvec_diff_noise[idx].begin(), + vec_tvec_diff_noise[idx].end(), 0.0) / vec_tvec_diff_noise[idx].size(); + double sq_sum_tvec_diff = std::inner_product(vec_tvec_diff_noise[idx].begin(), vec_tvec_diff_noise[idx].end(), + vec_tvec_diff_noise[idx].begin(), 0.0); + double std_tvec_diff = std::sqrt(sq_sum_tvec_diff / vec_tvec_diff_noise[idx].size() - mean_tvec_diff * mean_tvec_diff); + + std::cout << "Method (noise) " << getMethodName(methods[idx]) << ":\n" + << "Max rvec error: " << max_rvec_diff << ", Mean rvec error: " << mean_rvec_diff + << ", Std rvec error: " << std_rvec_diff << "\n" + << "Max tvec error: " << max_tvec_diff << ", Mean tvec error: " << mean_tvec_diff + << ", Std tvec error: " << std_tvec_diff << std::endl; + } + } +} + +void CV_CalibrateHandEyeTest::generatePose(RNG& rng, double min_theta, double max_theta, + double min_tx, double max_tx, + double min_ty, double max_ty, + double min_tz, double max_tz, + Mat& R, Mat& tvec, + bool random_sign) +{ + Mat axis(3, 1, CV_64FC1); + for (int i = 0; i < 3; i++) + { + axis.at(i,0) = rng.uniform(-1.0, 1.0); + } + double theta = rng.uniform(min_theta, max_theta); + if (random_sign) + { + theta *= sign_double(rng.uniform(-1.0, 1.0)); + } + + Mat rvec(3, 1, CV_64FC1); + rvec.at(0,0) = theta*axis.at(0,0); + rvec.at(1,0) = theta*axis.at(1,0); + rvec.at(2,0) = theta*axis.at(2,0); + + tvec.create(3, 1, CV_64FC1); + tvec.at(0,0) = rng.uniform(min_tx, max_tx); + tvec.at(1,0) = rng.uniform(min_ty, max_ty); + tvec.at(2,0) = rng.uniform(min_tz, max_tz); + + if (random_sign) + { + tvec.at(0,0) *= sign_double(rng.uniform(-1.0, 1.0)); + tvec.at(1,0) *= sign_double(rng.uniform(-1.0, 1.0)); + tvec.at(2,0) *= sign_double(rng.uniform(-1.0, 1.0)); + } + + cv::Rodrigues(rvec, R); +} + +void CV_CalibrateHandEyeTest::simulateData(RNG& rng, int nPoses, + std::vector &R_gripper2base, std::vector &t_gripper2base, + std::vector &R_target2cam, std::vector &t_target2cam, + bool noise, Mat& R_cam2gripper, Mat& t_cam2gripper) +{ + //to avoid generating values close to zero, + //we use positive range values and randomize the sign + const bool random_sign = true; + generatePose(rng, 10.0*CV_PI/180.0, 50.0*CV_PI/180.0, + 0.05, 0.5, 0.05, 0.5, 0.05, 0.5, + R_cam2gripper, t_cam2gripper, random_sign); + + Mat R_target2base, t_target2base; + generatePose(rng, 5.0*CV_PI/180.0, 85.0*CV_PI/180.0, + 0.5, 3.5, 0.5, 3.5, 0.5, 3.5, + R_target2base, t_target2base, random_sign); + + for (int i = 0; i < nPoses; i++) + { + Mat R_gripper2base_, t_gripper2base_; + generatePose(rng, 5.0*CV_PI/180.0, 45.0*CV_PI/180.0, + 0.5, 1.5, 0.5, 1.5, 0.5, 1.5, + R_gripper2base_, t_gripper2base_, random_sign); + + R_gripper2base.push_back(R_gripper2base_); + t_gripper2base.push_back(t_gripper2base_); + + Mat T_cam2gripper = Mat::eye(4, 4, CV_64FC1); + R_cam2gripper.copyTo(T_cam2gripper(Rect(0, 0, 3, 3))); + t_cam2gripper.copyTo(T_cam2gripper(Rect(3, 0, 1, 3))); + + Mat T_gripper2base = Mat::eye(4, 4, CV_64FC1); + R_gripper2base_.copyTo(T_gripper2base(Rect(0, 0, 3, 3))); + t_gripper2base_.copyTo(T_gripper2base(Rect(3, 0, 1, 3))); + + Mat T_base2cam = homogeneousInverse(T_cam2gripper) * homogeneousInverse(T_gripper2base); + Mat T_target2base = Mat::eye(4, 4, CV_64FC1); + R_target2base.copyTo(T_target2base(Rect(0, 0, 3, 3))); + t_target2base.copyTo(T_target2base(Rect(3, 0, 1, 3))); + Mat T_target2cam = T_base2cam * T_target2base; + + if (noise) + { + //Add some noise for the transformation between the target and the camera + Mat R_target2cam_noise = T_target2cam(Rect(0, 0, 3, 3)); + Mat rvec_target2cam_noise; + cv::Rodrigues(R_target2cam_noise, rvec_target2cam_noise); + rvec_target2cam_noise.at(0,0) += rng.gaussian(0.002); + rvec_target2cam_noise.at(1,0) += rng.gaussian(0.002); + rvec_target2cam_noise.at(2,0) += rng.gaussian(0.002); + + cv::Rodrigues(rvec_target2cam_noise, R_target2cam_noise); + + Mat t_target2cam_noise = T_target2cam(Rect(3, 0, 1, 3)); + t_target2cam_noise.at(0,0) += rng.gaussian(0.005); + t_target2cam_noise.at(1,0) += rng.gaussian(0.005); + t_target2cam_noise.at(2,0) += rng.gaussian(0.005); + + //Add some noise for the transformation between the gripper and the robot base + Mat R_gripper2base_noise = T_gripper2base(Rect(0, 0, 3, 3)); + Mat rvec_gripper2base_noise; + cv::Rodrigues(R_gripper2base_noise, rvec_gripper2base_noise); + rvec_gripper2base_noise.at(0,0) += rng.gaussian(0.001); + rvec_gripper2base_noise.at(1,0) += rng.gaussian(0.001); + rvec_gripper2base_noise.at(2,0) += rng.gaussian(0.001); + + cv::Rodrigues(rvec_gripper2base_noise, R_gripper2base_noise); + + Mat t_gripper2base_noise = T_gripper2base(Rect(3, 0, 1, 3)); + t_gripper2base_noise.at(0,0) += rng.gaussian(0.001); + t_gripper2base_noise.at(1,0) += rng.gaussian(0.001); + t_gripper2base_noise.at(2,0) += rng.gaussian(0.001); + } + + R_target2cam.push_back(T_target2cam(Rect(0, 0, 3, 3))); + t_target2cam.push_back(T_target2cam(Rect(3, 0, 1, 3))); + } +} + +Mat CV_CalibrateHandEyeTest::homogeneousInverse(const Mat& T) +{ + CV_Assert( T.rows == 4 && T.cols == 4 ); + + Mat R = T(Rect(0, 0, 3, 3)); + Mat t = T(Rect(3, 0, 1, 3)); + Mat Rt = R.t(); + Mat tinv = -Rt * t; + Mat Tinv = Mat::eye(4, 4, T.type()); + Rt.copyTo(Tinv(Rect(0, 0, 3, 3))); + tinv.copyTo(Tinv(Rect(3, 0, 1, 3))); + + return Tinv; +} + +std::string CV_CalibrateHandEyeTest::getMethodName(HandEyeCalibrationMethod method) +{ + std::string method_name = ""; + switch (method) + { + case CALIB_HAND_EYE_TSAI: + method_name = "Tsai"; + break; + + case CALIB_HAND_EYE_PARK: + method_name = "Park"; + break; + + case CALIB_HAND_EYE_HORAUD: + method_name = "Horaud"; + break; + + case CALIB_HAND_EYE_ANDREFF: + method_name = "Andreff"; + break; + + case CALIB_HAND_EYE_DANIILIDIS: + method_name = "Daniilidis"; + break; + + default: + break; + } + + return method_name; +} + +double CV_CalibrateHandEyeTest::sign_double(double val) +{ + return (0 < val) - (val < 0); +} + +/////////////////////////////////////////////////////////////////////////////////////////////////// + +TEST(Calib3d_CalibrateHandEye, regression) { CV_CalibrateHandEyeTest test; test.safe_run(); } + +}} // namespace