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opencv/modules/calib3d/test/test_calibration_hand_eye.cpp

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// 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 {
static std::string 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;
}
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<Mat> &R_gripper2base, std::vector<Mat> &t_gripper2base,
std::vector<Mat> &R_target2cam, std::vector<Mat> &t_target2cam,
bool noise, Mat& R_cam2gripper, Mat& t_cam2gripper);
Mat homogeneousInverse(const Mat& T);
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<std::vector<double> > vec_rvec_diff(5);
std::vector<std::vector<double> > vec_tvec_diff(5);
std::vector<std::vector<double> > vec_rvec_diff_noise(5);
std::vector<std::vector<double> > vec_tvec_diff_noise(5);
std::vector<HandEyeCalibrationMethod> 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<Mat> R_gripper2base, t_gripper2base;
std::vector<Mat> 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<Mat> R_gripper2base, t_gripper2base;
std::vector<Mat> 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<double>(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<double>(0,0) = theta*axis.at<double>(0,0);
rvec.at<double>(1,0) = theta*axis.at<double>(1,0);
rvec.at<double>(2,0) = theta*axis.at<double>(2,0);
tvec.create(3, 1, CV_64FC1);
tvec.at<double>(0,0) = rng.uniform(min_tx, max_tx);
tvec.at<double>(1,0) = rng.uniform(min_ty, max_ty);
tvec.at<double>(2,0) = rng.uniform(min_tz, max_tz);
if (random_sign)
{
tvec.at<double>(0,0) *= sign_double(rng.uniform(-1.0, 1.0));
tvec.at<double>(1,0) *= sign_double(rng.uniform(-1.0, 1.0));
tvec.at<double>(2,0) *= sign_double(rng.uniform(-1.0, 1.0));
}
cv::Rodrigues(rvec, R);
}
void CV_CalibrateHandEyeTest::simulateData(RNG& rng, int nPoses,
std::vector<Mat> &R_gripper2base, std::vector<Mat> &t_gripper2base,
std::vector<Mat> &R_target2cam, std::vector<Mat> &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<double>(0,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(1,0) += rng.gaussian(0.002);
rvec_target2cam_noise.at<double>(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<double>(0,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(1,0) += rng.gaussian(0.005);
t_target2cam_noise.at<double>(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<double>(0,0) += rng.gaussian(0.001);
rvec_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
rvec_gripper2base_noise.at<double>(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<double>(0,0) += rng.gaussian(0.001);
t_gripper2base_noise.at<double>(1,0) += rng.gaussian(0.001);
t_gripper2base_noise.at<double>(2,0) += rng.gaussian(0.001);
}
// test rvec represenation
Mat rvec_target2cam;
cv::Rodrigues(T_target2cam(Rect(0, 0, 3, 3)), rvec_target2cam);
R_target2cam.push_back(rvec_target2cam);
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;
}
double CV_CalibrateHandEyeTest::sign_double(double val)
{
return (0 < val) - (val < 0);
}
///////////////////////////////////////////////////////////////////////////////////////////////////
TEST(Calib3d_CalibrateHandEye, regression) { CV_CalibrateHandEyeTest test; test.safe_run(); }
TEST(Calib3d_CalibrateHandEye, regression_17986)
{
const std::string camera_poses_filename = findDataFile("cv/hand_eye_calibration/cali.txt");
const std::string end_effector_poses = findDataFile("cv/hand_eye_calibration/robot_cali.txt");
std::vector<Mat> R_target2cam;
std::vector<Mat> t_target2cam;
// Parse camera poses
{
std::ifstream file(camera_poses_filename.c_str());
ASSERT_TRUE(file.is_open());
int ndata = 0;
file >> ndata;
R_target2cam.reserve(ndata);
t_target2cam.reserve(ndata);
std::string image_name;
Matx33d cameraMatrix;
Matx33d R;
Matx31d t;
Matx16d distCoeffs;
Matx13d distCoeffs2;
while (file >> image_name >>
cameraMatrix(0,0) >> cameraMatrix(0,1) >> cameraMatrix(0,2) >>
cameraMatrix(1,0) >> cameraMatrix(1,1) >> cameraMatrix(1,2) >>
cameraMatrix(2,0) >> cameraMatrix(2,1) >> cameraMatrix(2,2) >>
R(0,0) >> R(0,1) >> R(0,2) >>
R(1,0) >> R(1,1) >> R(1,2) >>
R(2,0) >> R(2,1) >> R(2,2) >>
t(0) >> t(1) >> t(2) >>
distCoeffs(0) >> distCoeffs(1) >> distCoeffs(2) >> distCoeffs(3) >> distCoeffs(4) >>
distCoeffs2(0) >> distCoeffs2(1) >> distCoeffs2(2)) {
R_target2cam.push_back(Mat(R));
t_target2cam.push_back(Mat(t));
}
}
std::vector<Mat> R_gripper2base;
std::vector<Mat> t_gripper2base;
// Parse end-effector poses
{
std::ifstream file(end_effector_poses.c_str());
ASSERT_TRUE(file.is_open());
int ndata = 0;
file >> ndata;
R_gripper2base.reserve(ndata);
t_gripper2base.reserve(ndata);
Matx33d R;
Matx31d t;
Matx14d last_row;
while (file >>
R(0,0) >> R(0,1) >> R(0,2) >> t(0) >>
R(1,0) >> R(1,1) >> R(1,2) >> t(1) >>
R(2,0) >> R(2,1) >> R(2,2) >> t(2) >>
last_row(0) >> last_row(1) >> last_row(2) >> last_row(3)) {
R_gripper2base.push_back(Mat(R));
t_gripper2base.push_back(Mat(t));
}
}
std::vector<HandEyeCalibrationMethod> 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);
for (size_t idx = 0; idx < methods.size(); idx++) {
SCOPED_TRACE(cv::format("method=%s", getMethodName(methods[idx]).c_str()));
Matx33d R_cam2gripper_est;
Matx31d t_cam2gripper_est;
calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam, R_cam2gripper_est, t_cam2gripper_est, methods[idx]);
EXPECT_TRUE(checkRange(R_cam2gripper_est));
EXPECT_TRUE(checkRange(t_cam2gripper_est));
}
}
}} // namespace