// Calculate the image position measurement noise at this location.
// @param h The image location
//
// This is not constant across the image. It has the value of m_measurement_sd at
// the centre, increasing with radial distance to 2*m_measurement_sd at the corners
Eigen::Matrix2d Camera::MeasurementNoise(const Eigen::Vector2d& h)
{
// Distance of point we are considering from image centre
const double distance = (h - centre_).norm();
const double max_distance = centre_.norm();
const double ratio = distance / max_distance; // goes from 0 to 1
const double SD_image_filter_to_use = measurement_sd_ * (1.0 + ratio);
const double measurement_noise_variance = SD_image_filter_to_use * SD_image_filter_to_use;
// RiRES is diagonal
Eigen::Matrix2d noise;
noise.setIdentity();
noise *= measurement_noise_variance;
return noise;
}
void ProbabilisticStereoTriangulator<CAMERA_GEOMETRY_T>::getUncertainty(
size_t keypointIdxA, size_t keypointIdxB,
const Eigen::Vector4d& homogeneousPoint_A,
Eigen::Matrix3d& outPointUOplus_A, bool& outCanBeInitialized) const {
OKVIS_ASSERT_TRUE_DBG(Exception,frameA_&&frameB_,"initialize with frames before use!");
// also get the point in the other coordinate representation
//Eigen::Vector4d& homogeneousPoint_B=_T_BA*homogeneousPoint_A;
Eigen::Vector4d hPA = homogeneousPoint_A;
// calculate point uncertainty by constructing the lhs of the Gauss-Newton equation system.
// note: the transformation T_WA is assumed constant and identity w.l.o.g.
Eigen::Matrix<double, 9, 9> H = H_;
// keypointA_t& kptA = _frameA_ptr->keypoint(keypointIdxA);
// keypointB_t& kptB = _frameB_ptr->keypoint(keypointIdxB);
Eigen::Vector2d kptA, kptB;
frameA_->getKeypoint(camIdA_, keypointIdxA, kptA);
frameB_->getKeypoint(camIdB_, keypointIdxB, kptB);
// assemble the stuff from the reprojection errors
double keypointStdDev;
frameA_->getKeypointSize(camIdA_, keypointIdxA, keypointStdDev);
keypointStdDev = 0.8 * keypointStdDev / 12.0;
Eigen::Matrix2d inverseMeasurementCovariance = Eigen::Matrix2d::Identity()
* (1.0 / (keypointStdDev * keypointStdDev));
::okvis::ceres::ReprojectionError<CAMERA_GEOMETRY_T> reprojectionErrorA(
frameA_->geometryAs<CAMERA_GEOMETRY_T>(camIdA_), 0, kptA,
inverseMeasurementCovariance);
//typename keypointA_t::measurement_t residualA;
Eigen::Matrix<double, 2, 1> residualA;
Eigen::Matrix<double, 2, 4, Eigen::RowMajor> J_hpA;
Eigen::Matrix<double, 2, 3, Eigen::RowMajor> J_hpA_min;
double* jacobiansA[3];
jacobiansA[0] = 0; // do not calculate, T_WA is fixed identity transform
jacobiansA[1] = J_hpA.data();
jacobiansA[2] = 0; // fixed extrinsics
double* jacobiansA_min[3];
jacobiansA_min[0] = 0; // do not calculate, T_WA is fixed identity transform
jacobiansA_min[1] = J_hpA_min.data();
jacobiansA_min[2] = 0; // fixed extrinsics
const double* parametersA[3];
//const double* test = _poseA.parameters();
parametersA[0] = poseA_.parameters();
parametersA[1] = hPA.data();
parametersA[2] = extrinsics_.parameters();
reprojectionErrorA.EvaluateWithMinimalJacobians(parametersA, residualA.data(),
jacobiansA, jacobiansA_min);
inverseMeasurementCovariance.setIdentity();
frameB_->getKeypointSize(camIdB_, keypointIdxB, keypointStdDev);
keypointStdDev = 0.8 * keypointStdDev / 12.0;
inverseMeasurementCovariance *= 1.0 / (keypointStdDev * keypointStdDev);
::okvis::ceres::ReprojectionError<CAMERA_GEOMETRY_T> reprojectionErrorB(
frameB_->geometryAs<CAMERA_GEOMETRY_T>(camIdB_), 0, kptB,
inverseMeasurementCovariance);
Eigen::Matrix<double, 2, 1> residualB;
Eigen::Matrix<double, 2, 7, Eigen::RowMajor> J_TB;
Eigen::Matrix<double, 2, 6, Eigen::RowMajor> J_TB_min;
Eigen::Matrix<double, 2, 4, Eigen::RowMajor> J_hpB;
Eigen::Matrix<double, 2, 3, Eigen::RowMajor> J_hpB_min;
double* jacobiansB[3];
jacobiansB[0] = J_TB.data();
jacobiansB[1] = J_hpB.data();
jacobiansB[2] = 0; // fixed extrinsics
double* jacobiansB_min[3];
jacobiansB_min[0] = J_TB_min.data();
jacobiansB_min[1] = J_hpB_min.data();
jacobiansB_min[2] = 0; // fixed extrinsics
const double* parametersB[3];
parametersB[0] = poseB_.parameters();
parametersB[1] = hPA.data();
parametersB[2] = extrinsics_.parameters();
reprojectionErrorB.EvaluateWithMinimalJacobians(parametersB, residualB.data(),
jacobiansB, jacobiansB_min);
// evaluate again closer:
hPA.head<3>() = 0.8 * (hPA.head<3>() - T_AB_.r() / 2.0 * hPA[3])
+ T_AB_.r() / 2.0 * hPA[3];
reprojectionErrorB.EvaluateWithMinimalJacobians(parametersB, residualB.data(),
jacobiansB, jacobiansB_min);
if (residualB.transpose() * residualB < 4.0)
outCanBeInitialized = false;
else
outCanBeInitialized = true;
// now add to H:
H.bottomRightCorner<3, 3>() += J_hpA_min.transpose() * J_hpA_min;
H.topLeftCorner<6, 6>() += J_TB_min.transpose() * J_TB_min;
H.topRightCorner<6, 3>() += J_TB_min.transpose() * J_hpB_min;
H.bottomLeftCorner<3, 6>() += J_hpB_min.transpose() * J_TB_min;
H.bottomRightCorner<3, 3>() += J_hpB_min.transpose() * J_hpB_min;
// invert (if invertible) to get covariance:
Eigen::Matrix<double, 9, 9> cov;
if (H.colPivHouseholderQr().rank() < 9) {
outCanBeInitialized = false;
return;
}
cov = H.inverse(); // FIXME: use the QR decomposition for this...
outPointUOplus_A = cov.bottomRightCorner<3, 3>();
}