本文整理汇总了C++中Matrix4::block方法的典型用法代码示例。如果您正苦于以下问题：C++ Matrix4::block方法的具体用法？C++ Matrix4::block怎么用？C++ Matrix4::block使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Matrix4的用法示例。
在下文中一共展示了Matrix4::block方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: H
template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::registration::TransformationEstimation2D<PointSource, PointTarget, Scalar>::getTransformationFromCorrelation (
const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> &cloud_src_demean,
const Eigen::Matrix<Scalar, 4, 1> ¢roid_src,
const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> &cloud_tgt_demean,
const Eigen::Matrix<Scalar, 4, 1> ¢roid_tgt,
Matrix4 &transformation_matrix) const
{
transformation_matrix.setIdentity ();
// Assemble the correlation matrix H = source * target'
Eigen::Matrix<Scalar, 3, 3> H = (cloud_src_demean * cloud_tgt_demean.transpose ()).topLeftCorner (3, 3);
float angle = atan2 ((H (0, 1) - H (1, 0)), (H(0, 0) + H (1, 1)));
Eigen::Matrix<Scalar, 3, 3> R (Eigen::Matrix<Scalar, 3, 3>::Identity ());
R (0, 0) = R (1, 1) = cos (angle);
R (0, 1) = -sin (angle);
R (1, 0) = sin (angle);
// Return the correct transformation
transformation_matrix.topLeftCorner (3, 3).matrix () = R;
const Eigen::Matrix<Scalar, 3, 1> Rc (R * centroid_src.head (3).matrix ());
transformation_matrix.block (0, 3, 3, 1).matrix () = centroid_tgt.head (3) - Rc;
}示例2: svd
template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::registration::TransformationEstimationSVD<PointSource, PointTarget, Scalar>::getTransformationFromCorrelation (
const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> &cloud_src_demean,
const Eigen::Matrix<Scalar, 4, 1> ¢roid_src,
const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> &cloud_tgt_demean,
const Eigen::Matrix<Scalar, 4, 1> ¢roid_tgt,
Matrix4 &transformation_matrix) const
{
transformation_matrix.setIdentity ();
// Assemble the correlation matrix H = source * target'
Eigen::Matrix<Scalar, 3, 3> H = (cloud_src_demean * cloud_tgt_demean.transpose ()).topLeftCorner (3, 3);
// Compute the Singular Value Decomposition
Eigen::JacobiSVD<Eigen::Matrix<Scalar, 3, 3> > svd (H, Eigen::ComputeFullU | Eigen::ComputeFullV);
Eigen::Matrix<Scalar, 3, 3> u = svd.matrixU ();
Eigen::Matrix<Scalar, 3, 3> v = svd.matrixV ();
// Compute R = V * U'
if (u.determinant () * v.determinant () < 0)
{
for (int x = 0; x < 3; ++x)
v (x, 2) *= -1;
}
Eigen::Matrix<Scalar, 3, 3> R = v * u.transpose ();
// Return the correct transformation
transformation_matrix.topLeftCorner (3, 3) = R;
const Eigen::Matrix<Scalar, 3, 1> Rc (R * centroid_src.head (3));
transformation_matrix.block (0, 3, 3, 1) = centroid_tgt.head (3) - Rc;
}示例3:
template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::registration::TransformationEstimationTranslation<PointSource, PointTarget, Scalar>::getTransformationFromCorrelation (
const Eigen::Matrix<Scalar, 4, 1> ¢roid_src,
const Eigen::Matrix<Scalar, 4, 1> ¢roid_tgt,
Matrix4 &transformation_matrix) const
{
transformation_matrix.setIdentity ();
transformation_matrix.block (0, 3, 3, 1) = centroid_tgt.head (3) - centroid_src.head (3);
}示例4: svd
template <typename PointSource, typename PointTarget, typename Scalar> void
pcl::registration::TransformationEstimationSVDScale<PointSource, PointTarget, Scalar>::getTransformationFromCorrelation (
const Eigen::MatrixXf &cloud_src_demean,
const Eigen::Vector4f ¢roid_src,
const Eigen::MatrixXf &cloud_tgt_demean,
const Eigen::Vector4f ¢roid_tgt,
Matrix4 &transformation_matrix) const
{
transformation_matrix.setIdentity ();
// Assemble the correlation matrix H = source * target'
Eigen::Matrix<Scalar, 3, 3> H = (cloud_src_demean.cast<Scalar> () * cloud_tgt_demean.cast<Scalar> ().transpose ()).topLeftCorner (3, 3);
// Compute the Singular Value Decomposition
Eigen::JacobiSVD<Eigen::Matrix<Scalar, 3, 3> > svd (H, Eigen::ComputeFullU | Eigen::ComputeFullV);
Eigen::Matrix<Scalar, 3, 3> u = svd.matrixU ();
Eigen::Matrix<Scalar, 3, 3> v = svd.matrixV ();
// Compute R = V * U'
if (u.determinant () * v.determinant () < 0)
{
for (int x = 0; x < 3; ++x)
v (x, 2) *= -1;
}
Eigen::Matrix<Scalar, 3, 3> R = v * u.transpose ();
// rotated cloud
Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> src_ = R * cloud_src_demean.cast<Scalar> ();
float scale1, scale2;
double sum_ss = 0.0f, sum_tt = 0.0f, sum_tt_ = 0.0f;
for (unsigned corrIdx = 0; corrIdx < cloud_src_demean.cols (); ++corrIdx)
{
sum_ss += cloud_src_demean (0, corrIdx) * cloud_src_demean (0, corrIdx);
sum_ss += cloud_src_demean (1, corrIdx) * cloud_src_demean (1, corrIdx);
sum_ss += cloud_src_demean (2, corrIdx) * cloud_src_demean (2, corrIdx);
sum_tt += cloud_tgt_demean (0, corrIdx) * cloud_tgt_demean (0, corrIdx);
sum_tt += cloud_tgt_demean (1, corrIdx) * cloud_tgt_demean (1, corrIdx);
sum_tt += cloud_tgt_demean (2, corrIdx) * cloud_tgt_demean (2, corrIdx);
sum_tt_ += cloud_tgt_demean (0, corrIdx) * src_ (0, corrIdx);
sum_tt_ += cloud_tgt_demean (1, corrIdx) * src_ (1, corrIdx);
sum_tt_ += cloud_tgt_demean (2, corrIdx) * src_ (2, corrIdx);
}
scale1 = sqrt (sum_tt / sum_ss);
scale2 = sum_tt_ / sum_ss;
float scale = scale2;
transformation_matrix.topLeftCorner (3, 3) = scale * R;
const Eigen::Matrix<Scalar, 3, 1> Rc (R * centroid_src.cast<Scalar> ().head (3));
transformation_matrix.block (0, 3, 3, 1) = centroid_tgt.cast<Scalar> (). head (3) - Rc;
}