transformation_estimation_svd.hpp

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#ifndef PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_SVD_HPP_
#define PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_SVD_HPP_
 
#include <pcl/common/eigen.h>
 
namespace pcl {
 
namespace registration {
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
                                const pcl::PointCloud<PointTarget>& cloud_tgt,
                                Matrix4& transformation_matrix) const
{
  const auto nr_points = cloud_src.size();
  if (cloud_tgt.size() != nr_points) {
    PCL_ERROR("[pcl::TransformationEstimationSVD::estimateRigidTransformation] Number "
              "or points in source (%zu) differs than target (%zu)!\n",
              static_cast<std::size_t>(nr_points),
              static_cast<std::size_t>(cloud_tgt.size()));
    return;
  }
 
  ConstCloudIterator<PointSource> source_it(cloud_src);
  ConstCloudIterator<PointTarget> target_it(cloud_tgt);
  estimateRigidTransformation(source_it, target_it, transformation_matrix);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
                                const pcl::Indices& indices_src,
                                const pcl::PointCloud<PointTarget>& cloud_tgt,
                                Matrix4& transformation_matrix) const
{
  if (indices_src.size() != cloud_tgt.size()) {
    PCL_ERROR("[pcl::TransformationSVD::estimateRigidTransformation] Number or points "
              "in source (%zu) differs than target (%zu)!\n",
              indices_src.size(),
              static_cast<std::size_t>(cloud_tgt.size()));
    return;
  }
 
  ConstCloudIterator<PointSource> source_it(cloud_src, indices_src);
  ConstCloudIterator<PointTarget> target_it(cloud_tgt);
  estimateRigidTransformation(source_it, target_it, transformation_matrix);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
                                const pcl::Indices& indices_src,
                                const pcl::PointCloud<PointTarget>& cloud_tgt,
                                const pcl::Indices& indices_tgt,
                                Matrix4& transformation_matrix) const
{
  if (indices_src.size() != indices_tgt.size()) {
    PCL_ERROR("[pcl::TransformationEstimationSVD::estimateRigidTransformation] Number "
              "or points in source (%zu) differs than target (%zu)!\n",
              indices_src.size(),
              indices_tgt.size());
    return;
  }
 
  ConstCloudIterator<PointSource> source_it(cloud_src, indices_src);
  ConstCloudIterator<PointTarget> target_it(cloud_tgt, indices_tgt);
  estimateRigidTransformation(source_it, target_it, transformation_matrix);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
                                const pcl::PointCloud<PointTarget>& cloud_tgt,
                                const pcl::Correspondences& correspondences,
                                Matrix4& transformation_matrix) const
{
  ConstCloudIterator<PointSource> source_it(cloud_src, correspondences, true);
  ConstCloudIterator<PointTarget> target_it(cloud_tgt, correspondences, false);
  estimateRigidTransformation(source_it, target_it, transformation_matrix);
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
inline void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    estimateRigidTransformation(ConstCloudIterator<PointSource>& source_it,
                                ConstCloudIterator<PointTarget>& target_it,
                                Matrix4& transformation_matrix) const
{
  // Convert to Eigen format
  const int npts = static_cast<int>(source_it.size());
 
  if (use_umeyama_) {
    Eigen::Matrix<Scalar, 3, Eigen::Dynamic> cloud_src(3, npts);
    Eigen::Matrix<Scalar, 3, Eigen::Dynamic> cloud_tgt(3, npts);
 
    for (int i = 0; i < npts; ++i) {
      cloud_src(0, i) = source_it->x;
      cloud_src(1, i) = source_it->y;
      cloud_src(2, i) = source_it->z;
      ++source_it;
 
      cloud_tgt(0, i) = target_it->x;
      cloud_tgt(1, i) = target_it->y;
      cloud_tgt(2, i) = target_it->z;
      ++target_it;
    }
 
    // Call Umeyama directly from Eigen (PCL patched version until Eigen is released)
    transformation_matrix = pcl::umeyama(cloud_src, cloud_tgt, false);
  }
  else {
    source_it.reset();
    target_it.reset();
    // <cloud_src,cloud_src> is the source dataset
    transformation_matrix.setIdentity();
 
    Eigen::Matrix<Scalar, 4, 1> centroid_src, centroid_tgt;
    // Estimate the centroids of source, target
    compute3DCentroid(source_it, centroid_src);
    compute3DCentroid(target_it, centroid_tgt);
    source_it.reset();
    target_it.reset();
 
    // Subtract the centroids from source, target
    Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> cloud_src_demean,
        cloud_tgt_demean;
    demeanPointCloud(source_it, centroid_src, cloud_src_demean);
    demeanPointCloud(target_it, centroid_tgt, cloud_tgt_demean);
 
    getTransformationFromCorrelation(cloud_src_demean,
                                     centroid_src,
                                     cloud_tgt_demean,
                                     centroid_tgt,
                                     transformation_matrix);
  }
}
 
template <typename PointSource, typename PointTarget, typename Scalar>
void
TransformationEstimationSVD<PointSource, PointTarget, Scalar>::
    getTransformationFromCorrelation(
        const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>& cloud_src_demean,
        const Eigen::Matrix<Scalar, 4, 1>& centroid_src,
        const Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>& cloud_tgt_demean,
        const Eigen::Matrix<Scalar, 4, 1>& centroid_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()).template 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.template topLeftCorner<3, 3>() = R;
  const Eigen::Matrix<Scalar, 3, 1> Rc(R * centroid_src.template head<3>());
  transformation_matrix.template block<3, 1>(0, 3) =
      centroid_tgt.template head<3>() - Rc;
 
  if (pcl::console::isVerbosityLevelEnabled(pcl::console::L_DEBUG)) {
    size_t N = cloud_src_demean.cols();
    PCL_DEBUG("[pcl::registration::TransformationEstimationSVD::"
              "getTransformationFromCorrelation] Loss: %.10e\n",
              (cloud_tgt_demean - R * cloud_src_demean).squaredNorm() / N);
  }
}
 
} // namespace registration
} // namespace pcl
 
//#define PCL_INSTANTIATE_TransformationEstimationSVD(T,U) template class PCL_EXPORTS
// pcl::registration::TransformationEstimationSVD<T,U>;
 
#endif /* PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_SVD_HPP_ */