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transformation_estimation_lm.hpp
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#ifndef PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#define PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_
#include <pcl/registration/warp_point_rigid_6d.h>
#include <unsupported/Eigen/NonLinearOptimization>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
TransformationEstimationLM()
: tmp_src_()
, tmp_tgt_()
, warp_point_(new WarpPointRigid6D<PointSource, PointTarget, MatScalar>){};
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
const pcl::PointCloud<PointTarget>& cloud_tgt,
Matrix4& transformation_matrix) const
{
// <cloud_src,cloud_src> is the source dataset
if (cloud_src.size() != cloud_tgt.size()) {
PCL_ERROR("[pcl::registration::TransformationEstimationLM::"
"estimateRigidTransformation] ");
PCL_ERROR("Number or points in source (%zu) differs than target (%zu)!\n",
static_cast<std::size_t>(cloud_src.size()),
static_cast<std::size_t>(cloud_tgt.size()));
return;
}
if (cloud_src.size() < 4) // need at least 4 samples
{
PCL_ERROR("[pcl::registration::TransformationEstimationLM::"
"estimateRigidTransformation] ");
PCL_ERROR("Need at least 4 points to estimate a transform! Source and target have "
"%zu points!\n",
static_cast<std::size_t>(cloud_src.size()));
return;
}
int n_unknowns = warp_point_->getDimension();
VectorX x(n_unknowns);
x.setZero();
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
OptimizationFunctor functor(static_cast<int>(cloud_src.size()), this);
Eigen::NumericalDiff<OptimizationFunctor> num_diff(functor);
// Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, double> lm
// (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, MatScalar> lm(
num_diff);
int info = lm.minimize(x);
// Compute the norm of the residuals
PCL_DEBUG(
"[pcl::registration::TransformationEstimationLM::estimateRigidTransformation]");
PCL_DEBUG("LM solver finished with exit code %i, having a residual norm of %g. \n",
info,
lm.fvec.norm());
PCL_DEBUG("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG(" %f", x[i]);
PCL_DEBUG("]\n");
// Return the correct transformation
warp_point_->setParam(x);
transformation_matrix = warp_point_->getTransform();
tmp_src_ = nullptr;
tmp_tgt_ = nullptr;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
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::registration::TransformationEstimationLM::estimateRigidTransformation] "
"Number or points in source (%zu) differs than target (%zu)!\n",
indices_src.size(),
static_cast<std::size_t>(cloud_tgt.size()));
return;
}
// <cloud_src,cloud_src> is the source dataset
transformation_matrix.setIdentity();
const auto nr_correspondences = cloud_tgt.size();
pcl::Indices indices_tgt;
indices_tgt.resize(nr_correspondences);
for (std::size_t i = 0; i < nr_correspondences; ++i)
indices_tgt[i] = i;
estimateRigidTransformation(
cloud_src, indices_src, cloud_tgt, indices_tgt, transformation_matrix);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
inline void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
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::registration::TransformationEstimationLM::estimateRigidTransformation] "
"Number or points in source (%lu) differs than target (%lu)!\n",
indices_src.size(),
indices_tgt.size());
return;
}
if (indices_src.size() < 4) // need at least 4 samples
{
PCL_ERROR("[pcl::IterativeClosestPointNonLinear::estimateRigidTransformationLM] ");
PCL_ERROR("Need at least 4 points to estimate a transform! Source and target have "
"%lu points!",
indices_src.size());
return;
}
int n_unknowns = warp_point_->getDimension(); // get dimension of unknown space
VectorX x(n_unknowns);
x.setConstant(n_unknowns, 0);
// Set temporary pointers
tmp_src_ = &cloud_src;
tmp_tgt_ = &cloud_tgt;
tmp_idx_src_ = &indices_src;
tmp_idx_tgt_ = &indices_tgt;
OptimizationFunctorWithIndices functor(static_cast<int>(indices_src.size()), this);
Eigen::NumericalDiff<OptimizationFunctorWithIndices> num_diff(functor);
// Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices> > lm
// (num_diff);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctorWithIndices>,
MatScalar>
lm(num_diff);
int info = lm.minimize(x);
// Compute the norm of the residuals
PCL_DEBUG(
"[pcl::registration::TransformationEstimationLM::estimateRigidTransformation] LM "
"solver finished with exit code %i, having a residual norm of %g. \n",
info,
lm.fvec.norm());
PCL_DEBUG("Final solution: [%f", x[0]);
for (int i = 1; i < n_unknowns; ++i)
PCL_DEBUG(" %f", x[i]);
PCL_DEBUG("]\n");
// Return the correct transformation
warp_point_->setParam(x);
transformation_matrix = warp_point_->getTransform();
tmp_src_ = nullptr;
tmp_tgt_ = nullptr;
tmp_idx_src_ = tmp_idx_tgt_ = nullptr;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
inline void
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
estimateRigidTransformation(const pcl::PointCloud<PointSource>& cloud_src,
const pcl::PointCloud<PointTarget>& cloud_tgt,
const pcl::Correspondences& correspondences,
Matrix4& transformation_matrix) const
{
const auto nr_correspondences = correspondences.size();
pcl::Indices indices_src(nr_correspondences);
pcl::Indices indices_tgt(nr_correspondences);
for (std::size_t i = 0; i < nr_correspondences; ++i) {
indices_src[i] = correspondences[i].index_query;
indices_tgt[i] = correspondences[i].index_match;
}
estimateRigidTransformation(
cloud_src, indices_src, cloud_tgt, indices_tgt, transformation_matrix);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
int
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
OptimizationFunctor::operator()(const VectorX& x, VectorX& fvec) const
{
const PointCloud<PointSource>& src_points = *estimator_->tmp_src_;
const PointCloud<PointTarget>& tgt_points = *estimator_->tmp_tgt_;
// Initialize the warp function with the given parameters
estimator_->warp_point_->setParam(x);
// Transform each source point and compute its distance to the corresponding target
// point
for (int i = 0; i < values(); ++i) {
const PointSource& p_src = src_points[i];
const PointTarget& p_tgt = tgt_points[i];
// Transform the source point based on the current warp parameters
Vector4 p_src_warped;
estimator_->warp_point_->warpPoint(p_src, p_src_warped);
// Estimate the distance (cost function)
fvec[i] = estimator_->computeDistance(p_src_warped, p_tgt);
}
return (0);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename MatScalar>
int
pcl::registration::TransformationEstimationLM<PointSource, PointTarget, MatScalar>::
OptimizationFunctorWithIndices::operator()(const VectorX& x, VectorX& fvec) const
{
const PointCloud<PointSource>& src_points = *estimator_->tmp_src_;
const PointCloud<PointTarget>& tgt_points = *estimator_->tmp_tgt_;
const pcl::Indices& src_indices = *estimator_->tmp_idx_src_;
const pcl::Indices& tgt_indices = *estimator_->tmp_idx_tgt_;
// Initialize the warp function with the given parameters
estimator_->warp_point_->setParam(x);
// Transform each source point and compute its distance to the corresponding target
// point
for (int i = 0; i < values(); ++i) {
const PointSource& p_src = src_points[src_indices[i]];
const PointTarget& p_tgt = tgt_points[tgt_indices[i]];
// Transform the source point based on the current warp parameters
Vector4 p_src_warped;
estimator_->warp_point_->warpPoint(p_src, p_src_warped);
// Estimate the distance (cost function)
fvec[i] = estimator_->computeDistance(p_src_warped, p_tgt);
}
return (0);
}
// #define PCL_INSTANTIATE_TransformationEstimationLM(T,U) template class PCL_EXPORTS
// pcl::registration::TransformationEstimationLM<T,U>;
#endif /* PCL_REGISTRATION_TRANSFORMATION_ESTIMATION_LM_HPP_ */
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::registration::TransformationEstimationLM::TransformationEstimationLM
TransformationEstimationLM()
Constructor.
Definition transformation_estimation_lm.hpp:50
pcl::registration::TransformationEstimationLM::Vector4
Eigen::Matrix< MatScalar, 4, 1 > Vector4
Definition transformation_estimation_lm.h:76
pcl::registration::TransformationEstimationLM::estimateRigidTransformation
void estimateRigidTransformation(const pcl::PointCloud< PointSource > &cloud_src, const pcl::PointCloud< PointTarget > &cloud_tgt, Matrix4 &transformation_matrix) const override
Estimate a rigid rotation transformation between a source and a target point cloud using LM.
Definition transformation_estimation_lm.hpp:59
pcl::registration::TransformationEstimationLM< PointSource, PointTarget, float >::VectorX
Eigen::Matrix< float, Eigen::Dynamic, 1 > VectorX
Definition transformation_estimation_lm.h:75
pcl::registration::TransformationEstimationLM::Matrix4
typename TransformationEstimation< PointSource, PointTarget, MatScalar >::Matrix4 Matrix4
Definition transformation_estimation_lm.h:78
pcl::registration::WarpPointRigid6D
WarpPointRigid3D enables 6D (3D rotation + 3D translation) transformations for points.
Definition warp_point_rigid_6d.h:56
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition correspondence.h:89
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::registration::TransformationEstimationLM::OptimizationFunctor::operator()
int operator()(const VectorX &x, VectorX &fvec) const
Fill fvec from x.
Definition transformation_estimation_lm.hpp:241
pcl::registration::TransformationEstimationLM::OptimizationFunctorWithIndices::operator()
int operator()(const VectorX &x, VectorX &fvec) const
Fill fvec from x.
Definition transformation_estimation_lm.hpp:269