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icp.hpp
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#ifndef PCL_REGISTRATION_IMPL_ICP_HPP_
#define PCL_REGISTRATION_IMPL_ICP_HPP_
#include <pcl/correspondence.h>
namespace pcl {
// NOLINTBEGIN(readability-container-data-pointer)
template <typename PointSource, typename PointTarget, typename Scalar>
void
IterativeClosestPoint<PointSource, PointTarget, Scalar>::transformCloud(
const PointCloudSource& input, PointCloudSource& output, const Matrix4& transform)
{
Eigen::Vector4f pt(0.0f, 0.0f, 0.0f, 1.0f), pt_t;
Eigen::Matrix4f tr = transform.template cast<float>();
// XYZ is ALWAYS present due to the templatization, so we only have to check for
// normals
if (source_has_normals_) {
Eigen::Vector3f nt, nt_t;
Eigen::Matrix3f rot = tr.block<3, 3>(0, 0);
for (std::size_t i = 0; i < input.size(); ++i) {
const auto* data_in = reinterpret_cast<const std::uint8_t*>(&input[i]);
auto* data_out = reinterpret_cast<std::uint8_t*>(&output[i]);
memcpy(&pt[0], data_in + x_idx_offset_, sizeof(float));
memcpy(&pt[1], data_in + y_idx_offset_, sizeof(float));
memcpy(&pt[2], data_in + z_idx_offset_, sizeof(float));
if (!std::isfinite(pt[0]) || !std::isfinite(pt[1]) || !std::isfinite(pt[2]))
continue;
pt_t = tr * pt;
memcpy(data_out + x_idx_offset_, &pt_t[0], sizeof(float));
memcpy(data_out + y_idx_offset_, &pt_t[1], sizeof(float));
memcpy(data_out + z_idx_offset_, &pt_t[2], sizeof(float));
memcpy(&nt[0], data_in + nx_idx_offset_, sizeof(float));
memcpy(&nt[1], data_in + ny_idx_offset_, sizeof(float));
memcpy(&nt[2], data_in + nz_idx_offset_, sizeof(float));
if (!std::isfinite(nt[0]) || !std::isfinite(nt[1]) || !std::isfinite(nt[2]))
continue;
nt_t = rot * nt;
memcpy(data_out + nx_idx_offset_, &nt_t[0], sizeof(float));
memcpy(data_out + ny_idx_offset_, &nt_t[1], sizeof(float));
memcpy(data_out + nz_idx_offset_, &nt_t[2], sizeof(float));
}
}
else {
for (std::size_t i = 0; i < input.size(); ++i) {
const auto* data_in = reinterpret_cast<const std::uint8_t*>(&input[i]);
auto* data_out = reinterpret_cast<std::uint8_t*>(&output[i]);
memcpy(&pt[0], data_in + x_idx_offset_, sizeof(float));
memcpy(&pt[1], data_in + y_idx_offset_, sizeof(float));
memcpy(&pt[2], data_in + z_idx_offset_, sizeof(float));
if (!std::isfinite(pt[0]) || !std::isfinite(pt[1]) || !std::isfinite(pt[2]))
continue;
pt_t = tr * pt;
memcpy(data_out + x_idx_offset_, &pt_t[0], sizeof(float));
memcpy(data_out + y_idx_offset_, &pt_t[1], sizeof(float));
memcpy(data_out + z_idx_offset_, &pt_t[2], sizeof(float));
}
}
}
template <typename PointSource, typename PointTarget, typename Scalar>
void
IterativeClosestPoint<PointSource, PointTarget, Scalar>::computeTransformation(
PointCloudSource& output, const Matrix4& guess)
{
// Point cloud containing the correspondences of each point in <input, indices>
PointCloudSourcePtr input_transformed(new PointCloudSource);
nr_iterations_ = 0;
converged_ = false;
// Initialise final transformation to the guessed one
final_transformation_ = guess;
// If the guessed transformation is non identity
if (guess != Matrix4::Identity()) {
input_transformed->resize(input_->size());
// Apply guessed transformation prior to search for neighbours
transformCloud(*input_, *input_transformed, guess);
}
else
*input_transformed = *input_;
transformation_ = Matrix4::Identity();
// Make blobs if necessary
determineRequiredBlobData();
PCLPointCloud2::Ptr target_blob(new PCLPointCloud2);
if (need_target_blob_)
pcl::toPCLPointCloud2(*target_, *target_blob);
// Pass in the default target for the Correspondence Estimation/Rejection code
correspondence_estimation_->setInputTarget(target_);
if (correspondence_estimation_->requiresTargetNormals())
correspondence_estimation_->setTargetNormals(target_blob);
// Correspondence Rejectors need a binary blob
for (std::size_t i = 0; i < correspondence_rejectors_.size(); ++i) {
registration::CorrespondenceRejector::Ptr& rej = correspondence_rejectors_[i];
if (rej->requiresTargetPoints())
rej->setTargetPoints(target_blob);
if (rej->requiresTargetNormals() && target_has_normals_)
rej->setTargetNormals(target_blob);
}
convergence_criteria_->setMaximumIterations(max_iterations_);
convergence_criteria_->setRelativeMSE(euclidean_fitness_epsilon_);
convergence_criteria_->setTranslationThreshold(transformation_epsilon_);
if (transformation_rotation_epsilon_ > 0)
convergence_criteria_->setRotationThreshold(transformation_rotation_epsilon_);
// Repeat until convergence
do {
// Get blob data if needed
PCLPointCloud2::Ptr input_transformed_blob;
if (need_source_blob_) {
input_transformed_blob.reset(new PCLPointCloud2);
toPCLPointCloud2(*input_transformed, *input_transformed_blob);
}
// Save the previously estimated transformation
previous_transformation_ = transformation_;
// Set the source each iteration, to ensure the dirty flag is updated
correspondence_estimation_->setInputSource(input_transformed);
if (correspondence_estimation_->requiresSourceNormals())
correspondence_estimation_->setSourceNormals(input_transformed_blob);
// Estimate correspondences
if (use_reciprocal_correspondence_)
correspondence_estimation_->determineReciprocalCorrespondences(
*correspondences_, corr_dist_threshold_);
else
correspondence_estimation_->determineCorrespondences(*correspondences_,
corr_dist_threshold_);
// if (correspondence_rejectors_.empty ())
CorrespondencesPtr temp_correspondences(new Correspondences(*correspondences_));
for (std::size_t i = 0; i < correspondence_rejectors_.size(); ++i) {
registration::CorrespondenceRejector::Ptr& rej = correspondence_rejectors_[i];
PCL_DEBUG("Applying a correspondence rejector method: %s.\n",
rej->getClassName().c_str());
if (rej->requiresSourcePoints())
rej->setSourcePoints(input_transformed_blob);
if (rej->requiresSourceNormals() && source_has_normals_)
rej->setSourceNormals(input_transformed_blob);
rej->setInputCorrespondences(temp_correspondences);
rej->getCorrespondences(*correspondences_);
// Modify input for the next iteration
if (i < correspondence_rejectors_.size() - 1)
*temp_correspondences = *correspondences_;
}
// Check whether we have enough correspondences
if (correspondences_->size() < min_number_correspondences_) {
PCL_ERROR("[pcl::%s::computeTransformation] Not enough correspondences found. "
"Relax your threshold parameters.\n",
getClassName().c_str());
convergence_criteria_->setConvergenceState(
pcl::registration::DefaultConvergenceCriteria<
Scalar>::CONVERGENCE_CRITERIA_NO_CORRESPONDENCES);
converged_ = false;
break;
}
// Estimate the transform
transformation_estimation_->estimateRigidTransformation(
*input_transformed, *target_, *correspondences_, transformation_);
// Transform the data
transformCloud(*input_transformed, *input_transformed, transformation_);
// Obtain the final transformation
final_transformation_ = transformation_ * final_transformation_;
++nr_iterations_;
// Update the visualization of icp convergence
if (update_visualizer_ != nullptr) {
pcl::Indices source_indices_good, target_indices_good;
for (const Correspondence& corr : *correspondences_) {
source_indices_good.emplace_back(corr.index_query);
target_indices_good.emplace_back(corr.index_match);
}
update_visualizer_(
*input_transformed, source_indices_good, *target_, target_indices_good);
}
converged_ = static_cast<bool>((*convergence_criteria_));
} while (convergence_criteria_->getConvergenceState() ==
pcl::registration::DefaultConvergenceCriteria<
Scalar>::CONVERGENCE_CRITERIA_NOT_CONVERGED);
// Transform the input cloud using the final transformation
PCL_DEBUG("Transformation "
"is:\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%5f\t%5f\t%5f\t%5f\n\t%"
"5f\t%5f\t%5f\t%5f\n",
final_transformation_(0, 0),
final_transformation_(0, 1),
final_transformation_(0, 2),
final_transformation_(0, 3),
final_transformation_(1, 0),
final_transformation_(1, 1),
final_transformation_(1, 2),
final_transformation_(1, 3),
final_transformation_(2, 0),
final_transformation_(2, 1),
final_transformation_(2, 2),
final_transformation_(2, 3),
final_transformation_(3, 0),
final_transformation_(3, 1),
final_transformation_(3, 2),
final_transformation_(3, 3));
// Copy all the values
output = *input_;
// Transform the XYZ + normals
transformCloud(*input_, output, final_transformation_);
}
template <typename PointSource, typename PointTarget, typename Scalar>
void
IterativeClosestPoint<PointSource, PointTarget, Scalar>::determineRequiredBlobData()
{
need_source_blob_ = false;
need_target_blob_ = false;
// Check estimator
need_source_blob_ |= correspondence_estimation_->requiresSourceNormals();
need_target_blob_ |= correspondence_estimation_->requiresTargetNormals();
// Add warnings if necessary
if (correspondence_estimation_->requiresSourceNormals() && !source_has_normals_) {
PCL_WARN("[pcl::%s::determineRequiredBlobData] Estimator expects source normals, "
"but we can't provide them.\n",
getClassName().c_str());
}
if (correspondence_estimation_->requiresTargetNormals() && !target_has_normals_) {
PCL_WARN("[pcl::%s::determineRequiredBlobData] Estimator expects target normals, "
"but we can't provide them.\n",
getClassName().c_str());
}
// Check rejectors
for (std::size_t i = 0; i < correspondence_rejectors_.size(); i++) {
registration::CorrespondenceRejector::Ptr& rej = correspondence_rejectors_[i];
need_source_blob_ |= rej->requiresSourcePoints();
need_source_blob_ |= rej->requiresSourceNormals();
need_target_blob_ |= rej->requiresTargetPoints();
need_target_blob_ |= rej->requiresTargetNormals();
if (rej->requiresSourceNormals() && !source_has_normals_) {
PCL_WARN("[pcl::%s::determineRequiredBlobData] Rejector %s expects source "
"normals, but we can't provide them.\n",
getClassName().c_str(),
rej->getClassName().c_str());
}
if (rej->requiresTargetNormals() && !target_has_normals_) {
PCL_WARN("[pcl::%s::determineRequiredBlobData] Rejector %s expects target "
"normals, but we can't provide them.\n",
getClassName().c_str(),
rej->getClassName().c_str());
}
}
}
template <typename PointSource, typename PointTarget, typename Scalar>
void
IterativeClosestPointWithNormals<PointSource, PointTarget, Scalar>::transformCloud(
const PointCloudSource& input, PointCloudSource& output, const Matrix4& transform)
{
pcl::transformPointCloudWithNormals(input, output, transform);
}
// NOLINTEND(readability-container-data-pointer)
} // namespace pcl
#endif /* PCL_REGISTRATION_IMPL_ICP_HPP_ */
pcl::IterativeClosestPoint::Matrix4
typename Registration< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition icp.h:143
pcl::IterativeClosestPoint::PointCloudSource
typename Registration< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition icp.h:101
pcl::IterativeClosestPoint::transformCloud
virtual void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform)
Apply a rigid transform to a given dataset.
Definition icp.hpp:51
pcl::IterativeClosestPoint::determineRequiredBlobData
virtual void determineRequiredBlobData()
Looks at the Estimators and Rejectors and determines whether their blob-setter methods need to be cal...
Definition icp.hpp:272
pcl::IterativeClosestPoint::computeTransformation
void computeTransformation(PointCloudSource &output, const Matrix4 &guess) override
Rigid transformation computation method with initial guess.
Definition icp.hpp:115
pcl::IterativeClosestPoint::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition icp.h:102
pcl::IterativeClosestPointWithNormals::PointCloudSource
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::PointCloudSource PointCloudSource
Definition icp.h:335
pcl::IterativeClosestPointWithNormals::transformCloud
void transformCloud(const PointCloudSource &input, PointCloudSource &output, const Matrix4 &transform) override
Apply a rigid transform to a given dataset.
Definition icp.hpp:314
pcl::IterativeClosestPointWithNormals::Matrix4
typename IterativeClosestPoint< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition icp.h:340
pcl::registration::CorrespondenceRejector::Ptr
shared_ptr< CorrespondenceRejector > Ptr
Definition correspondence_rejection.h:56
pcl::registration::DefaultConvergenceCriteria
DefaultConvergenceCriteria represents an instantiation of ConvergenceCriteria, and implements the fol...
Definition default_convergence_criteria.h:65
pcl::transformPointCloudWithNormals
void transformPointCloudWithNormals(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Transform a point cloud and rotate its normals using an Eigen transform.
Definition transforms.hpp:349
pcl
Definition convolution.h:46
pcl::CorrespondencesPtr
shared_ptr< Correspondences > CorrespondencesPtr
Definition correspondence.h:90
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition correspondence.h:89
pcl::toPCLPointCloud2
void toPCLPointCloud2(const pcl::PointCloud< PointT > &cloud, pcl::PCLPointCloud2 &msg)
Convert a pcl::PointCloud<T> object to a PCLPointCloud2 binary data blob.
Definition conversions.h:264
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::Correspondence
Correspondence represents a match between two entities (e.g., points, descriptors,...
Definition correspondence.h:61
pcl::PCLPointCloud2
Definition PCLPointCloud2.h:17
pcl::PCLPointCloud2::Ptr
shared_ptr< ::pcl::PCLPointCloud2 > Ptr
Definition PCLPointCloud2.h:35