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transformation_validation_euclidean.hpp
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#ifndef PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
#define PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
namespace pcl {
namespace registration {
template <typename PointSource, typename PointTarget, typename Scalar>
double
TransformationValidationEuclidean<PointSource, PointTarget, Scalar>::
validateTransformation(const PointCloudSourceConstPtr& cloud_src,
const PointCloudTargetConstPtr& cloud_tgt,
const Matrix4& transformation_matrix) const
{
double fitness_score = 0.0;
// Transform the input dataset using the final transformation
pcl::PointCloud<PointSource> input_transformed;
// transformPointCloud (*cloud_src, input_transformed, transformation_matrix);
input_transformed.resize(cloud_src->size());
for (std::size_t i = 0; i < cloud_src->size(); ++i) {
const PointSource& src = (*cloud_src)[i];
PointTarget& tgt = input_transformed[i];
tgt.x = static_cast<float>(
transformation_matrix(0, 0) * src.x + transformation_matrix(0, 1) * src.y +
transformation_matrix(0, 2) * src.z + transformation_matrix(0, 3));
tgt.y = static_cast<float>(
transformation_matrix(1, 0) * src.x + transformation_matrix(1, 1) * src.y +
transformation_matrix(1, 2) * src.z + transformation_matrix(1, 3));
tgt.z = static_cast<float>(
transformation_matrix(2, 0) * src.x + transformation_matrix(2, 1) * src.y +
transformation_matrix(2, 2) * src.z + transformation_matrix(2, 3));
}
typename MyPointRepresentation::ConstPtr point_rep(new MyPointRepresentation);
if (!force_no_recompute_) {
tree_->setPointRepresentation(point_rep);
tree_->setInputCloud(cloud_tgt);
}
pcl::Indices nn_indices(1);
std::vector<float> nn_dists(1);
// For each point in the source dataset
int nr = 0;
for (const auto& point : input_transformed) {
// Find its nearest neighbor in the target
tree_->nearestKSearch(point, 1, nn_indices, nn_dists);
// Deal with occlusions (incomplete targets)
if (nn_dists[0] > max_range_)
continue;
// Calculate the fitness score
fitness_score += nn_dists[0];
++nr;
}
if (nr > 0)
return (fitness_score / nr);
return (std::numeric_limits<double>::max());
}
} // namespace registration
} // namespace pcl
#endif // PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition point_cloud.h:462
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation
Internal point representation uses only 3D coordinates for L2.
Definition transformation_validation_euclidean.h:234
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation::ConstPtr
shared_ptr< const MyPointRepresentation > ConstPtr
Definition transformation_validation_euclidean.h:240
pcl::registration::TransformationValidationEuclidean::Matrix4
typename TransformationValidation< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition transformation_validation_euclidean.h:78
pcl::registration::TransformationValidationEuclidean::PointCloudTargetConstPtr
typename TransformationValidation< PointSource, PointTarget >::PointCloudTargetConstPtr PointCloudTargetConstPtr
Definition transformation_validation_euclidean.h:94
pcl::registration::TransformationValidationEuclidean::validateTransformation
double validateTransformation(const PointCloudSourceConstPtr &cloud_src, const PointCloudTargetConstPtr &cloud_tgt, const Matrix4 &transformation_matrix) const
Validate the given transformation with respect to the input cloud data, and return a score.
Definition transformation_validation_euclidean.hpp:51
pcl::registration::TransformationValidationEuclidean::PointCloudSourceConstPtr
typename TransformationValidation< PointSource, PointTarget >::PointCloudSourceConstPtr PointCloudSourceConstPtr
Definition transformation_validation_euclidean.h:91
pcl
Definition convolution.h:46
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133