transformation_validation_euclidean.hpp
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#ifndef PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
#define PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
/////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointSource, typename PointTarget, typename Scalar> double
pcl::registration::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 (size_t i = 0; i < cloud_src->size (); ++i)
{
const PointSource &src = cloud_src->points[i];
PointTarget &tgt = input_transformed.points[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);
}
std::vector<int> nn_indices (1);
std::vector<float> nn_dists (1);
// For each point in the source dataset
int nr = 0;
for (size_t i = 0; i < input_transformed.points.size (); ++i)
{
// Find its nearest neighbor in the target
tree_->nearestKSearch (input_transformed.points[i], 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);
else
return (std::numeric_limits<double>::max ());
}
#endif // PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation::ConstPtr
boost::shared_ptr< const MyPointRepresentation > ConstPtr
Definition: transformation_validation_euclidean.h:241
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:45
pcl::registration::TransformationValidationEuclidean::PointCloudTargetConstPtr
TransformationValidation< PointSource, PointTarget >::PointCloudTargetConstPtr PointCloudTargetConstPtr
Definition: transformation_validation_euclidean.h:88
pcl::registration::TransformationValidationEuclidean::Matrix4
TransformationValidation< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: transformation_validation_euclidean.h:77
pcl::PointCloud< PointSource >
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation
Internal point representation uses only 3D coordinates for L2.
Definition: transformation_validation_euclidean.h:235
pcl::PointCloud::resize
void resize(size_t n)
Resize the cloud.
Definition: point_cloud.h:455
pcl::registration::TransformationValidationEuclidean::PointCloudSourceConstPtr
TransformationValidation< PointSource, PointTarget >::PointCloudSourceConstPtr PointCloudSourceConstPtr
Definition: transformation_validation_euclidean.h:87