Point Cloud Library (PCL)  1.11.1-dev
transformation_validation_euclidean.hpp
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40 
41 #ifndef PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
42 #define PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
43 
44 namespace pcl {
45 
46 namespace registration {
47 
48 template <typename PointSource, typename PointTarget, typename Scalar>
49 double
52  const PointCloudTargetConstPtr& cloud_tgt,
53  const Matrix4& transformation_matrix) const
54 {
55  double fitness_score = 0.0;
56 
57  // Transform the input dataset using the final transformation
58  pcl::PointCloud<PointSource> input_transformed;
59  // transformPointCloud (*cloud_src, input_transformed, transformation_matrix);
60  input_transformed.resize(cloud_src->size());
61  for (std::size_t i = 0; i < cloud_src->size(); ++i) {
62  const PointSource& src = (*cloud_src)[i];
63  PointTarget& tgt = input_transformed[i];
64  tgt.x = static_cast<float>(
65  transformation_matrix(0, 0) * src.x + transformation_matrix(0, 1) * src.y +
66  transformation_matrix(0, 2) * src.z + transformation_matrix(0, 3));
67  tgt.y = static_cast<float>(
68  transformation_matrix(1, 0) * src.x + transformation_matrix(1, 1) * src.y +
69  transformation_matrix(1, 2) * src.z + transformation_matrix(1, 3));
70  tgt.z = static_cast<float>(
71  transformation_matrix(2, 0) * src.x + transformation_matrix(2, 1) * src.y +
72  transformation_matrix(2, 2) * src.z + transformation_matrix(2, 3));
73  }
74 
76  if (!force_no_recompute_) {
77  tree_->setPointRepresentation(point_rep);
78  tree_->setInputCloud(cloud_tgt);
79  }
80 
81  std::vector<int> nn_indices(1);
82  std::vector<float> nn_dists(1);
83 
84  // For each point in the source dataset
85  int nr = 0;
86  for (const auto& point : input_transformed) {
87  // Find its nearest neighbor in the target
88  tree_->nearestKSearch(point, 1, nn_indices, nn_dists);
89 
90  // Deal with occlusions (incomplete targets)
91  if (nn_dists[0] > max_range_)
92  continue;
93 
94  // Calculate the fitness score
95  fitness_score += nn_dists[0];
96  ++nr;
97  }
98 
99  if (nr > 0)
100  return (fitness_score / nr);
101  return (std::numeric_limits<double>::max());
102 }
103 
104 } // namespace registration
105 } // namespace pcl
106 
107 #endif // PCL_REGISTRATION_TRANSFORMATION_VALIDATION_EUCLIDEAN_IMPL_H_
pcl
Definition: convolution.h:46
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::Matrix4
typename TransformationValidation< PointSource, PointTarget, Scalar >::Matrix4 Matrix4
Definition: transformation_validation_euclidean.h:78
pcl::PointCloud< PointSource >
pcl::registration::TransformationValidationEuclidean::PointCloudTargetConstPtr
typename TransformationValidation< PointSource, PointTarget >::PointCloudTargetConstPtr PointCloudTargetConstPtr
Definition: transformation_validation_euclidean.h:94
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:456
pcl::registration::TransformationValidationEuclidean::PointCloudSourceConstPtr
typename TransformationValidation< PointSource, PointTarget >::PointCloudSourceConstPtr PointCloudSourceConstPtr
Definition: transformation_validation_euclidean.h:91
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation::ConstPtr
shared_ptr< const MyPointRepresentation > ConstPtr
Definition: transformation_validation_euclidean.h:241
pcl::registration::TransformationValidationEuclidean::MyPointRepresentation
Internal point representation uses only 3D coordinates for L2.
Definition: transformation_validation_euclidean.h:235