Point Cloud Library (PCL)  1.11.1-dev
sac_model_registration_2d.h
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38 
39 #pragma once
40 
41 #include <pcl/sample_consensus/sac_model_registration.h>
42 #include <pcl/memory.h>
43 #include <pcl/pcl_macros.h>
44 
45 namespace pcl
46 {
47  /** \brief SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection using distances between 2D pixels
48  * \author Radu B. Rusu
49  * \ingroup sample_consensus
50  */
51  template <typename PointT>
53  {
54  public:
65 
69 
70  using Ptr = shared_ptr<SampleConsensusModelRegistration2D<PointT> >;
71  using ConstPtr = shared_ptr<const SampleConsensusModelRegistration2D<PointT> >;
72 
73  /** \brief Constructor for base SampleConsensusModelRegistration2D.
74  * \param[in] cloud the input point cloud dataset
75  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
76  */
78  bool random = false)
79  : pcl::SampleConsensusModelRegistration<PointT> (cloud, random)
80  , projection_matrix_ (Eigen::Matrix3f::Identity ())
81  {
82  // Call our own setInputCloud
83  setInputCloud (cloud);
84  model_name_ = "SampleConsensusModelRegistration2D";
85  sample_size_ = 3;
86  model_size_ = 16;
87  }
88 
89  /** \brief Constructor for base SampleConsensusModelRegistration2D.
90  * \param[in] cloud the input point cloud dataset
91  * \param[in] indices a vector of point indices to be used from \a cloud
92  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
93  */
95  const Indices &indices,
96  bool random = false)
97  : pcl::SampleConsensusModelRegistration<PointT> (cloud, indices, random)
98  , projection_matrix_ (Eigen::Matrix3f::Identity ())
99  {
101  computeSampleDistanceThreshold (cloud, indices);
102  model_name_ = "SampleConsensusModelRegistration2D";
103  sample_size_ = 3;
104  model_size_ = 16;
105  }
106 
107  /** \brief Empty destructor */
109 
110  /** \brief Compute all distances from the transformed points to their correspondences
111  * \param[in] model_coefficients the 4x4 transformation matrix
112  * \param[out] distances the resultant estimated distances
113  */
114  void
115  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
116  std::vector<double> &distances) const;
117 
118  /** \brief Select all the points which respect the given model coefficients as inliers.
119  * \param[in] model_coefficients the 4x4 transformation matrix
120  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
121  * \param[out] inliers the resultant model inliers
122  */
123  void
124  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
125  const double threshold,
126  Indices &inliers);
127 
128  /** \brief Count all the points which respect the given model coefficients as inliers.
129  *
130  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
131  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
132  * \return the resultant number of inliers
133  */
134  virtual std::size_t
135  countWithinDistance (const Eigen::VectorXf &model_coefficients,
136  const double threshold) const;
137 
138  /** \brief Set the camera projection matrix.
139  * \param[in] projection_matrix the camera projection matrix
140  */
141  inline void
142  setProjectionMatrix (const Eigen::Matrix3f &projection_matrix)
143  { projection_matrix_ = projection_matrix; }
144 
145  /** \brief Get the camera projection matrix. */
146  inline Eigen::Matrix3f
148  { return (projection_matrix_); }
149 
150  protected:
153 
154  /** \brief Check if a sample of indices results in a good sample of points
155  * indices.
156  * \param[in] samples the resultant index samples
157  */
158  bool
159  isSampleGood (const Indices &samples) const;
160 
161  /** \brief Computes an "optimal" sample distance threshold based on the
162  * principal directions of the input cloud.
163  */
164  inline void
166  {
167  //// Compute the principal directions via PCA
168  //Eigen::Vector4f xyz_centroid;
169  //Eigen::Matrix3f covariance_matrix = Eigen::Matrix3f::Zero ();
170 
171  //computeMeanAndCovarianceMatrix (*cloud, covariance_matrix, xyz_centroid);
172 
173  //// Check if the covariance matrix is finite or not.
174  //for (int i = 0; i < 3; ++i)
175  // for (int j = 0; j < 3; ++j)
176  // if (!std::isfinite (covariance_matrix.coeffRef (i, j)))
177  // PCL_ERROR ("[pcl::SampleConsensusModelRegistration::computeSampleDistanceThreshold] Covariance matrix has NaN values! Is the input cloud finite?\n");
178 
179  //Eigen::Vector3f eigen_values;
180  //pcl::eigen33 (covariance_matrix, eigen_values);
181 
182  //// Compute the distance threshold for sample selection
183  //sample_dist_thresh_ = eigen_values.array ().sqrt ().sum () / 3.0;
184  //sample_dist_thresh_ *= sample_dist_thresh_;
185  //PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::setInputCloud] Estimated a sample selection distance threshold of: %f\n", sample_dist_thresh_);
186  }
187 
188  /** \brief Computes an "optimal" sample distance threshold based on the
189  * principal directions of the input cloud.
190  */
191  inline void
193  const Indices&)
194  {
195  //// Compute the principal directions via PCA
196  //Eigen::Vector4f xyz_centroid;
197  //Eigen::Matrix3f covariance_matrix;
198  //computeMeanAndCovarianceMatrix (*cloud, indices, covariance_matrix, xyz_centroid);
199 
200  //// Check if the covariance matrix is finite or not.
201  //for (int i = 0; i < 3; ++i)
202  // for (int j = 0; j < 3; ++j)
203  // if (!std::isfinite (covariance_matrix.coeffRef (i, j)))
204  // PCL_ERROR ("[pcl::SampleConsensusModelRegistration::computeSampleDistanceThreshold] Covariance matrix has NaN values! Is the input cloud finite?\n");
205 
206  //Eigen::Vector3f eigen_values;
207  //pcl::eigen33 (covariance_matrix, eigen_values);
208 
209  //// Compute the distance threshold for sample selection
210  //sample_dist_thresh_ = eigen_values.array ().sqrt ().sum () / 3.0;
211  //sample_dist_thresh_ *= sample_dist_thresh_;
212  //PCL_DEBUG ("[pcl::SampleConsensusModelRegistration::setInputCloud] Estimated a sample selection distance threshold of: %f\n", sample_dist_thresh_);
213  }
214 
215  private:
216  /** \brief Camera projection matrix. */
217  Eigen::Matrix3f projection_matrix_;
218 
219  public:
221  };
222 }
223 
224 #include <pcl/sample_consensus/impl/sac_model_registration_2d.hpp>
pcl_macros.h
Defines all the PCL and non-PCL macros used.
pcl
Definition: convolution.h:46
Eigen
Definition: bfgs.h:9
pcl::SampleConsensusModelRegistration2D
SampleConsensusModelRegistration2D defines a model for Point-To-Point registration outlier rejection ...
Definition: sac_model_registration_2d.h:52
pcl::SampleConsensusModelRegistration2D::countWithinDistance
virtual std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_registration_2d.hpp:181
pcl::SampleConsensusModelRegistration::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition: sac_model_registration.h:120
pcl::SampleConsensusModelRegistration2D::getProjectionMatrix
Eigen::Matrix3f getProjectionMatrix() const
Get the camera projection matrix.
Definition: sac_model_registration_2d.h:147
pcl::SampleConsensusModelRegistration2D::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const
Compute all distances from the transformed points to their correspondences.
Definition: sac_model_registration_2d.hpp:67
pcl::SampleConsensusModelRegistration2D::computeSampleDistanceThreshold
void computeSampleDistanceThreshold(const PointCloudConstPtr &, const Indices &)
Computes an "optimal" sample distance threshold based on the principal directions of the input cloud.
Definition: sac_model_registration_2d.h:192
pcl::SampleConsensusModel::sample_size_
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:588
pcl::SampleConsensusModel::model_size_
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:591
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: distances.h:55
pcl::SampleConsensusModelRegistration2D::isSampleGood
bool isSampleGood(const Indices &samples) const
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_registration_2d.hpp:45
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:628
pcl::SampleConsensusModelRegistration2D::SampleConsensusModelRegistration2D
SampleConsensusModelRegistration2D(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelRegistration2D.
Definition: sac_model_registration_2d.h:77
pcl::SampleConsensusModel::ConstPtr
shared_ptr< const SampleConsensusModel< PointT > > ConstPtr
Definition: sac_model.h:78
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
pcl::SampleConsensusModelRegistration::computeOriginalIndexMapping
void computeOriginalIndexMapping()
Compute mappings between original indices of the input_/target_ clouds.
Definition: sac_model_registration.h:306
pcl::SampleConsensusModel::Ptr
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
pcl::SampleConsensusModel::model_name_
std::string model_name_
The model name.
Definition: sac_model.h:550
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:141
pcl::SampleConsensusModel::PointCloudConstPtr
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:73
pcl::SampleConsensusModelRegistration2D::~SampleConsensusModelRegistration2D
virtual ~SampleConsensusModelRegistration2D()
Empty destructor.
Definition: sac_model_registration_2d.h:108
pcl::SampleConsensusModelRegistration2D::SampleConsensusModelRegistration2D
SampleConsensusModelRegistration2D(const PointCloudConstPtr &cloud, const Indices &indices, bool random=false)
Constructor for base SampleConsensusModelRegistration2D.
Definition: sac_model_registration_2d.h:94
pcl::SampleConsensusModelRegistration2D::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_registration_2d.hpp:121
pcl::SampleConsensusModelRegistration2D::setProjectionMatrix
void setProjectionMatrix(const Eigen::Matrix3f &projection_matrix)
Set the camera projection matrix.
Definition: sac_model_registration_2d.h:142
pcl::SampleConsensusModel::PointCloudPtr
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:74
pcl::SampleConsensusModel
SampleConsensusModel represents the base model class.
Definition: sac_model.h:69
pcl::SampleConsensusModelRegistration
SampleConsensusModelRegistration defines a model for Point-To-Point registration outlier rejection.
Definition: sac_model_registration.h:61
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl::SampleConsensusModelRegistration2D::computeSampleDistanceThreshold
void computeSampleDistanceThreshold(const PointCloudConstPtr &)
Computes an "optimal" sample distance threshold based on the principal directions of the input cloud.
Definition: sac_model_registration_2d.h:165