Point Cloud Library (PCL) 1.12.1
fpfh.h
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40
41#pragma once
42
43#include <pcl/features/feature.h>
44
45namespace pcl
46{
47 /** \brief FPFHEstimation estimates the <b>Fast Point Feature Histogram (FPFH)</b> descriptor for a given point
48 * cloud dataset containing points and normals.
49 *
50 * A commonly used type for PointOutT is pcl::FPFHSignature33.
51 *
52 * \note If you use this code in any academic work, please cite:
53 *
54 * - R.B. Rusu, N. Blodow, M. Beetz.
55 * Fast Point Feature Histograms (FPFH) for 3D Registration.
56 * In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),
57 * Kobe, Japan, May 12-17 2009.
58 * - R.B. Rusu, A. Holzbach, N. Blodow, M. Beetz.
59 * Fast Geometric Point Labeling using Conditional Random Fields.
60 * In Proceedings of the 22nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
61 * St. Louis, MO, USA, October 11-15 2009.
62 *
63 * \attention
64 * The convention for FPFH features is:
65 * - if a query point's nearest neighbors cannot be estimated, the FPFH feature will be set to NaN
66 * (not a number)
67 * - it is impossible to estimate a FPFH descriptor for a point that
68 * doesn't have finite 3D coordinates. Therefore, any point that contains
69 * NaN data on x, y, or z, will have its FPFH feature property set to NaN.
70 *
71 * \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
72 * \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
73 *
74 * \author Radu B. Rusu
75 * \ingroup features
76 */
77 template <typename PointInT, typename PointNT, typename PointOutT = pcl::FPFHSignature33>
78 class FPFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
79 {
80 public:
81 using Ptr = shared_ptr<FPFHEstimation<PointInT, PointNT, PointOutT> >;
82 using ConstPtr = shared_ptr<const FPFHEstimation<PointInT, PointNT, PointOutT> >;
83 using Feature<PointInT, PointOutT>::feature_name_;
84 using Feature<PointInT, PointOutT>::getClassName;
85 using Feature<PointInT, PointOutT>::indices_;
86 using Feature<PointInT, PointOutT>::k_;
87 using Feature<PointInT, PointOutT>::search_parameter_;
88 using Feature<PointInT, PointOutT>::input_;
89 using Feature<PointInT, PointOutT>::surface_;
90 using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
91
93
94 /** \brief Empty constructor. */
96 nr_bins_f1_ (11), nr_bins_f2_ (11), nr_bins_f3_ (11),
97 d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI)))
98 {
99 feature_name_ = "FPFHEstimation";
100 };
101
102 /** \brief Compute the 4-tuple representation containing the three angles and one distance between two points
103 * represented by Cartesian coordinates and normals.
104 * \note For explanations about the features, please see the literature mentioned above (the order of the
105 * features might be different).
106 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
107 * \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud
108 * \param[in] p_idx the index of the first point (source)
109 * \param[in] q_idx the index of the second point (target)
110 * \param[out] f1 the first angular feature (angle between the projection of nq_idx and u)
111 * \param[out] f2 the second angular feature (angle between nq_idx and v)
112 * \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
113 * \param[out] f4 the distance feature (p_idx - q_idx)
114 */
115 bool
117 int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4);
118
119 /** \brief Estimate the SPFH (Simple Point Feature Histograms) individual signatures of the three angular
120 * (f1, f2, f3) features for a given point based on its spatial neighborhood of 3D points with normals
121 * \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
122 * \param[in] normals the dataset containing the surface normals at each point in \a cloud
123 * \param[in] p_idx the index of the query point (source)
124 * \param[in] row the index row in feature histogramms
125 * \param[in] indices the k-neighborhood point indices in the dataset
126 * \param[out] hist_f1 the resultant SPFH histogram for feature f1
127 * \param[out] hist_f2 the resultant SPFH histogram for feature f2
128 * \param[out] hist_f3 the resultant SPFH histogram for feature f3
129 */
130 void
132 const pcl::PointCloud<PointNT> &normals, pcl::index_t p_idx, int row,
133 const pcl::Indices &indices,
134 Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
135
136 /** \brief Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH
137 * (Fast Point Feature Histogram) for a given point based on its 3D spatial neighborhood
138 * \param[in] hist_f1 the histogram feature vector of \a f1 values over the given patch
139 * \param[in] hist_f2 the histogram feature vector of \a f2 values over the given patch
140 * \param[in] hist_f3 the histogram feature vector of \a f3 values over the given patch
141 * \param[in] indices the point indices of p_idx's k-neighborhood in the point cloud
142 * \param[in] dists the distances from p_idx to all its k-neighbors
143 * \param[out] fpfh_histogram the resultant FPFH histogram representing the feature at the query point
144 */
145 void
146 weightPointSPFHSignature (const Eigen::MatrixXf &hist_f1,
147 const Eigen::MatrixXf &hist_f2,
148 const Eigen::MatrixXf &hist_f3,
149 const pcl::Indices &indices,
150 const std::vector<float> &dists,
151 Eigen::VectorXf &fpfh_histogram);
152
153 /** \brief Set the number of subdivisions for each angular feature interval.
154 * \param[in] nr_bins_f1 number of subdivisions for the first angular feature
155 * \param[in] nr_bins_f2 number of subdivisions for the second angular feature
156 * \param[in] nr_bins_f3 number of subdivisions for the third angular feature
157 */
158 inline void
159 setNrSubdivisions (int nr_bins_f1, int nr_bins_f2, int nr_bins_f3)
160 {
161 nr_bins_f1_ = nr_bins_f1;
162 nr_bins_f2_ = nr_bins_f2;
163 nr_bins_f3_ = nr_bins_f3;
164 }
165
166 /** \brief Get the number of subdivisions for each angular feature interval.
167 * \param[out] nr_bins_f1 number of subdivisions for the first angular feature
168 * \param[out] nr_bins_f2 number of subdivisions for the second angular feature
169 * \param[out] nr_bins_f3 number of subdivisions for the third angular feature
170 */
171 inline void
172 getNrSubdivisions (int &nr_bins_f1, int &nr_bins_f2, int &nr_bins_f3)
173 {
174 nr_bins_f1 = nr_bins_f1_;
175 nr_bins_f2 = nr_bins_f2_;
176 nr_bins_f3 = nr_bins_f3_;
177 }
178
179 protected:
180
181 /** \brief Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud
182 * \param[out] spf_hist_lookup a lookup table for all the SPF feature indices
183 * \param[out] hist_f1 the resultant SPFH histogram for feature f1
184 * \param[out] hist_f2 the resultant SPFH histogram for feature f2
185 * \param[out] hist_f3 the resultant SPFH histogram for feature f3
186 */
187 void
188 computeSPFHSignatures (std::vector<int> &spf_hist_lookup,
189 Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
190
191 /** \brief Estimate the Fast Point Feature Histograms (FPFH) descriptors at a set of points given by
192 * <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
193 * setSearchMethod ()
194 * \param[out] output the resultant point cloud model dataset that contains the FPFH feature estimates
195 */
196 void
197 computeFeature (PointCloudOut &output) override;
198
199 /** \brief The number of subdivisions for each angular feature interval. */
201
202 /** \brief Placeholder for the f1 histogram. */
203 Eigen::MatrixXf hist_f1_;
204
205 /** \brief Placeholder for the f2 histogram. */
206 Eigen::MatrixXf hist_f2_;
207
208 /** \brief Placeholder for the f3 histogram. */
209 Eigen::MatrixXf hist_f3_;
210
211 /** \brief Placeholder for a point's FPFH signature. */
212 Eigen::VectorXf fpfh_histogram_;
213
214 /** \brief Float constant = 1.0 / (2.0 * M_PI) */
215 float d_pi_;
216 };
217}
218
219#ifdef PCL_NO_PRECOMPILE
220#include <pcl/features/impl/fpfh.hpp>
221#endif
FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud d...
Definition: fpfh.h:79
void getNrSubdivisions(int &nr_bins_f1, int &nr_bins_f2, int &nr_bins_f3)
Get the number of subdivisions for each angular feature interval.
Definition: fpfh.h:172
float d_pi_
Float constant = 1.0 / (2.0 * M_PI)
Definition: fpfh.h:215
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: fpfh.h:92
void computeSPFHSignatures(std::vector< int > &spf_hist_lookup, Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud.
Definition: fpfh.hpp:182
Eigen::MatrixXf hist_f3_
Placeholder for the f3 histogram.
Definition: fpfh.h:209
void computePointSPFHSignature(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, pcl::index_t p_idx, int row, const pcl::Indices &indices, Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3)
Estimate the SPFH (Simple Point Feature Histograms) individual signatures of the three angular (f1,...
Definition: fpfh.hpp:64
Eigen::MatrixXf hist_f2_
Placeholder for the f2 histogram.
Definition: fpfh.h:206
void computeFeature(PointCloudOut &output) override
Estimate the Fast Point Feature Histograms (FPFH) descriptors at a set of points given by <setInputCl...
Definition: fpfh.hpp:238
Eigen::MatrixXf hist_f1_
Placeholder for the f1 histogram.
Definition: fpfh.h:203
Eigen::VectorXf fpfh_histogram_
Placeholder for a point's FPFH signature.
Definition: fpfh.h:212
bool computePairFeatures(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
Definition: fpfh.hpp:52
FPFHEstimation()
Empty constructor.
Definition: fpfh.h:95
shared_ptr< FPFHEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition: fpfh.h:81
void setNrSubdivisions(int nr_bins_f1, int nr_bins_f2, int nr_bins_f3)
Set the number of subdivisions for each angular feature interval.
Definition: fpfh.h:159
int nr_bins_f1_
The number of subdivisions for each angular feature interval.
Definition: fpfh.h:200
shared_ptr< const FPFHEstimation< PointInT, PointNT, PointOutT > > ConstPtr
Definition: fpfh.h:82
void weightPointSPFHSignature(const Eigen::MatrixXf &hist_f1, const Eigen::MatrixXf &hist_f2, const Eigen::MatrixXf &hist_f3, const pcl::Indices &indices, const std::vector< float > &dists, Eigen::VectorXf &fpfh_histogram)
Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH (Fas...
Definition: fpfh.hpp:110
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition: feature.h:355
Feature represents the base feature class.
Definition: feature.h:107
double search_parameter_
The actual search parameter (from either search_radius_ or k_).
Definition: feature.h:237
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition: feature.h:247
int k_
The number of K nearest neighbors to use for each point.
Definition: feature.h:243
std::string feature_name_
The feature name.
Definition: feature.h:223
PointCloudInConstPtr surface_
An input point cloud describing the surface that is to be used for nearest neighbors estimation.
Definition: feature.h:231
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: pcl_base.h:150
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
#define M_PI
Definition: pcl_macros.h:201