Point Cloud Library (PCL) 1.12.1
cvfh.h
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40
41#pragma once
42
43#include <pcl/features/feature.h>
44#include <pcl/features/vfh.h>
45#include <pcl/search/search.h> // for Search
46
47namespace pcl
48{
49 /** \brief CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given
50 * point cloud dataset containing XYZ data and normals, as presented in:
51 * - CAD-Model Recognition and 6 DOF Pose Estimation
52 * A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski
53 * ICCV 2011, 3D Representation and Recognition (3dRR11) workshop
54 * Barcelona, Spain, (2011)
55 *
56 * The suggested PointOutT is pcl::VFHSignature308.
57 *
58 * \author Aitor Aldoma
59 * \ingroup features
60 */
61 template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308>
62 class CVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
63 {
64 public:
65 using Ptr = shared_ptr<CVFHEstimation<PointInT, PointNT, PointOutT> >;
66 using ConstPtr = shared_ptr<const CVFHEstimation<PointInT, PointNT, PointOutT> >;
67
75
79
80 /** \brief Empty constructor. */
82 vpx_ (0), vpy_ (0), vpz_ (0),
83 leaf_size_ (0.005f),
84 normalize_bins_ (false),
85 curv_threshold_ (0.03f),
86 cluster_tolerance_ (leaf_size_ * 3),
87 eps_angle_threshold_ (0.125f),
88 min_points_ (50),
89 radius_normals_ (leaf_size_ * 3)
90 {
92 k_ = 1;
93 feature_name_ = "CVFHEstimation";
94 }
95 ;
96
97 /** \brief Removes normals with high curvature caused by real edges or noisy data
98 * \param[in] cloud pointcloud to be filtered
99 * \param[in] indices_to_use the indices to use
100 * \param[out] indices_out the indices of the points with higher curvature than threshold
101 * \param[out] indices_in the indices of the remaining points after filtering
102 * \param[in] threshold threshold value for curvature
103 */
104 void
105 filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud, pcl::Indices & indices_to_use, pcl::Indices &indices_out,
106 pcl::Indices &indices_in, float threshold);
107
108 /** \brief Set the viewpoint.
109 * \param[in] vpx the X coordinate of the viewpoint
110 * \param[in] vpy the Y coordinate of the viewpoint
111 * \param[in] vpz the Z coordinate of the viewpoint
112 */
113 inline void
114 setViewPoint (float vpx, float vpy, float vpz)
115 {
116 vpx_ = vpx;
117 vpy_ = vpy;
118 vpz_ = vpz;
119 }
120
121 /** \brief Set the radius used to compute normals
122 * \param[in] radius_normals the radius
123 */
124 inline void
125 setRadiusNormals (float radius_normals)
126 {
127 radius_normals_ = radius_normals;
128 }
129
130 /** \brief Get the viewpoint.
131 * \param[out] vpx the X coordinate of the viewpoint
132 * \param[out] vpy the Y coordinate of the viewpoint
133 * \param[out] vpz the Z coordinate of the viewpoint
134 */
135 inline void
136 getViewPoint (float &vpx, float &vpy, float &vpz)
137 {
138 vpx = vpx_;
139 vpy = vpy_;
140 vpz = vpz_;
141 }
142
143 /** \brief Get the centroids used to compute different CVFH descriptors
144 * \param[out] centroids vector to hold the centroids
145 */
146 inline void
147 getCentroidClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
148 {
149 centroids.insert (centroids.cend (), centroids_dominant_orientations_.cbegin (),
151 }
152
153 /** \brief Get the normal centroids used to compute different CVFH descriptors
154 * \param[out] centroids vector to hold the normal centroids
155 */
156 inline void
157 getCentroidNormalClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
158 {
159 for (const auto& normal: dominant_normals_)
160 centroids.push_back (normal);
161 }
162
163 /** \brief Sets max. Euclidean distance between points to be added to the cluster
164 * \param[in] d the maximum Euclidean distance
165 */
166
167 inline void
169 {
170 cluster_tolerance_ = d;
171 }
172
173 /** \brief Sets max. deviation of the normals between two points so they can be clustered together
174 * \param[in] d the maximum deviation
175 */
176 inline void
178 {
179 eps_angle_threshold_ = d;
180 }
181
182 /** \brief Sets curvature threshold for removing normals
183 * \param[in] d the curvature threshold
184 */
185 inline void
187 {
188 curv_threshold_ = d;
189 }
190
191 /** \brief Set minimum amount of points for a cluster to be considered
192 * \param[in] min the minimum amount of points to be set
193 */
194 inline void
195 setMinPoints (std::size_t min)
196 {
197 min_points_ = min;
198 }
199
200 /** \brief Sets whether if the CVFH signatures should be normalized or not
201 * \param[in] normalize true if normalization is required, false otherwise
202 */
203 inline void
204 setNormalizeBins (bool normalize)
205 {
206 normalize_bins_ = normalize;
207 }
208
209 /** \brief Overloaded computed method from pcl::Feature.
210 * \param[out] output the resultant point cloud model dataset containing the estimated features
211 */
212 void
213 compute (PointCloudOut &output);
214
215 private:
216 /** \brief Values describing the viewpoint ("pinhole" camera model assumed).
217 * By default, the viewpoint is set to 0,0,0.
218 */
219 float vpx_, vpy_, vpz_;
220
221 /** \brief Size of the voxels after voxel gridding. IMPORTANT: Must match the voxel
222 * size of the training data or the normalize_bins_ flag must be set to true.
223 */
224 float leaf_size_;
225
226 /** \brief Whether to normalize the signatures or not. Default: false. */
227 bool normalize_bins_;
228
229 /** \brief Curvature threshold for removing normals. */
230 float curv_threshold_;
231
232 /** \brief allowed Euclidean distance between points to be added to the cluster. */
233 float cluster_tolerance_;
234
235 /** \brief deviation of the normals between two points so they can be clustered together. */
236 float eps_angle_threshold_;
237
238 /** \brief Minimum amount of points in a clustered region to be considered stable for CVFH
239 * computation.
240 */
241 std::size_t min_points_;
242
243 /** \brief Radius for the normals computation. */
244 float radius_normals_;
245
246 /** \brief Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at
247 * a set of points given by <setInputCloud (), setIndices ()> using the surface in
248 * setSearchSurface ()
249 *
250 * \param[out] output the resultant point cloud model dataset that contains the CVFH
251 * feature estimates
252 */
253 void
254 computeFeature (PointCloudOut &output) override;
255
256 /** \brief Region growing method using Euclidean distances and neighbors normals to
257 * add points to a region.
258 * \param[in] cloud point cloud to split into regions
259 * \param[in] normals are the normals of cloud
260 * \param[in] tolerance is the allowed Euclidean distance between points to be added to
261 * the cluster
262 * \param[in] tree is the spatial search structure for nearest neighbour search
263 * \param[out] clusters vector of indices representing the clustered regions
264 * \param[in] eps_angle deviation of the normals between two points so they can be
265 * clustered together
266 * \param[in] min_pts_per_cluster minimum cluster size. (default: 1 point)
267 * \param[in] max_pts_per_cluster maximum cluster size. (default: all the points)
268 */
269 void
270 extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
271 const pcl::PointCloud<pcl::PointNormal> &normals, float tolerance,
273 std::vector<pcl::PointIndices> &clusters, double eps_angle,
274 unsigned int min_pts_per_cluster = 1,
275 unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
276
277 protected:
278 /** \brief Centroids that were used to compute different CVFH descriptors */
279 std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > centroids_dominant_orientations_;
280 /** \brief Normal centroids that were used to compute different CVFH descriptors */
281 std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > dominant_normals_;
282 };
283}
284
285#ifdef PCL_NO_PRECOMPILE
286#include <pcl/features/impl/cvfh.hpp>
287#endif
CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given poin...
Definition: cvfh.h:63
std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > centroids_dominant_orientations_
Centroids that were used to compute different CVFH descriptors.
Definition: cvfh.h:279
void setEPSAngleThreshold(float d)
Sets max.
Definition: cvfh.h:177
shared_ptr< CVFHEstimation< PointInT, PointNT, PointOutT > > Ptr
Definition: cvfh.h:65
void setMinPoints(std::size_t min)
Set minimum amount of points for a cluster to be considered.
Definition: cvfh.h:195
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: cvfh.h:76
shared_ptr< const CVFHEstimation< PointInT, PointNT, PointOutT > > ConstPtr
Definition: cvfh.h:66
std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > dominant_normals_
Normal centroids that were used to compute different CVFH descriptors.
Definition: cvfh.h:281
void getCentroidNormalClusters(std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > &centroids)
Get the normal centroids used to compute different CVFH descriptors.
Definition: cvfh.h:157
void setNormalizeBins(bool normalize)
Sets whether if the CVFH signatures should be normalized or not.
Definition: cvfh.h:204
void setCurvatureThreshold(float d)
Sets curvature threshold for removing normals.
Definition: cvfh.h:186
void setRadiusNormals(float radius_normals)
Set the radius used to compute normals.
Definition: cvfh.h:125
void filterNormalsWithHighCurvature(const pcl::PointCloud< PointNT > &cloud, pcl::Indices &indices_to_use, pcl::Indices &indices_out, pcl::Indices &indices_in, float threshold)
Removes normals with high curvature caused by real edges or noisy data.
Definition: cvfh.hpp:160
void setViewPoint(float vpx, float vpy, float vpz)
Set the viewpoint.
Definition: cvfh.h:114
void getCentroidClusters(std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > &centroids)
Get the centroids used to compute different CVFH descriptors.
Definition: cvfh.h:147
typename pcl::search::Search< PointNormal >::Ptr KdTreePtr
Definition: cvfh.h:77
void setClusterTolerance(float d)
Sets max.
Definition: cvfh.h:168
void getViewPoint(float &vpx, float &vpy, float &vpz)
Get the viewpoint.
Definition: cvfh.h:136
CVFHEstimation()
Empty constructor.
Definition: cvfh.h:81
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: cvfh.hpp:50
Feature represents the base feature class.
Definition: feature.h:107
double search_radius_
The nearest neighbors search radius for each point.
Definition: feature.h:240
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
VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...
Definition: vfh.h:73
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:81
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133