Point Cloud Library (PCL) 1.13.1
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vfh.hpp
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40
41#ifndef PCL_FEATURES_IMPL_VFH_H_
42#define PCL_FEATURES_IMPL_VFH_H_
43
44#include <pcl/features/vfh.h>
45#include <pcl/features/pfh_tools.h>
46#include <pcl/common/common.h>
47#include <pcl/common/centroid.h>
48
49//////////////////////////////////////////////////////////////////////////////////////////////
50template<typename PointInT, typename PointNT, typename PointOutT> bool
52{
53 if (input_->size () < 2 || (surface_ && surface_->size () < 2))
54 {
55 PCL_ERROR ("[pcl::VFHEstimation::initCompute] Input dataset must have at least 2 points!\n");
56 return (false);
57 }
58 if (search_radius_ == 0 && k_ == 0)
59 k_ = 1;
61}
62
63//////////////////////////////////////////////////////////////////////////////////////////////
64template<typename PointInT, typename PointNT, typename PointOutT> void
66{
67 if (!initCompute ())
68 {
69 output.width = output.height = 0;
70 output.clear ();
71 return;
72 }
73 // Copy the header
74 output.header = input_->header;
75
76 // Resize the output dataset
77 // Important! We should only allocate precisely how many elements we will need, otherwise
78 // we risk at pre-allocating too much memory which could lead to bad_alloc
79 // (see http://dev.pointclouds.org/issues/657)
80 output.width = output.height = 1;
81 output.is_dense = input_->is_dense;
82 output.resize (1);
83
84 // Perform the actual feature computation
85 computeFeature (output);
86
88}
89
90//////////////////////////////////////////////////////////////////////////////////////////////
91template<typename PointInT, typename PointNT, typename PointOutT> void
93 const Eigen::Vector4f &centroid_n,
94 const pcl::PointCloud<PointInT> &cloud,
95 const pcl::PointCloud<PointNT> &normals,
96 const pcl::Indices &indices)
97{
98 Eigen::Vector4f pfh_tuple;
99 // Reset the whole thing
100 for (int i = 0; i < 4; ++i)
101 {
102 hist_f_[i].setZero (nr_bins_f_[i]);
103 }
104
105 // Get the bounding box of the current cluster
106 //Eigen::Vector4f min_pt, max_pt;
107 //pcl::getMinMax3D (cloud, indices, min_pt, max_pt);
108 //double distance_normalization_factor = (std::max)((centroid_p - min_pt).norm (), (centroid_p - max_pt).norm ());
109
110 //Instead of using the bounding box to normalize the VFH distance component, it is better to use the max_distance
111 //from any point to centroid. VFH is invariant to rotation about the roll axis but the bounding box is not,
112 //resulting in different normalization factors for point clouds that are just rotated about that axis.
113
114 double distance_normalization_factor = 1.0;
115 if (normalize_distances_)
116 {
117 Eigen::Vector4f max_pt;
118 pcl::getMaxDistance (cloud, indices, centroid_p, max_pt);
119 max_pt[3] = 0;
120 distance_normalization_factor = (centroid_p - max_pt).norm ();
121 }
122
123 // Factorization constant
124 float hist_incr = 1;
125 if (normalize_bins_)
126 hist_incr = 100.0f / static_cast<float> (indices.size () - 1);
127
128 float hist_incr_size_component = 0;
129 if (size_component_)
130 hist_incr_size_component = hist_incr;
131
132 // Iterate over all the points in the neighborhood
133 for (const auto &index : indices)
134 {
135 // Compute the pair P to NNi
136 if (!computePairFeatures (centroid_p, centroid_n, cloud[index].getVector4fMap (),
137 normals[index].getNormalVector4fMap (), pfh_tuple[0], pfh_tuple[1],
138 pfh_tuple[2], pfh_tuple[3]))
139 continue;
140
141 // Normalize the f1, f2, f3, f4 features and push them in the histogram
142 for (int i = 0; i < 3; ++i)
143 {
144 const int raw_index = static_cast<int> (std::floor (nr_bins_f_[i] * ((pfh_tuple[i] + M_PI) * d_pi_)));
145 const int h_index = std::max(std::min(raw_index, nr_bins_f_[i] - 1), 0);
146 hist_f_[i] (h_index) += hist_incr;
147 }
148
149 if (hist_incr_size_component)
150 {
151 int h_index;
152 if (normalize_distances_)
153 h_index = static_cast<int> (std::floor (nr_bins_f_[3] * (pfh_tuple[3] / distance_normalization_factor)));
154 else
155 h_index = static_cast<int> (pcl_round (pfh_tuple[3] * 100));
156
157 h_index = std::max (std::min (h_index, nr_bins_f_[3] - 1), 0);
158 hist_f_[3] (h_index) += hist_incr_size_component;
159 }
160 }
161}
162//////////////////////////////////////////////////////////////////////////////////////////////
163template <typename PointInT, typename PointNT, typename PointOutT> void
165{
166 // ---[ Step 1a : compute the centroid in XYZ space
167 Eigen::Vector4f xyz_centroid (0, 0, 0, 0);
168
169 if (use_given_centroid_)
170 xyz_centroid = centroid_to_use_;
171 else
172 compute3DCentroid (*surface_, *indices_, xyz_centroid); // Estimate the XYZ centroid
173
174 // ---[ Step 1b : compute the centroid in normal space
175 Eigen::Vector4f normal_centroid = Eigen::Vector4f::Zero ();
176
177 // If the data is dense, we don't need to check for NaN
178 if (use_given_normal_)
179 normal_centroid = normal_to_use_;
180 else
181 {
182 std::size_t cp = 0;
183 if (normals_->is_dense)
184 {
185 for (const auto& index: *indices_)
186 {
187 normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
188 }
189 cp = indices_->size();
190 }
191 // NaN or Inf values could exist => check for them
192 else
193 {
194 for (const auto& index: *indices_)
195 {
196 if (!std::isfinite ((*normals_)[index].normal[0]) ||
197 !std::isfinite ((*normals_)[index].normal[1]) ||
198 !std::isfinite ((*normals_)[index].normal[2]))
199 continue;
200 normal_centroid.noalias () += (*normals_)[index].getNormalVector4fMap ();
201 cp++;
202 }
203 }
204 normal_centroid /= static_cast<float> (cp);
205 }
206
207 // Compute the direction of view from the viewpoint to the centroid
208 Eigen::Vector4f viewpoint (vpx_, vpy_, vpz_, 0);
209 Eigen::Vector4f d_vp_p = viewpoint - xyz_centroid;
210 d_vp_p.normalize ();
211
212 // Estimate the SPFH at nn_indices[0] using the entire cloud
213 computePointSPFHSignature (xyz_centroid, normal_centroid, *surface_, *normals_, *indices_);
214
215 // ---[ Step 2 : obtain the viewpoint component
216 hist_vp_.setZero (nr_bins_vp_);
217
218 float hist_incr = 1.0;
219 if (normalize_bins_)
220 hist_incr = 100.0 / static_cast<double> (indices_->size ());
221
222 for (const auto& index: *indices_)
223 {
224 Eigen::Vector4f normal ((*normals_)[index].normal[0],
225 (*normals_)[index].normal[1],
226 (*normals_)[index].normal[2], 0);
227 // Normalize
228 double alpha = (normal.dot (d_vp_p) + 1.0) * 0.5;
229 auto fi = static_cast<std::size_t> (std::floor (alpha * hist_vp_.size ()));
230 fi = std::max<std::size_t> (0u, fi);
231 fi = std::min<std::size_t> (hist_vp_.size () - 1, fi);
232 // Bin into the histogram
233 hist_vp_ [fi] += hist_incr;
234 }
235
236 // We only output _1_ signature
237 output.resize (1);
238 output.width = 1;
239 output.height = 1;
240
241 // Estimate the FPFH at nn_indices[0] using the entire cloud and copy the resultant signature
242 auto outPtr = std::begin (output[0].histogram);
243
244 for (int i = 0; i < 4; ++i)
245 {
246 outPtr = std::copy (hist_f_[i].data (), hist_f_[i].data () + hist_f_[i].size (), outPtr);
247 }
248 outPtr = std::copy (hist_vp_.data (), hist_vp_.data () + hist_vp_.size (), outPtr);
249}
250
251#define PCL_INSTANTIATE_VFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::VFHEstimation<T,NT,OutT>;
252
253#endif // PCL_FEATURES_IMPL_VFH_H_
Define methods for centroid estimation and covariance matrix calculus.
Feature represents the base feature class.
Definition feature.h:107
PointCloud represents the base class in PCL for storing collections of 3D points.
VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud data...
Definition vfh.h:73
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition vfh.h:84
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition vfh.hpp:65
bool initCompute() override
This method should get called before starting the actual computation.
Definition vfh.hpp:51
void computePointSPFHSignature(const Eigen::Vector4f &centroid_p, const Eigen::Vector4f &centroid_n, const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices)
Estimate the SPFH (Simple Point Feature Histograms) signatures of the angular (f1,...
Definition vfh.hpp:92
Define standard C methods and C++ classes that are common to all methods.
void getMaxDistance(const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
Get the point at maximum distance from a given point and a given pointcloud.
Definition common.hpp:197
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition centroid.hpp:56
PCL_EXPORTS bool computePairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
int cp(int from, int to)
Returns field copy operation code.
Definition repacks.hpp:56
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
__inline double pcl_round(double number)
Win32 doesn't seem to have rounding functions.
Definition pcl_macros.h:239
#define M_PI
Definition pcl_macros.h:201