Point Cloud Library (PCL) 1.12.0
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sac_model_normal_sphere.hpp
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37 * $Id: sac_model_normal_sphere.hpp schrandt $
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40
41#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_
42#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_
43
44#include <pcl/sample_consensus/sac_model_normal_sphere.h>
45#include <pcl/common/common.h> // for getAngle3D
46
47//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
48template <typename PointT, typename PointNT> void
50 const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
51{
52 if (!normals_)
53 {
54 PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::selectWithinDistance] No input dataset containing normals was given!\n");
55 inliers.clear ();
56 return;
57 }
58
59 // Check if the model is valid given the user constraints
60 if (!isModelValid (model_coefficients))
61 {
62 inliers.clear ();
63 return;
64 }
65
66 // Obtain the sphere center
67 Eigen::Vector4f center = model_coefficients;
68 center[3] = 0.0f;
69
70 inliers.clear ();
71 error_sqr_dists_.clear ();
72 inliers.reserve (indices_->size ());
73 error_sqr_dists_.reserve (indices_->size ());
74
75 // Iterate through the 3d points and calculate the distances from them to the sphere
76 for (std::size_t i = 0; i < indices_->size (); ++i)
77 {
78 // Calculate the distance from the point to the sphere center as the difference between
79 // dist(point,sphere_origin) and sphere_radius
80 Eigen::Vector4f p ((*input_)[(*indices_)[i]].x,
81 (*input_)[(*indices_)[i]].y,
82 (*input_)[(*indices_)[i]].z,
83 0.0f);
84
85 Eigen::Vector4f n_dir = p - center;
86 const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
87 if (weighted_euclid_dist > threshold) // Early termination: cannot be an inlier
88 continue;
89
90 // Calculate the angular distance between the point normal and the sphere normal
91 Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0],
92 (*normals_)[(*indices_)[i]].normal[1],
93 (*normals_)[(*indices_)[i]].normal[2],
94 0.0f);
95 double d_normal = std::abs (getAngle3D (n, n_dir));
96 d_normal = (std::min) (d_normal, M_PI - d_normal);
97
98 double distance = std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist);
99 if (distance < threshold)
100 {
101 // Returns the indices of the points whose distances are smaller than the threshold
102 inliers.push_back ((*indices_)[i]);
103 error_sqr_dists_.push_back (static_cast<double> (distance));
104 }
105 }
106}
107
108//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
109template <typename PointT, typename PointNT> std::size_t
111 const Eigen::VectorXf &model_coefficients, const double threshold) const
112{
113 if (!normals_)
114 {
115 PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::getDistancesToModel] No input dataset containing normals was given!\n");
116 return (0);
117 }
118
119 // Check if the model is valid given the user constraints
120 if (!isModelValid (model_coefficients))
121 return(0);
122
123
124 // Obtain the sphere centroid
125 Eigen::Vector4f center = model_coefficients;
126 center[3] = 0.0f;
127
128 std::size_t nr_p = 0;
129
130 // Iterate through the 3d points and calculate the distances from them to the sphere
131 for (std::size_t i = 0; i < indices_->size (); ++i)
132 {
133 // Calculate the distance from the point to the sphere centroid as the difference between
134 // dist(point,sphere_origin) and sphere_radius
135 Eigen::Vector4f p ((*input_)[(*indices_)[i]].x,
136 (*input_)[(*indices_)[i]].y,
137 (*input_)[(*indices_)[i]].z,
138 0.0f);
139
140 Eigen::Vector4f n_dir = (p-center);
141 const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
142 if (weighted_euclid_dist > threshold) // Early termination: cannot be an inlier
143 continue;
144
145 // Calculate the angular distance between the point normal and the sphere normal
146 Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0],
147 (*normals_)[(*indices_)[i]].normal[1],
148 (*normals_)[(*indices_)[i]].normal[2],
149 0.0f);
150 double d_normal = std::abs (getAngle3D (n, n_dir));
151 d_normal = (std::min) (d_normal, M_PI - d_normal);
152
153 if (std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist) < threshold)
154 nr_p++;
155 }
156 return (nr_p);
157}
158
159//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
160template <typename PointT, typename PointNT> void
162 const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
163{
164 if (!normals_)
165 {
166 PCL_ERROR ("[pcl::SampleConsensusModelNormalSphere::getDistancesToModel] No input dataset containing normals was given!\n");
167 return;
168 }
169
170 // Check if the model is valid given the user constraints
171 if (!isModelValid (model_coefficients))
172 {
173 distances.clear ();
174 return;
175 }
176
177 // Obtain the sphere centroid
178 Eigen::Vector4f center = model_coefficients;
179 center[3] = 0.0f;
180
181 distances.resize (indices_->size ());
182
183 // Iterate through the 3d points and calculate the distances from them to the sphere
184 for (std::size_t i = 0; i < indices_->size (); ++i)
185 {
186 // Calculate the distance from the point to the sphere as the difference between
187 // dist(point,sphere_origin) and sphere_radius
188 Eigen::Vector4f p ((*input_)[(*indices_)[i]].x,
189 (*input_)[(*indices_)[i]].y,
190 (*input_)[(*indices_)[i]].z,
191 0.0f);
192
193 Eigen::Vector4f n_dir = (p-center);
194 const double weighted_euclid_dist = (1.0 - normal_distance_weight_) * std::abs (n_dir.norm () - model_coefficients[3]);
195
196 // Calculate the angular distance between the point normal and the sphere normal
197 Eigen::Vector4f n ((*normals_)[(*indices_)[i]].normal[0],
198 (*normals_)[(*indices_)[i]].normal[1],
199 (*normals_)[(*indices_)[i]].normal[2],
200 0.0f);
201 double d_normal = std::abs (getAngle3D (n, n_dir));
202 d_normal = (std::min) (d_normal, M_PI - d_normal);
203
204 distances[i] = std::abs (normal_distance_weight_ * d_normal + weighted_euclid_dist);
205 }
206}
207
208#define PCL_INSTANTIATE_SampleConsensusModelNormalSphere(PointT, PointNT) template class PCL_EXPORTS pcl::SampleConsensusModelNormalSphere<PointT, PointNT>;
209
210#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_NORMAL_SPHERE_H_
211
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given sphere model.
Define standard C methods and C++ classes that are common to all methods.
double getAngle3D(const Eigen::Vector4f &v1, const Eigen::Vector4f &v2, const bool in_degree=false)
Compute the smallest angle between two 3D vectors in radians (default) or degree.
Definition common.hpp:47
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
#define M_PI
Definition pcl_macros.h:201