Point Cloud Library (PCL)  1.11.1-dev
distances.h
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40 
41 #pragma once
42 
43 #include <Eigen/Core>
44 
45 #include <string.h>
46 
47 #include <algorithm>
48 #include <vector>
49 
50 namespace pcl {
51 namespace distances {
52 
53 /** \brief Compute the median value from a set of doubles
54  * \param[in] fvec the set of doubles
55  * \param[in] m the number of doubles in the set
56  */
57 inline double
58 computeMedian(double* fvec, int m)
59 {
60  // Copy the values to vectors for faster sorting
61  std::vector<double> data(m);
62  memcpy(&data[0], fvec, sizeof(double) * m);
63 
64  std::nth_element(data.begin(), data.begin() + (data.size() >> 1), data.end());
65  return (data[data.size() >> 1]);
66 }
67 
68 /** \brief Use a Huber kernel to estimate the distance between two vectors
69  * \param[in] p_src the first eigen vector
70  * \param[in] p_tgt the second eigen vector
71  * \param[in] sigma the sigma value
72  */
73 inline double
74 huber(const Eigen::Vector4f& p_src, const Eigen::Vector4f& p_tgt, double sigma)
75 {
76  Eigen::Array4f diff = (p_tgt.array() - p_src.array()).abs();
77  double norm = 0.0;
78  for (int i = 0; i < 3; ++i) {
79  if (diff[i] < sigma)
80  norm += diff[i] * diff[i];
81  else
82  norm += 2.0 * sigma * diff[i] - sigma * sigma;
83  }
84  return (norm);
85 }
86 
87 /** \brief Use a Huber kernel to estimate the distance between two vectors
88  * \param[in] diff the norm difference between two vectors
89  * \param[in] sigma the sigma value
90  */
91 inline double
92 huber(double diff, double sigma)
93 {
94  double norm = 0.0;
95  if (diff < sigma)
96  norm += diff * diff;
97  else
98  norm += 2.0 * sigma * diff - sigma * sigma;
99  return (norm);
100 }
101 
102 /** \brief Use a Gedikli kernel to estimate the distance between two vectors
103  * (for more information, see
104  * \param[in] val the norm difference between two vectors
105  * \param[in] clipping the clipping value
106  * \param[in] slope the slope. Default: 4
107  */
108 inline double
109 gedikli(double val, double clipping, double slope = 4)
110 {
111  return (1.0 / (1.0 + pow(std::abs(val) / clipping, slope)));
112 }
113 
114 /** \brief Compute the Manhattan distance between two eigen vectors.
115  * \param[in] p_src the first eigen vector
116  * \param[in] p_tgt the second eigen vector
117  */
118 inline double
119 l1(const Eigen::Vector4f& p_src, const Eigen::Vector4f& p_tgt)
120 {
121  return ((p_src.array() - p_tgt.array()).abs().sum());
122 }
123 
124 /** \brief Compute the Euclidean distance between two eigen vectors.
125  * \param[in] p_src the first eigen vector
126  * \param[in] p_tgt the second eigen vector
127  */
128 inline double
129 l2(const Eigen::Vector4f& p_src, const Eigen::Vector4f& p_tgt)
130 {
131  return ((p_src - p_tgt).norm());
132 }
133 
134 /** \brief Compute the squared Euclidean distance between two eigen vectors.
135  * \param[in] p_src the first eigen vector
136  * \param[in] p_tgt the second eigen vector
137  */
138 inline double
139 l2Sqr(const Eigen::Vector4f& p_src, const Eigen::Vector4f& p_tgt)
140 {
141  return ((p_src - p_tgt).squaredNorm());
142 }
143 } // namespace distances
144 } // namespace pcl
pcl
Definition: convolution.h:46
pcl::distances::l2
double l2(const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the Euclidean distance between two eigen vectors.
Definition: distances.h:129
pcl::distances::computeMedian
double computeMedian(double *fvec, int m)
Compute the median value from a set of doubles.
Definition: distances.h:58
pcl::distances::l1
double l1(const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the Manhattan distance between two eigen vectors.
Definition: distances.h:119
pcl::distances::l2Sqr
double l2Sqr(const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the squared Euclidean distance between two eigen vectors.
Definition: distances.h:139
pcl::distances::huber
double huber(const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt, double sigma)
Use a Huber kernel to estimate the distance between two vectors.
Definition: distances.h:74
pcl::distances::gedikli
double gedikli(double val, double clipping, double slope=4)
Use a Gedikli kernel to estimate the distance between two vectors (for more information,...
Definition: distances.h:109