Point Cloud Library (PCL)  1.14.1-dev
pcl::distances Namespace Reference

## Functions

double computeMedian (double *fvec, int m)
Compute the median value from a set of doubles. More...

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. More...

double huber (double diff, double sigma)
Use a Huber kernel to estimate the distance between two vectors. More...

double gedikli (double val, double clipping, double slope=4)
Use a Gedikli kernel to estimate the distance between two vectors (for more information, see. More...

double l1 (const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the Manhattan distance between two eigen vectors. More...

double l2 (const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the Euclidean distance between two eigen vectors. More...

double l2Sqr (const Eigen::Vector4f &p_src, const Eigen::Vector4f &p_tgt)
Compute the squared Euclidean distance between two eigen vectors. More...

## ◆ computeMedian()

 double pcl::distances::computeMedian ( double * fvec, int m )
inline

Compute the median value from a set of doubles.

Parameters
 [in] fvec the set of doubles [in] m the number of doubles in the set

Definition at line 57 of file distances.h.

## ◆ gedikli()

 double pcl::distances::gedikli ( double val, double clipping, double slope = `4` )
inline

Use a Gedikli kernel to estimate the distance between two vectors (for more information, see.

Parameters
 [in] val the norm difference between two vectors [in] clipping the clipping value [in] slope the slope. Default: 4

Definition at line 107 of file distances.h.

## ◆ huber() [1/2]

 double pcl::distances::huber ( const Eigen::Vector4f & p_src, const Eigen::Vector4f & p_tgt, double sigma )
inline

Use a Huber kernel to estimate the distance between two vectors.

Parameters
 [in] p_src the first eigen vector [in] p_tgt the second eigen vector [in] sigma the sigma value

Definition at line 72 of file distances.h.

## ◆ huber() [2/2]

 double pcl::distances::huber ( double diff, double sigma )
inline

Use a Huber kernel to estimate the distance between two vectors.

Parameters
 [in] diff the norm difference between two vectors [in] sigma the sigma value

Definition at line 90 of file distances.h.

## ◆ l1()

 double pcl::distances::l1 ( const Eigen::Vector4f & p_src, const Eigen::Vector4f & p_tgt )
inline

Compute the Manhattan distance between two eigen vectors.

Parameters
 [in] p_src the first eigen vector [in] p_tgt the second eigen vector

Definition at line 117 of file distances.h.

## ◆ l2()

 double pcl::distances::l2 ( const Eigen::Vector4f & p_src, const Eigen::Vector4f & p_tgt )
inline

Compute the Euclidean distance between two eigen vectors.

Parameters
 [in] p_src the first eigen vector [in] p_tgt the second eigen vector

Definition at line 127 of file distances.h.

## ◆ l2Sqr()

 double pcl::distances::l2Sqr ( const Eigen::Vector4f & p_src, const Eigen::Vector4f & p_tgt )
inline

Compute the squared Euclidean distance between two eigen vectors.

Parameters
 [in] p_src the first eigen vector [in] p_tgt the second eigen vector

Definition at line 137 of file distances.h.