Point Cloud Library (PCL)  1.14.1-dev
Functions
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...
 

Function Documentation

◆ computeMedian()

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

Compute the median value from a set of doubles.

Parameters
[in]fvecthe set of doubles
[in]mthe 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]valthe norm difference between two vectors
[in]clippingthe clipping value
[in]slopethe 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_srcthe first eigen vector
[in]p_tgtthe second eigen vector
[in]sigmathe 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]diffthe norm difference between two vectors
[in]sigmathe 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_srcthe first eigen vector
[in]p_tgtthe 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_srcthe first eigen vector
[in]p_tgtthe 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_srcthe first eigen vector
[in]p_tgtthe second eigen vector

Definition at line 137 of file distances.h.