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vector_average.h
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2010-2012, Willow Garage, Inc.
* Copyright (c) 2012-, Open Perception, Inc.
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#pragma once
#include <Eigen/Core> // for Matrix
#include <pcl/memory.h>
#include <pcl/pcl_macros.h>
namespace pcl
{
/** \brief Calculates the weighted average and the covariance matrix
*
* A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights.
* The original data is not saved. Mean and covariance are calculated iteratively.
* \author Bastian Steder
* \ingroup common
*/
template <typename real, int dimension>
class VectorAverage
{
public:
using VectorType = Eigen::Matrix<real, dimension, 1>;
using MatrixType = Eigen::Matrix<real, dimension, dimension>;
//-----CONSTRUCTOR&DESTRUCTOR-----
/** Constructor - dimension gives the size of the vectors to work with. */
VectorAverage ();
//-----METHODS-----
/** Reset the object to work with a new data set */
inline void
reset ();
/** Get the mean of the added vectors */
inline const
VectorType& getMean () const { return mean_;}
/** Get the covariance matrix of the added vectors */
inline const
MatrixType& getCovariance () const { return covariance_;}
/** Get the summed up weight of all added vectors */
inline real
getAccumulatedWeight () const { return accumulatedWeight_;}
/** Get the number of added vectors */
inline unsigned int
getNoOfSamples () { return noOfSamples_;}
/** Add a new sample */
inline void
add (const VectorType& sample, real weight=1.0);
/** Do Principal component analysis */
inline void
doPCA (VectorType& eigen_values, VectorType& eigen_vector1,
VectorType& eigen_vector2, VectorType& eigen_vector3) const;
/** Do Principal component analysis */
inline void
doPCA (VectorType& eigen_values) const;
/** Get the eigenvector corresponding to the smallest eigenvalue */
inline void
getEigenVector1 (VectorType& eigen_vector1) const;
PCL_MAKE_ALIGNED_OPERATOR_NEW
//-----VARIABLES-----
protected:
//-----METHODS-----
//-----VARIABLES-----
unsigned int noOfSamples_ = 0;
real accumulatedWeight_ = 0;
VectorType mean_ = VectorType::Identity ();
MatrixType covariance_ = MatrixType::Identity ();
};
using VectorAverage2f = VectorAverage<float, 2>;
using VectorAverage3f = VectorAverage<float, 3>;
using VectorAverage4f = VectorAverage<float, 4>;
} // END namespace
#include <pcl/common/impl/vector_average.hpp>
pcl::VectorAverage
Calculates the weighted average and the covariance matrix.
Definition vector_average.h:56
pcl::VectorAverage::mean_
VectorType mean_
Definition vector_average.h:112
pcl::VectorAverage::accumulatedWeight_
real accumulatedWeight_
Definition vector_average.h:111
pcl::VectorAverage::add
void add(const VectorType &sample, real weight=1.0)
Add a new sample.
Definition vector_average.hpp:63
pcl::VectorAverage::reset
void reset()
Reset the object to work with a new data set.
Definition vector_average.hpp:54
pcl::VectorAverage::VectorAverage
VectorAverage()
Constructor - dimension gives the size of the vectors to work with.
Definition vector_average.hpp:48
pcl::VectorAverage::VectorType
Eigen::Matrix< real, dimension, 1 > VectorType
Definition vector_average.h:58
pcl::VectorAverage::doPCA
void doPCA(VectorType &eigen_values, VectorType &eigen_vector1, VectorType &eigen_vector2, VectorType &eigen_vector3) const
Do Principal component analysis.
Definition vector_average.hpp:84
pcl::VectorAverage::MatrixType
Eigen::Matrix< real, dimension, dimension > MatrixType
Definition vector_average.h:59
pcl::VectorAverage::getAccumulatedWeight
real getAccumulatedWeight() const
Get the summed up weight of all added vectors.
Definition vector_average.h:79
pcl::VectorAverage::getMean
const VectorType & getMean() const
Get the mean of the added vectors.
Definition vector_average.h:71
pcl::VectorAverage::getEigenVector1
void getEigenVector1(VectorType &eigen_vector1) const
Get the eigenvector corresponding to the smallest eigenvalue.
Definition vector_average.hpp:121
pcl::VectorAverage::getNoOfSamples
unsigned int getNoOfSamples()
Get the number of added vectors.
Definition vector_average.h:83
pcl::VectorAverage::covariance_
MatrixType covariance_
Definition vector_average.h:113
pcl::VectorAverage::noOfSamples_
unsigned int noOfSamples_
Definition vector_average.h:110
pcl::VectorAverage::getCovariance
const MatrixType & getCovariance() const
Get the covariance matrix of the added vectors.
Definition vector_average.h:75
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:63
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl
Definition convolution.h:46
pcl::VectorAverage3f
VectorAverage< float, 3 > VectorAverage3f
Definition vector_average.h:117
pcl::VectorAverage4f
VectorAverage< float, 4 > VectorAverage4f
Definition vector_average.h:118
pcl::VectorAverage2f
VectorAverage< float, 2 > VectorAverage2f
Definition vector_average.h:116
pcl_macros.h
Defines all the PCL and non-PCL macros used.