Detailed Description

A gaussian distribution for 2D points.

Also a method for bayesian fusion is provided.

See also:: CPoint2DPDF

#include <mrpt/poses/CPoint2DPDFGaussian.h>

Inheritance diagram for mrpt::poses::CPoint2DPDFGaussian:

Public Types
enum	{ is_3D_val = 0 }
enum	{ is_PDF_val = 1 }
typedef TDATA	type_value
	The type of the state the PDF represents.
Public Member Functions
	CPoint2DPDFGaussian ()
	Default constructor.
	CPoint2DPDFGaussian (const CPoint2D &init_Mean)
	Constructor.
	CPoint2DPDFGaussian (const CPoint2D &init_Mean, const CMatrixDouble22 &init_Cov)
	Constructor.
void	getMean (CPoint2D &p) const
	Returns an estimate of the point, (the mean, or mathematical expectation of the PDF)
void	getCovarianceAndMean (CMatrixDouble22 &cov, CPoint2D &mean_point) const
	Returns an estimate of the point covariance matrix (2x2 cov matrix) and the mean, both at once.
void	copyFrom (const CPoint2DPDF &o)
	Copy operator, translating if necesary (for example, between particles and gaussian representations)
void	saveToTextFile (const std::string &file) const
	Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.
void	changeCoordinatesReference (const CPose3D &newReferenceBase)
	This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which "to project" the current pdf.
void	bayesianFusion (const CPoint2DPDFGaussian &p1, const CPoint2DPDFGaussian &p2)
	Bayesian fusion of two points gauss.
double	productIntegralWith (const CPoint2DPDFGaussian &p) const
	Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.
double	productIntegralNormalizedWith (const CPoint2DPDFGaussian &p) const
	Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.
void	drawSingleSample (CPoint2D &outSample) const
	Draw a sample from the pdf.
void	bayesianFusion (const CPoint2DPDF &p1, const CPoint2DPDF &p2, const double &minMahalanobisDistToDrop=0)
	Bayesian fusion of two point distributions (product of two distributions->new distribution), then save the result in this object (WARNING: See implementing classes to see classes that can and cannot be mixtured!)
double	mahalanobisDistanceTo (const CPoint2DPDFGaussian &other) const
	Returns the Mahalanobis distance from this PDF to another PDF, that is, it's evaluation at (0,0,0)
mrpt::utils::CObjectPtr	duplicateGetSmartPtr () const
	Returns a copy of the object, indepently of its class, as a smart pointer (the newly created object will exist as long as any copy of this smart pointer).
CObject *	clone () const
	Cloning interface for smart pointers.
virtual void	getMean (TDATA &mean_point) const =0
	Returns the mean, or mathematical expectation of the probability density distribution (PDF).
virtual void	getCovarianceAndMean (CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &cov, TDATA &mean_point) const =0
	Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.
void	getCovarianceDynAndMean (CMatrixDouble &cov, TDATA &mean_point) const
	Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.
TDATA	getMeanVal () const
	Returns the mean, or mathematical expectation of the probability density distribution (PDF).
void	getCovariance (CMatrixDouble &cov) const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
void	getCovariance (CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &cov) const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN >	getCovariance () const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
virtual void	drawSingleSample (TDATA &outPart) const =0
	Draws a single sample from the distribution.
virtual void	drawManySamples (size_t N, std::vector< vector_double > &outSamples) const
	Draws a number of samples from the distribution, and saves as a list of 1xSTATE_LEN vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum.
double	getCovarianceEntropy () const
	Compute the entropy of the estimated covariance matrix.
Static Public Member Functions
static bool	is_3D ()
static bool	is_PDF ()
Public Attributes
CPoint2D	mean
	The mean value.
CMatrixDouble22	cov
	The 2x2 covariance matrix.
Static Public Attributes
static const mrpt::utils::TRuntimeClassId	classCObject
static const size_t	state_length
	The length of the variable, for example, 3 for a 3D point, 6 for a 3D pose (x y z yaw pitch roll).
RTTI stuff
static const mrpt::utils::TRuntimeClassId	classCSerializable
Protected Member Functions
virtual void	writeToStream (mrpt::utils::CStream &out, int *getVersion) const =0
	Introduces a pure virtual method responsible for writing to a CStream.
virtual void	readFromStream (mrpt::utils::CStream &in, int version)=0
	Introduces a pure virtual method responsible for loading from a CStream This can not be used directly be users, instead use "stream >> object;" for reading it from a stream or "stream >> object_ptr;" if the class is unknown apriori.
RTTI stuff
static const mrpt::utils::TRuntimeClassId	classCPoint2DPDF
class	mrpt::utils::CStream
RTTI stuff
typedef CPoint2DPDFGaussianPtr	SmartPtr
static mrpt::utils::CLASSINIT	_init_CPoint2DPDFGaussian
static mrpt::utils::TRuntimeClassId	classCPoint2DPDFGaussian
static const mrpt::utils::TRuntimeClassId *	classinfo
static const mrpt::utils::TRuntimeClassId *	_GetBaseClass ()
virtual const mrpt::utils::TRuntimeClassId *	GetRuntimeClass () const
	Returns information about the class of an object in runtime.
virtual mrpt::utils::CObject *	duplicate () const
	Returns a copy of the object, indepently of its class.
static mrpt::utils::CObject *	CreateObject ()
static CPoint2DPDFGaussianPtr	Create ()

Member Typedef Documentation

typedef CPoint2DPDFGaussianPtr mrpt::poses::CPoint2DPDFGaussian::SmartPtr

A typedef for the associated smart pointer

Definition at line 48 of file CPoint2DPDFGaussian.h.

typedef TDATA mrpt::utils::CProbabilityDensityFunction::type_value [inherited]

The type of the state the PDF represents.

Definition at line 54 of file CProbabilityDensityFunction.h.

Member Enumeration Documentation

anonymous enum [inherited]

Enumerator:

is_3D_val

Definition at line 72 of file CPoint2DPDF.h.

anonymous enum [inherited]

Enumerator:

is_PDF_val

Definition at line 74 of file CPoint2DPDF.h.

Constructor & Destructor Documentation

mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian ( )

Default constructor.

mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian ( const CPoint2D & init_Mean )

Constructor.

mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian	(	const CPoint2D &	init_Mean,
		const CMatrixDouble22 &	init_Cov
	)

Constructor.

Member Function Documentation

static const mrpt::utils::TRuntimeClassId* mrpt::poses::CPoint2DPDFGaussian::_GetBaseClass ( ) [static, protected]

Reimplemented from mrpt::poses::CPoint2DPDF.

void mrpt::poses::CPoint2DPDFGaussian::bayesianFusion	(	const CPoint2DPDFGaussian &	p1,
		const CPoint2DPDFGaussian &	p2
	)

Bayesian fusion of two points gauss.

distributions, then save the result in this object. The process is as follows:

(x1,S1): Mean and variance of the p1 distribution.
(x2,S2): Mean and variance of the p2 distribution.
(x,S): Mean and variance of the resulting distribution.

S = (S1^-1 + S2^-1)^-1; x = S * ( S1^-1*x1 + S2^-1*x2 );

void mrpt::poses::CPoint2DPDFGaussian::bayesianFusion	(	const CPoint2DPDF &	p1,
		const CPoint2DPDF &	p2,
		const double &	minMahalanobisDistToDrop = `0`
	)		`[virtual]`

Bayesian fusion of two point distributions (product of two distributions->new distribution), then save the result in this object (WARNING: See implementing classes to see classes that can and cannot be mixtured!)

Parameters:

p1	The first distribution to fuse
p2	The second distribution to fuse
minMahalanobisDistToDrop	If set to different of 0, the result of very separate Gaussian modes (that will result in negligible components) in SOGs will be dropped to reduce the number of modes in the output.

Implements mrpt::poses::CPoint2DPDF.

void mrpt::poses::CPoint2DPDFGaussian::changeCoordinatesReference ( const CPose3D & newReferenceBase ) [virtual]

This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which "to project" the current pdf.

Result PDF substituted the currently stored one in the object. Both the mean value and the covariance matrix are updated correctly.

Implements mrpt::utils::CProbabilityDensityFunction< CPoint2D, 2 >.

CObject* mrpt::utils::CObject::clone ( ) const [inline, inherited]

Cloning interface for smart pointers.

Reimplemented in mrpt::opengl::CRenderizable, and mrpt::opengl::CRenderizableDisplayList.

Definition at line 154 of file CObject.h.

void mrpt::poses::CPoint2DPDFGaussian::copyFrom ( const CPoint2DPDF & o ) [virtual]

Copy operator, translating if necesary (for example, between particles and gaussian representations)

Implements mrpt::poses::CPoint2DPDF.

static CPoint2DPDFGaussianPtr mrpt::poses::CPoint2DPDFGaussian::Create ( ) [static]

static mrpt::utils::CObject* mrpt::poses::CPoint2DPDFGaussian::CreateObject ( ) [static]

virtual void mrpt::utils::CProbabilityDensityFunction::drawManySamples	(	size_t	N,
		std::vector< vector_double > &	outSamples
	)		const `[inline, virtual, inherited]`

Draws a number of samples from the distribution, and saves as a list of 1xSTATE_LEN vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum.

This base method just call N times to drawSingleSample, but derived classes should implemented optimized method for each particular PDF.

Definition at line 126 of file CProbabilityDensityFunction.h.

virtual void mrpt::utils::CProbabilityDensityFunction::drawSingleSample ( TDATA & outPart ) const [pure virtual, inherited]

Draws a single sample from the distribution.

void mrpt::poses::CPoint2DPDFGaussian::drawSingleSample ( CPoint2D & outSample ) const

Draw a sample from the pdf.

virtual mrpt::utils::CObject* mrpt::poses::CPoint2DPDFGaussian::duplicate ( ) const [virtual]

Returns a copy of the object, indepently of its class.

Implements mrpt::utils::CObject.

mrpt::utils::CObjectPtr mrpt::utils::CObject::duplicateGetSmartPtr ( ) const [inline, inherited]

Returns a copy of the object, indepently of its class, as a smart pointer (the newly created object will exist as long as any copy of this smart pointer).

Definition at line 151 of file CObject.h.

void mrpt::utils::CProbabilityDensityFunction::getCovariance ( CMatrixDouble & cov ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean, getCovarianceAndMean

Definition at line 89 of file CProbabilityDensityFunction.h.

void mrpt::utils::CProbabilityDensityFunction::getCovariance ( CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > & cov ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean, getCovarianceAndMean

Definition at line 98 of file CProbabilityDensityFunction.h.

CMatrixFixedNumeric<double,STATE_LEN,STATE_LEN> mrpt::utils::CProbabilityDensityFunction::getCovariance ( ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean

Definition at line 107 of file CProbabilityDensityFunction.h.

virtual void mrpt::utils::CProbabilityDensityFunction::getCovarianceAndMean	(	CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &	cov,
		TDATA &	mean_point
	)		const `[pure virtual, inherited]`

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:: getMean

void mrpt::poses::CPoint2DPDFGaussian::getCovarianceAndMean	(	CMatrixDouble22 &	cov,
		CPoint2D &	mean_point
	)		const `[inline]`

Returns an estimate of the point covariance matrix (2x2 cov matrix) and the mean, both at once.

See also:: getMean

Definition at line 80 of file CPoint2DPDFGaussian.h.

References mrpt::math::cov(), and mean().

void mrpt::utils::CProbabilityDensityFunction::getCovarianceDynAndMean	(	CMatrixDouble &	cov,
		TDATA &	mean_point
	)		const `[inline, inherited]`

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:: getMean

Definition at line 69 of file CProbabilityDensityFunction.h.

double mrpt::utils::CProbabilityDensityFunction::getCovarianceEntropy ( ) const [inline, inherited]

Compute the entropy of the estimated covariance matrix.

See also:: http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Entropy

Definition at line 145 of file CProbabilityDensityFunction.h.

virtual void mrpt::utils::CProbabilityDensityFunction::getMean ( TDATA & mean_point ) const [pure virtual, inherited]

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

See also:: getCovarianceAndMean

void mrpt::poses::CPoint2DPDFGaussian::getMean ( CPoint2D & p ) const [inline]

Returns an estimate of the point, (the mean, or mathematical expectation of the PDF)

Definition at line 73 of file CPoint2DPDFGaussian.h.

References mean().

TDATA mrpt::utils::CProbabilityDensityFunction::getMeanVal ( ) const [inline, inherited]

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

See also:: getCovariance

Definition at line 79 of file CProbabilityDensityFunction.h.

virtual const mrpt::utils::TRuntimeClassId* mrpt::poses::CPoint2DPDFGaussian::GetRuntimeClass ( ) const [virtual]

Returns information about the class of an object in runtime.

Reimplemented from mrpt::poses::CPoint2DPDF.

static bool mrpt::poses::CPoint2DPDF::is_3D ( ) [inline, static, inherited]

Definition at line 73 of file CPoint2DPDF.h.

static bool mrpt::poses::CPoint2DPDF::is_PDF ( ) [inline, static, inherited]

Definition at line 75 of file CPoint2DPDF.h.

double mrpt::poses::CPoint2DPDFGaussian::mahalanobisDistanceTo ( const CPoint2DPDFGaussian & other ) const

Returns the Mahalanobis distance from this PDF to another PDF, that is, it's evaluation at (0,0,0)

double mrpt::poses::CPoint2DPDFGaussian::productIntegralNormalizedWith ( const CPoint2DPDFGaussian & p ) const

Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.

The resulting number is in the range [0,1]. Note that the resulting value is in fact

$exp( -\frac{1}{2} D^2 )$

, with $ D^2 $ being the square Mahalanobis distance between the two pdfs.

See also:: productIntegralWith

Exceptions:

std::exception On errors like covariance matrix with null determinant, etc...

double mrpt::poses::CPoint2DPDFGaussian::productIntegralWith ( const CPoint2DPDFGaussian & p ) const

Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.

The resulting number is >=0.

See also:: productIntegralNormalizedWith

Exceptions:

std::exception On errors like covariance matrix with null determinant, etc...

virtual void mrpt::utils::CSerializable::readFromStream	(	mrpt::utils::CStream &	in,
		int	version
	)		`[protected, pure virtual, inherited]`

Introduces a pure virtual method responsible for loading from a CStream This can not be used directly be users, instead use "stream >> object;" for reading it from a stream or "stream >> object_ptr;" if the class is unknown apriori.

Parameters:

in	The input binary stream where the object data must read from.
version	The version of the object stored in the stream: use this version number in your code to know how to read the incoming data.

Exceptions:

std::exception On any error, see CStream::ReadBuffer

See also:: CStream

Implemented in mrpt::math::CMatrixD, and mrpt::math::CMatrix.

void mrpt::poses::CPoint2DPDFGaussian::saveToTextFile ( const std::string & file ) const [virtual]

Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.

Implements mrpt::utils::CProbabilityDensityFunction< CPoint2D, 2 >.

virtual void mrpt::utils::CSerializable::writeToStream	(	mrpt::utils::CStream &	out,
		int *	getVersion
	)		const `[protected, pure virtual, inherited]`

Introduces a pure virtual method responsible for writing to a CStream.

This can not be used directly be users, instead use "stream << object;" for writing it to a stream.

Parameters:

out	The output binary stream where object must be dumped.
getVersion	If NULL, the object must be dumped. If not, only the version of the object dump must be returned in this pointer. This enables the versioning of objects dumping and backward compatibility with previously stored data.

Exceptions:

std::exception On any error, see CStream::WriteBuffer