Detailed Description

Declares a class that represents a Probability Density function (PDF) of a 2D pose $p(\mathbf{x}) = [x ~ y ~ \phi ]^t$ .

This class implements that PDF using a mono-modal Gaussian distribution. See mrpt::poses::CPosePDF for more details.

See also:: CPose2D, CPosePDF, CPosePDFParticles

Definition at line 51 of file CPosePDFGaussian.h.

#include <mrpt/poses/CPosePDFGaussian.h>

Inheritance diagram for mrpt::poses::CPosePDFGaussian:

[legend]

List of all members.

Public Member Functions
	CPosePDFGaussian ()
	Default constructor.
	CPosePDFGaussian (const CPose2D &init_Mean)
	Constructor.
	CPosePDFGaussian (const CPose2D &init_Mean, const CMatrixDouble33 &init_Cov)
	Constructor.
	CPosePDFGaussian (const CPosePDF &o)
	Copy constructor, including transformations between other PDFs.
	CPosePDFGaussian (const CPose3DPDF &o)
	Copy constructor, including transformations between other PDFs.
void	getMean (CPose2D &mean_pose) const
	Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF).
void	getCovarianceAndMean (CMatrixDouble33 &cov, CPose2D &mean_point) const
	Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.
void	copyFrom (const CPosePDF &o)
	Copy operator, translating if necesary (for example, between particles and gaussian representations)
void	copyFrom (const CPose3DPDF &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	changeCoordinatesReference (const CPose2D &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	rotateCov (const double ang)
	Rotate the covariance matrix by replacing it by $\mathbf{R}~\mathbf{COV}~\mathbf{R}^t$ , where $\mathbf{R} = \left[ \begin{array}{ccc} \cos\alpha & -\sin\alpha & 0 \\ \sin\alpha & \cos\alpha & 0 \\ 0 & 0 & 1 \end{array}\right]$ .
void	inverseComposition (const CPosePDFGaussian &x, const CPosePDFGaussian &ref)
	Set $this = x1 \ominus x0$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (For 'x0' and 'x1' being independent variables!).
void	inverseComposition (const CPosePDFGaussian &x1, const CPosePDFGaussian &x0, const CMatrixDouble33 &COV_01)
	Set $this = x1 \ominus x0$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (Given the 3x3 cross-covariance matrix of variables x0 and x1).
void	drawSingleSample (CPose2D &outPart) const
	Draws a single sample from the distribution.
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 1x3 vectors, where each row contains a (x,y,phi) datum.
void	bayesianFusion (const CPosePDF &p1, const CPosePDF &p2, const double &minMahalanobisDistToDrop=0)
	Bayesian fusion of two points gauss.
void	inverse (CPosePDF &o) const
	Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF.
void	operator+= (const CPose2D &Ap)
	Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated).
double	evaluatePDF (const CPose2D &x) const
	Evaluates the PDF at a given point.
double	evaluateNormalizedPDF (const CPose2D &x) const
	Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1].
double	mahalanobisDistanceTo (const CPosePDFGaussian &theOther)
	Computes the Mahalanobis distance between the centers of two Gaussians.
void	assureMinCovariance (const double &minStdXY, const double &minStdPhi)
	Substitutes the diagonal elements if (square) they are below some given minimum values (Use this before bayesianFusion, for example, to avoid inversion of singular matrixes, etc...)
void	operator+= (const CPosePDFGaussian &Ap)
	Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated) (see formulas in jacobiansPoseComposition ).
void	operator-= (const CPosePDFGaussian &ref)
	Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated)
Static Public Member Functions
static void	jacobiansPoseComposition (const CPosePDFGaussian &x, const CPosePDFGaussian &u, CMatrixDouble33 &df_dx, CMatrixDouble33 &df_du)
	This static method computes the pose composition Jacobians, with these formulas:
Public Attributes
Data fields
CPose2D	mean
	The mean value.
CMatrixDouble33	cov
	The 3x3 covariance matrix.
Protected Member Functions
void	assureSymmetry ()
	Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!)
RTTI stuff
typedef CPosePDFGaussianPtr	SmartPtr
static mrpt::utils::CLASSINIT	_init_CPosePDFGaussian
static mrpt::utils::TRuntimeClassId	classCPosePDFGaussian
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 CPosePDFGaussianPtr	Create ()

Member Typedef Documentation

typedef CPosePDFGaussianPtr mrpt::poses::CPosePDFGaussian::SmartPtr

A typedef for the associated smart pointer

Definition at line 54 of file CPosePDFGaussian.h.

Constructor & Destructor Documentation

mrpt::poses::CPosePDFGaussian::CPosePDFGaussian ( )

Default constructor.

mrpt::poses::CPosePDFGaussian::CPosePDFGaussian ( const CPose2D & init_Mean ) [explicit]

Constructor.

mrpt::poses::CPosePDFGaussian::CPosePDFGaussian	(	const CPose2D &	init_Mean,
		const CMatrixDouble33 &	init_Cov
	)

Constructor.

mrpt::poses::CPosePDFGaussian::CPosePDFGaussian ( const CPosePDF & o ) [inline, explicit]

Copy constructor, including transformations between other PDFs.

Definition at line 83 of file CPosePDFGaussian.h.

mrpt::poses::CPosePDFGaussian::CPosePDFGaussian ( const CPose3DPDF & o ) [inline, explicit]

Copy constructor, including transformations between other PDFs.

Definition at line 86 of file CPosePDFGaussian.h.

Member Function Documentation

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

Reimplemented from mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFGaussian::assureMinCovariance	(	const double &	minStdXY,
		const double &	minStdPhi
	)

Substitutes the diagonal elements if (square) they are below some given minimum values (Use this before bayesianFusion, for example, to avoid inversion of singular matrixes, etc...)

void mrpt::poses::CPosePDFGaussian::assureSymmetry ( ) [protected]

Assures the symmetry of the covariance matrix (eventually certain operations in the math-coprocessor lead to non-symmetric matrixes!)

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

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 );

Implements mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFGaussian::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.

Implements mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

void mrpt::poses::CPosePDFGaussian::changeCoordinatesReference ( const CPose2D & 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.

Result PDF substituted the currently stored one in the object.

void mrpt::poses::CPosePDFGaussian::copyFrom ( const CPosePDF & o ) [virtual]

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

Implements mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFGaussian::copyFrom ( const CPose3DPDF & o )

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

static CPosePDFGaussianPtr mrpt::poses::CPosePDFGaussian::Create ( ) [static]

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

void mrpt::poses::CPosePDFGaussian::drawManySamples	(	size_t	N,
		std::vector< vector_double > &	outSamples
	)		const `[virtual]`

Draws a number of samples from the distribution, and saves as a list of 1x3 vectors, where each row contains a (x,y,phi) datum.

Reimplemented from mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

void mrpt::poses::CPosePDFGaussian::drawSingleSample ( CPose2D & outPart ) const [virtual]

Draws a single sample from the distribution.

Implements mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

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

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

Implements mrpt::utils::CObject.

double mrpt::poses::CPosePDFGaussian::evaluateNormalizedPDF ( const CPose2D & x ) const

Evaluates the ratio PDF(x) / PDF(MEAN), that is, the normalized PDF in the range [0,1].

double mrpt::poses::CPosePDFGaussian::evaluatePDF ( const CPose2D & x ) const

Evaluates the PDF at a given point.

void mrpt::poses::CPosePDFGaussian::getCovarianceAndMean	(	CMatrixDouble33 &	cov,
		CPose2D &	mean_point
	)		const

Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.

See also:: getMean

void mrpt::poses::CPosePDFGaussian::getMean ( CPose2D & mean_pose ) const [virtual]

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

See also:: getCovariance

Implements mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

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

Returns information about the class of an object in runtime.

Reimplemented from mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFGaussian::inverse ( CPosePDF & o ) const [virtual]

Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF.

Implements mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFGaussian::inverseComposition	(	const CPosePDFGaussian &	x,
		const CPosePDFGaussian &	ref
	)

Set $this = x1 \ominus x0$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (For 'x0' and 'x1' being independent variables!).

Referenced by mrpt::poses::operator-().

void mrpt::poses::CPosePDFGaussian::inverseComposition	(	const CPosePDFGaussian &	x1,
		const CPosePDFGaussian &	x0,
		const CMatrixDouble33 &	COV_01
	)

Set $this = x1 \ominus x0$ , computing the mean using the "-" operator and the covariances through the corresponding Jacobians (Given the 3x3 cross-covariance matrix of variables x0 and x1).

static void mrpt::poses::CPosePDFGaussian::jacobiansPoseComposition	(	const CPosePDFGaussian &	x,
		const CPosePDFGaussian &	u,
		CMatrixDouble33 &	df_dx,
		CMatrixDouble33 &	df_du
	)		`[static]`

This static method computes the pose composition Jacobians, with these formulas:

                                df_dx =
                                [ 1, 0, -sin(phi_x)*x_u-cos(phi_x)*y_u ]
                                [ 0, 1,  cos(phi_x)*x_u-sin(phi_x)*y_u ]
                                [ 0, 0,                              1 ]

                                df_du =
                                [ cos(phi_x) , -sin(phi_x) ,  0  ]
                                [ sin(phi_x) ,  cos(phi_x) ,  0  ]
                                [         0  ,          0  ,  1  ]

Referenced by mrpt::math::jacobians::jacobs_2D_pose_comp().

double mrpt::poses::CPosePDFGaussian::mahalanobisDistanceTo ( const CPosePDFGaussian & theOther )

Computes the Mahalanobis distance between the centers of two Gaussians.

void mrpt::poses::CPosePDFGaussian::operator+= ( const CPose2D & Ap )

Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated).

void mrpt::poses::CPosePDFGaussian::operator+= ( const CPosePDFGaussian & Ap )

Makes: thisPDF = thisPDF + Ap, where "+" is pose composition (both the mean, and the covariance matrix are updated) (see formulas in jacobiansPoseComposition ).

void mrpt::poses::CPosePDFGaussian::operator-= ( const CPosePDFGaussian & ref ) [inline]

Makes: thisPDF = thisPDF - Ap, where "-" is pose inverse composition (both the mean, and the covariance matrix are updated)

Definition at line 185 of file CPosePDFGaussian.h.

void mrpt::poses::CPosePDFGaussian::rotateCov ( const double ang )

Rotate the covariance matrix by replacing it by $\mathbf{R}~\mathbf{COV}~\mathbf{R}^t$ , where $\mathbf{R} = \left[ \begin{array}{ccc} \cos\alpha & -\sin\alpha & 0 \\ \sin\alpha & \cos\alpha & 0 \\ 0 & 0 & 1 \end{array}\right]$ .

void mrpt::poses::CPosePDFGaussian::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< CPose2D, 3 >.