Class CPosePDFGaussian

Inheritance Relationships

Base Type

Class Documentation

class CPosePDFGaussian : public mrpt::poses::CPosePDF

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

Data fields

CPose2D mean

The mean value

mrpt::math::CMatrixDouble33 cov

The 3x3 covariance matrix

Public Functions

inline const CPose2D &getPoseMean() const
inline CPose2D &getPoseMean()
CPosePDFGaussian()

Default constructor

explicit CPosePDFGaussian(const CPose2D &init_Mean)

Constructor

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

Constructor

inline explicit CPosePDFGaussian(const CPosePDF &o)

Copy constructor, including transformations between other PDFs

inline explicit CPosePDFGaussian(const CPose3DPDF &o)

Copy constructor, including transformations between other PDFs

inline void getMean(CPose2D &mean_pose) const override
inline std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override
virtual void copyFrom(const CPosePDF &o) override

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

void copyFrom(const CPose3DPDF &o)

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

bool saveToTextFile(const std::string &file) const override

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

virtual void changeCoordinatesReference(const CPose3D &newReferenceBase) override

this = p (+) this. 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 changeCoordinatesReference(const CPose2D &newReferenceBase)

this = p (+) this. 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 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 mrpt::math::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 override

Draws a single sample from the distribution

void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble> &outSamples) const override

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.

virtual void bayesianFusion(const CPosePDF &p1, const CPosePDF &p2, const double minMahalanobisDistToDrop = 0) override

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.

\( S = (S_1^{-1} + S_2^{-1})^{-1} \) \( x = S ( S_1^{-1} x_1 + S_2^{-1} x_2 ) \)

virtual void inverse(CPosePDF &o) const override

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(double minStdXY, 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 ).

inline void operator-=(const CPosePDFGaussian &ref)

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

void composePoint(const mrpt::math::TPoint2D &l, CPoint2DPDFGaussian &g) const

Returns the PDF of the 2D point \( g = q \oplus l\) with “q”=this pose and “l” a point without uncertainty

virtual void printTo(std::ostream &out) const override

Write a human-readable description of this PDF to the given stream. Derived classes must override this method.

Protected Functions

void enforceCovSymmetry()

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