class mrpt::poses::CPoint2DPDF

Overview

Declares a class that represents a Probability Distribution function (PDF) of a 2D point (x,y).

This class is just the base class for unifying many different ways this PDF can be implemented.

For convenience, a pose composition is also defined for any PDF derived class, changeCoordinatesReference, in the form of a method rather than an operator.

For a similar class for 6D poses (a 3D point with attitude), see CPose3DPDF

See also:

CPoint2D, CPointPDF

#include <mrpt/poses/CPoint2DPDF.h>

class CPoint2DPDF:
    public mrpt::serialization::CSerializable,
    public mrpt::math::CProbabilityDensityFunction
{
public:
    // typedefs

    typedef std::shared_ptr<CPoint2DPDF> Ptr;
    typedef std::shared_ptr<const CPoint2DPDF> ConstPtr;

    // enums

    enum
    {
        is_3D_val = 0,
    };

    enum
    {
        is_PDF_val = 1,
    };

    // construction

    CPoint2DPDF();
    CPoint2DPDF(const CPoint2DPDF&);
    CPoint2DPDF(CPoint2DPDF&&);

    // methods

    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    CPoint2DPDF& operator = (const CPoint2DPDF&);
    CPoint2DPDF& operator = (CPoint2DPDF&&);
    virtual void copyFrom(const CPoint2DPDF& o) = 0;
    virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0;

    virtual void bayesianFusion(
        const CPoint2DPDF& p1,
        const CPoint2DPDF& p2,
        const double minMahalanobisDistToDrop = 0
        ) = 0;

    static constexpr bool is_3D();
    static constexpr bool is_PDF();
};

// direct descendants

class CPoint2DPDFGaussian;

Inherited Members

public:
    // typedefs

    typedef std::shared_ptr<CObject> Ptr;
    typedef std::shared_ptr<const CObject> ConstPtr;
    typedef std::unique_ptr<CObject> UniquePtr;
    typedef std::unique_ptr<const CObject> ConstUniquePtr;
    typedef std::shared_ptr<CSerializable> Ptr;
    typedef std::shared_ptr<const CSerializable> ConstPtr;
    typedef TDATA type_value;
    typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t;
    typedef mrpt::math::CMatrixFixed<double, STATE_LEN, STATE_LEN> cov_mat_t;
    typedef cov_mat_t inf_mat_t;

    // fields

    static constexpr size_t state_length = STATE_LEN;

    // methods

    mrpt::rtti::CObject::Ptr duplicateGetSmartPtr() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    virtual CObject* clone() const = 0;
    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    CProbabilityDensityFunction& operator = (const CProbabilityDensityFunction&);
    CProbabilityDensityFunction& operator = (CProbabilityDensityFunction&&);
    virtual void getMean(type_value& mean_point) const = 0;
    virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const = 0;
    virtual void getCovarianceAndMean(cov_mat_t& c, TDATA& mean) const;
    void getCovarianceDynAndMean(mrpt::math::CMatrixDouble& cov, type_value& mean_point) const;
    type_value getMeanVal() const;
    void getCovariance(mrpt::math::CMatrixDouble& cov) const;
    void getCovariance(cov_mat_t& cov) const;
    cov_mat_t getCovariance() const;
    virtual bool isInfType() const;
    virtual void getInformationMatrix(inf_mat_t& inf) const;
    virtual bool saveToTextFile(const std::string& file) const = 0;
    virtual void drawSingleSample(TDATA& outPart) const = 0;
    virtual void drawManySamples(size_t N, std::vector<mrpt::math::CVectorDouble>& outSamples) const;
    double getCovarianceEntropy() const;

Methods

virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const

Returns information about the class of an object in runtime.

virtual void copyFrom(const CPoint2DPDF& o) = 0

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

virtual void bayesianFusion(
    const CPoint2DPDF& p1,
    const CPoint2DPDF& p2,
    const double minMahalanobisDistToDrop = 0
    ) = 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!)

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.