class mrpt::maps::CBeacon

Python API: mrpt.maps.CBeacon

Overview

The class for storing individual “beacon landmarks” under a variety of 3D position PDF distributions.

This class is used for storage within the class CBeaconMap. The class implements the same methods than the interface “CPointPDF”, and invoking them actually becomes a mapping into the methods of the current PDF representation of the beacon, selectable by means of “m_typePDF”

See also:

CBeaconMap, CPointPDFSOG

#include <mrpt/maps/CBeacon.h>

class CBeacon: public mrpt::poses::CPointPDF
{
public:
    // typedefs

    typedef std::shared_ptr<mrpt::maps ::CBeacon> Ptr;
    typedef std::shared_ptr<const mrpt::maps ::CBeacon> ConstPtr;
    typedef std::unique_ptr<mrpt::maps ::CBeacon> UniquePtr;
    typedef std::unique_ptr<const mrpt::maps ::CBeacon> ConstUniquePtr;
    typedef int64_t TBeaconID;

    // enums

    enum TTypePDF;

    // fields

    static constexpr const char* className = "mrpt::maps" "::" "CBeacon";
    TTypePDF m_typePDF {pdfGauss};
    mrpt::poses::CPointPDFParticles m_locationMC {1};
    mrpt::poses::CPointPDFGaussian m_locationGauss;
    mrpt::poses::CPointPDFSOG m_locationSOG {1};
    TBeaconID m_ID = mrpt::obs::INVALID_BEACON_ID;

    // methods

    static constexpr auto getClassName();
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    static std::shared_ptr<CObject> CreateObject();

    template <typename... Args>
    static Ptr Create(Args&&... args);

    template <typename Alloc, typename... Args>
    static Ptr CreateAlloc(
        const Alloc& alloc,
        Args&&... args
        );

    template <typename... Args>
    static UniquePtr CreateUnique(Args&&... args);

    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    virtual mrpt::rtti::CObject* clone() const;

    static void generateRingSOG(
        float sensedRange,
        mrpt::poses::CPointPDFSOG& outPDF,
        const CBeaconMap* myBeaconMap,
        const mrpt::poses::CPoint3D& sensorPnt,
        const mrpt::math::CMatrixDouble33* covarianceCompositionToAdd = nullptr,
        bool clearPreviousContentsOutPDF = true,
        const mrpt::poses::CPoint3D& centerPoint = mrpt::poses::CPoint3D(0, 0, 0),
        float maxDistanceFromCenter = 0
        );

    void getMean(mrpt::poses::CPoint3D& mean_point) const;
    virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const;
    virtual void copyFrom(const mrpt::poses::CPointPDF& o);
    virtual bool saveToTextFile(const std::string& file) const;
    virtual void changeCoordinatesReference(const mrpt::poses::CPose3D& newReferenceBase);
    void getVisualizationInto(mrpt::viz::CSetOfObjects& o) const;
    void getAsMatlabDrawCommands(std::vector<std::string>& out_Str) const;
    void drawSingleSample(mrpt::poses::CPoint3D& outSample) const;
    void bayesianFusion(const CPointPDF& p1, const CPointPDF& p2, const double minMahalanobisDistToDrop = 0);

    void generateObservationModelDistribution(
        float sensedRange,
        mrpt::poses::CPointPDFSOG& outPDF,
        const CBeaconMap* myBeaconMap,
        const mrpt::poses::CPoint3D& sensorPntOnRobot,
        const mrpt::poses::CPoint3D& centerPoint = mrpt::poses::CPoint3D(0, 0, 0),
        float maxDistanceFromCenter = 0
        ) const;
};

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

    // enums

    enum
    {
        is_3D_val = 1,
    };

    enum
    {
        is_PDF_val = 1,
    };

    // 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;
    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    virtual void copyFrom(const CPointPDF& o) = 0;
    virtual void bayesianFusion(const CPointPDF& p1, const CPointPDF& p2, const double minMahalanobisDistToDrop = 0) = 0;
    virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0;

    template <class OPENGL_SETOFOBJECTSPTR>
    void getAs3DObject(OPENGL_SETOFOBJECTSPTR& out_obj) const;

    template <class OPENGL_SETOFOBJECTSPTR, class OPENGL_SETOFOBJECTS>
    OPENGL_SETOFOBJECTSPTR getAs3DObject() const;

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

Typedefs

typedef std::shared_ptr<mrpt::maps ::CBeacon> Ptr

A type for the associated smart pointer.

typedef int64_t TBeaconID

The type for the IDs of landmarks.

Fields

TTypePDF m_typePDF {pdfGauss}

Which one of the different 3D point PDF is currently used in this object: montecarlo, gaussian, or a sum of gaussians.

See also:

m_location

mrpt::poses::CPointPDFParticles m_locationMC {1}

The individual PDF, if m_typePDF=pdfMonteCarlo (publicly accessible for ease of use, but the CPointPDF interface is also implemented in CBeacon).

mrpt::poses::CPointPDFGaussian m_locationGauss

The individual PDF, if m_typePDF=pdfGauss (publicly accessible for ease of use, but the CPointPDF interface is also implemented in CBeacon).

mrpt::poses::CPointPDFSOG m_locationSOG {1}

The individual PDF, if m_typePDF=pdfSOG (publicly accessible for ease of use, but the CPointPDF interface is also implemented in CBeacon).

TBeaconID m_ID = mrpt::obs::INVALID_BEACON_ID

An ID for the landmark (see details next…) This ID was introduced in the version 3 of this class (21/NOV/2006), and its aim is to provide a way for easily establishing correspondences between landmarks detected in sequential image frames.

Thus, the management of this field should be:

In ‘servers’ (classes/modules/… that detect landmarks from images): A different ID must be assigned to every landmark (e.g. a sequential counter), BUT only in the case of being sure of the correspondence of one landmark with another one in the past (e.g. tracking).
In ‘clients’: This field can be ignored, but if it is used, the advantage is solving the correspondence between landmarks detected in consequentive instants of time: Two landmarks with the same ID correspond to the same physical feature, BUT it should not be expected the inverse to be always true.

Note that this field is never fill out automatically, it must be set by the programmer if used.

Methods

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

Returns information about the class of an object in runtime.

virtual mrpt::rtti::CObject* clone() const

Returns a deep copy (clone) of the object, indepently of its class.

static void generateRingSOG(
    float sensedRange,
    mrpt::poses::CPointPDFSOG& outPDF,
    const CBeaconMap* myBeaconMap,
    const mrpt::poses::CPoint3D& sensorPnt,
    const mrpt::math::CMatrixDouble33* covarianceCompositionToAdd = nullptr,
    bool clearPreviousContentsOutPDF = true,
    const mrpt::poses::CPoint3D& centerPoint = mrpt::poses::CPoint3D(0, 0, 0),
    float maxDistanceFromCenter = 0
    )

This static method returns a SOG with ring-shape (or as a 3D sphere) that can be used to initialize a beacon if observed the first time.

sensorPnt is the center of the ring/sphere, i.e. the absolute position of the range sensor. If clearPreviousContentsOutPDF=false, the SOG modes will be added to the current contents of outPDF If the 3x3 matrix covarianceCompositionToAdd is provided, it will be add to every Gaussian (to model the composition of uncertainty).

See also:

generateObservationModelDistribution

virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const

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

See also:

getMean, getInformationMatrix

virtual void copyFrom(const mrpt::poses::CPointPDF& o)

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

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

Save PDF’s particles to a text file.

See derived classes for more information about the format of generated files

virtual void changeCoordinatesReference(const mrpt::poses::CPose3D& 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 getVisualizationInto(mrpt::viz::CSetOfObjects& o) const

Saves a 3D representation of the beacon into a given OpenGL scene

void getAsMatlabDrawCommands(std::vector<std::string>& out_Str) const

Gets a set of MATLAB commands which draw the current state of the beacon:

void drawSingleSample(mrpt::poses::CPoint3D& outSample) const

Draw a sample from the pdf.

void bayesianFusion(
    const CPointPDF& p1,
    const CPointPDF& 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!)

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.

void generateObservationModelDistribution(
    float sensedRange,
    mrpt::poses::CPointPDFSOG& outPDF,
    const CBeaconMap* myBeaconMap,
    const mrpt::poses::CPoint3D& sensorPntOnRobot,
    const mrpt::poses::CPoint3D& centerPoint = mrpt::poses::CPoint3D(0, 0, 0),
    float maxDistanceFromCenter = 0
    ) const

Compute the observation model p(z_t|x_t) for a given observation (range value), and return it as an approximate SOG.

Note that if the beacon is a SOG itself, the number of gaussian modes will be square. As a speed-up, if a “center point”+”maxDistanceFromCenter” is supplied (maxDistanceFromCenter!=0), those modes farther than this sphere will be discarded. Parameters such as the stdSigma of the sensor are gathered from “myBeaconMap” The result is one “ring” for each Gaussian mode that represent the beacon position in this object. The position of the sensor on the robot is used to shift the resulting densities such as they represent the position of the robot, not the sensor.