Class CBeacon

Inheritance Relationships

Base Type

  • public mrpt::poses::CPointPDF

Class Documentation

class CBeacon : public mrpt::poses::CPointPDF

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

Public Types

enum TTypePDF

See m_typePDF

Values:

enumerator pdfMonteCarlo
enumerator pdfGauss
enumerator pdfSOG
using TBeaconID = int64_t

The type for the IDs of landmarks.

Public Functions

void getMean(mrpt::poses::CPoint3D &mean_point) const override
std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override
virtual void copyFrom(const mrpt::poses::CPointPDF &o) override

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

void changeCoordinatesReference(const mrpt::poses::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 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 override

Draw a sample from the pdf.

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

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.

See also

CBeaconMap::insertionOptions, generateRingSOG

Public Members

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.

Public Static Functions

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