# class mrpt::poses::CPosePDF¶

Declares a class that represents a probability density function (pdf) of a 2D pose (x,y,phi).

This class is just the base class for unifying many diferent 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.

#include <mrpt/poses/CPosePDF.h>

class CPosePDF:
public mrpt::serialization::CSerializable,
public mrpt::math::CProbabilityDensityFunction
{
public:
//
methods

virtual void copyFrom(const CPosePDF& o) = 0;

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

virtual void inverse(CPosePDF& o) const = 0;
virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0;
};

// direct descendants

class CPosePDFGaussian;
class CPosePDFGaussianInf;
class CPosePDFGrid;
class CPosePDFParticles;
class CPosePDFSOG;

## Inherited Members¶

public:
// typedefs

typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t;

## Methods¶

virtual void copyFrom(const CPosePDF& o) = 0

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

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

Bayesian fusion of two pose 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.
virtual void inverse(CPosePDF& o) const = 0

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