#include <mrpt/poses/CPoint2DPDFGaussian.h>
Public Member Functions | |
CPoint2DPDFGaussian () | |
Default constructor. | |
CPoint2DPDFGaussian (const CPoint2D &init_Mean) | |
Constructor. | |
CPoint2DPDFGaussian (const CPoint2D &init_Mean, const CMatrixDouble22 &init_Cov) | |
Constructor. | |
void | getMean (CPoint2D &p) const |
Returns an estimate of the point, (the mean, or mathematical expectation of the PDF). | |
void | getCovarianceAndMean (CMatrixDouble22 &cov, CPoint2D &mean_point) const |
Returns an estimate of the point covariance matrix (2x2 cov matrix) and the mean, both at once. | |
void | copyFrom (const CPoint2DPDF &o) |
Copy operator, translating if necesary (for example, between particles and gaussian representations). | |
void | saveToTextFile (const std::string &file) const |
Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines. | |
void | changeCoordinatesReference (const CPose3D &newReferenceBase) |
This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which "to project" the current pdf. | |
void | bayesianFusion (const CPoint2DPDFGaussian &p1, const CPoint2DPDFGaussian &p2) |
Bayesian fusion of two points gauss. | |
double | productIntegralWith (const CPoint2DPDFGaussian &p) const |
Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF. | |
double | productIntegralNormalizedWith (const CPoint2DPDFGaussian &p) const |
Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF. | |
void | drawSingleSample (CPoint2D &outSample) const |
Draw a sample from the pdf. | |
void | bayesianFusion (const CPoint2DPDF &p1, const CPoint2DPDF &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!). | |
double | mahalanobisDistanceTo (const CPoint2DPDFGaussian &other) const |
Returns the Mahalanobis distance from this PDF to another PDF, that is, it's evaluation at (0,0,0). | |
Public Attributes | |
CPoint2D | mean |
The mean value. | |
CMatrixDouble22 | cov |
The 2x2 covariance matrix. |
Also a method for bayesian fusion is provided.
Definition at line 45 of file CPoint2DPDFGaussian.h.
mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian | ( | ) |
Default constructor.
mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian | ( | const CPoint2D & | init_Mean | ) |
Constructor.
mrpt::poses::CPoint2DPDFGaussian::CPoint2DPDFGaussian | ( | const CPoint2D & | init_Mean, | |
const CMatrixDouble22 & | init_Cov | |||
) |
Constructor.
void mrpt::poses::CPoint2DPDFGaussian::bayesianFusion | ( | const CPoint2DPDF & | p1, | |
const CPoint2DPDF & | p2, | |||
const double & | minMahalanobisDistToDrop = 0 | |||
) | [virtual] |
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!).
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. |
Implements mrpt::poses::CPoint2DPDF.
void mrpt::poses::CPoint2DPDFGaussian::bayesianFusion | ( | const CPoint2DPDFGaussian & | p1, | |
const CPoint2DPDFGaussian & | p2 | |||
) |
Bayesian fusion of two points gauss.
distributions, then save the result in this object. The process is as follows:
S = (S1-1 + S2-1)-1; x = S * ( S1-1*x1 + S2-1*x2 );
void mrpt::poses::CPoint2DPDFGaussian::changeCoordinatesReference | ( | const CPose3D & | newReferenceBase | ) | [virtual] |
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. Both the mean value and the covariance matrix are updated correctly.
Implements mrpt::utils::CProbabilityDensityFunction< CPoint2D, 2 >.
void mrpt::poses::CPoint2DPDFGaussian::copyFrom | ( | const CPoint2DPDF & | o | ) | [virtual] |
Copy operator, translating if necesary (for example, between particles and gaussian representations).
Implements mrpt::poses::CPoint2DPDF.
void mrpt::poses::CPoint2DPDFGaussian::drawSingleSample | ( | CPoint2D & | outSample | ) | const |
Draw a sample from the pdf.
void mrpt::poses::CPoint2DPDFGaussian::getCovarianceAndMean | ( | CMatrixDouble22 & | cov, | |
CPoint2D & | mean_point | |||
) | const [inline] |
Returns an estimate of the point covariance matrix (2x2 cov matrix) and the mean, both at once.
Definition at line 80 of file CPoint2DPDFGaussian.h.
void mrpt::poses::CPoint2DPDFGaussian::getMean | ( | CPoint2D & | p | ) | const [inline] |
Returns an estimate of the point, (the mean, or mathematical expectation of the PDF).
Definition at line 73 of file CPoint2DPDFGaussian.h.
double mrpt::poses::CPoint2DPDFGaussian::mahalanobisDistanceTo | ( | const CPoint2DPDFGaussian & | other | ) | const |
Returns the Mahalanobis distance from this PDF to another PDF, that is, it's evaluation at (0,0,0).
double mrpt::poses::CPoint2DPDFGaussian::productIntegralNormalizedWith | ( | const CPoint2DPDFGaussian & | p | ) | const |
Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.
The resulting number is in the range [0,1]. Note that the resulting value is in fact
, with being the square Mahalanobis distance between the two pdfs.
std::exception | On errors like covariance matrix with null determinant, etc... |
double mrpt::poses::CPoint2DPDFGaussian::productIntegralWith | ( | const CPoint2DPDFGaussian & | p | ) | const |
Computes the "correspondence likelihood" of this PDF with another one: This is implemented as the integral from -inf to +inf of the product of both PDF.
The resulting number is >=0.
std::exception | On errors like covariance matrix with null determinant, etc... |
void mrpt::poses::CPoint2DPDFGaussian::saveToTextFile | ( | const std::string & | file | ) | const [virtual] |
Save PDF's particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.
Implements mrpt::utils::CProbabilityDensityFunction< CPoint2D, 2 >.
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