Class CPointPDFGaussian
Defined in File CPointPDFGaussian.h
Inheritance Relationships
Base Type
public mrpt::poses::CPointPDF(Class CPointPDF)
Class Documentation
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class CPointPDFGaussian : public mrpt::poses::CPointPDF
A gaussian distribution for 3D points. Also a method for bayesian fusion is provided.
See also
Public Functions
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CPointPDFGaussian()
Default constructor
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CPointPDFGaussian(const CPoint3D &init_Mean, const mrpt::math::CMatrixDouble33 &init_Cov)
Constructor
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inline std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const override
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virtual void copyFrom(const CPointPDF &o) override
Copy operator, translating if necessary (for example, between particles and gaussian representations)
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bool saveToTextFile(const std::string &file) const override
Save PDF’s particles to a text file, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.
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virtual void changeCoordinatesReference(const 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. Both the mean value and the covariance matrix are updated correctly.
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void bayesianFusion(const CPointPDFGaussian &p1, const CPointPDFGaussian &p2)
Bayesian fusion of two points gauss. distributions, then save the result in this object. The process is as follows:
(x1,S1): Mean and variance of the p1 distribution.
(x2,S2): Mean and variance of the p2 distribution.
(x,S): Mean and variance of the resulting distribution.
\( S = (S_1^{-1} + S_2^{-1})^{-1} \) \( x = S ( S_1^{-1} x_1 + S_2^{-1} x_2 ) \)
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double productIntegralWith(const CPointPDFGaussian &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.
See also
- Throws:
std::exception – On errors like covariance matrix with null determinant, etc…
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double productIntegralWith2D(const CPointPDFGaussian &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. NOTE: This version ignores the “z” coordinates!!
See also
- Throws:
std::exception – On errors like covariance matrix with null determinant, etc…
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double productIntegralNormalizedWith(const CPointPDFGaussian &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
\[ exp( -\frac{1}{2} D^2 ) \], with \( D^2 \) being the square Mahalanobis distance between the two pdfs.See also
- Throws:
std::exception – On errors like covariance matrix with null determinant, etc…
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double productIntegralNormalizedWith2D(const CPointPDFGaussian &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]. This versions ignores the “z” coordinate.
Note that the resulting value is in fact
\[ exp( -\frac{1}{2} D^2 ) \], with \( D^2 \) being the square Mahalanobis distance between the two pdfs.See also
- Throws:
std::exception – On errors like covariance matrix with null determinant, etc…
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virtual 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.
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double mahalanobisDistanceTo(const CPointPDFGaussian &other, bool only_2D = false) const
Returns the Mahalanobis distance from this PDF to another PDF, that is, it’s evaluation at (0,0,0)
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CPointPDFGaussian()