class mrpt::poses::CPoint2DPDFGaussian
Overview
A gaussian distribution for 2D points.
Also a method for bayesian fusion is provided.
See also:
#include <mrpt/poses/CPoint2DPDFGaussian.h> class CPoint2DPDFGaussian: public mrpt::poses::CPoint2DPDF { public: // typedefs typedef std::shared_ptr<mrpt::poses ::CPoint2DPDFGaussian> Ptr; typedef std::shared_ptr<const mrpt::poses ::CPoint2DPDFGaussian> ConstPtr; typedef std::unique_ptr<mrpt::poses ::CPoint2DPDFGaussian> UniquePtr; typedef std::unique_ptr<const mrpt::poses ::CPoint2DPDFGaussian> ConstUniquePtr; // enums enum { is_3D_val = 0, }; enum { is_PDF_val = 1, }; // fields static constexpr const char* className = "mrpt::poses" "::" "CPoint2DPDFGaussian"; CPoint2D mean; mrpt::math::CMatrixDouble22 cov; // construction CPoint2DPDFGaussian(); CPoint2DPDFGaussian(const CPoint2D& init_Mean); CPoint2DPDFGaussian(const CPoint2D& init_Mean, const mrpt::math::CMatrixDouble22& init_Cov); // 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 constexpr bool is_3D(); static constexpr bool is_PDF(); void getMean(CPoint2D& p) const; virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const; virtual void copyFrom(const CPoint2DPDF& o); virtual bool saveToTextFile(const std::string& file) const; virtual void changeCoordinatesReference(const CPose3D& newReferenceBase); void bayesianFusion(const CPoint2DPDFGaussian& p1, const CPoint2DPDFGaussian& p2); double productIntegralWith(const CPoint2DPDFGaussian& p) const; double productIntegralNormalizedWith(const CPoint2DPDFGaussian& p) const; void drawSingleSample(CPoint2D& outSample) const; virtual void bayesianFusion(const CPoint2DPDF& p1, const CPoint2DPDF& p2, const double minMahalanobisDistToDrop = 0); double mahalanobisDistanceTo(const CPoint2DPDFGaussian& other) const; double mahalanobisDistanceToPoint(const double x, const double y) const; };
Inherited Members
public: // typedefs typedef std::shared_ptr<CObject> Ptr; typedef std::shared_ptr<const CObject> ConstPtr; typedef std::shared_ptr<CSerializable> Ptr; typedef std::shared_ptr<const CSerializable> ConstPtr; typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t; typedef std::shared_ptr<CPoint2DPDF> Ptr; typedef std::shared_ptr<const CPoint2DPDF> ConstPtr; // methods static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic(); virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const; virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const; static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic(); CProbabilityDensityFunction& operator = (const CProbabilityDensityFunction&); CProbabilityDensityFunction& operator = (CProbabilityDensityFunction&&); virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const; static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic(); CPoint2DPDF& operator = (const CPoint2DPDF&); CPoint2DPDF& operator = (CPoint2DPDF&&); virtual void copyFrom(const CPoint2DPDF& o) = 0; virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0; virtual void bayesianFusion(const CPoint2DPDF& p1, const CPoint2DPDF& p2, const double minMahalanobisDistToDrop = 0) = 0; virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const = 0; virtual bool saveToTextFile(const std::string& file) const = 0;
Typedefs
typedef std::shared_ptr<mrpt::poses ::CPoint2DPDFGaussian> Ptr
A type for the associated smart pointer.
Fields
CPoint2D mean
The mean value.
mrpt::math::CMatrixDouble22 cov
The 2x2 covariance matrix.
Construction
CPoint2DPDFGaussian()
Default constructor.
CPoint2DPDFGaussian(const CPoint2D& init_Mean)
Constructor.
CPoint2DPDFGaussian(const CPoint2D& init_Mean, const mrpt::math::CMatrixDouble22& init_Cov)
Constructor.
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.
void getMean(CPoint2D& p) const
Returns an estimate of the point, (the mean, or mathematical expectation of the PDF)
virtual std::tuple<cov_mat_t, type_value> getCovarianceAndMean() const
Returns an estimate of the point covariance matrix (2x2 cov matrix) and the mean, both at once.
See also:
virtual void copyFrom(const CPoint2DPDF& 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, containing the 2D pose in the first line, then the covariance matrix in next 3 lines.
virtual void changeCoordinatesReference(const 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. Both the mean value and the covariance matrix are updated correctly.
void 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:
(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.
\(S = (S_1^{-1} + S_2^{-1})^{-1}\) \(x = S ( S_1^{-1} x_1 + S_2^{-1} x_2 )\)
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.
The resulting number is >=0.
Parameters:
std::exception |
On errors like covariance matrix with null determinant, etc… |
See also:
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.
The resulting number is in the range [0,1]. Note that the resulting value is in fact
, with \(D^2\) being the square Mahalanobis distance between the two pdfs.
Parameters:
std::exception |
On errors like covariance matrix with null determinant, etc… |
See also:
void drawSingleSample(CPoint2D& outSample) const
Draw a sample from the pdf.
virtual 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!)
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. |
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)
double mahalanobisDistanceToPoint(const double x, const double y) const
Returns the Mahalanobis distance from this PDF to some point.