Detailed Description

An isotropic noise model corresponds to a scaled diagonal covariance To construct, use one of the static methods.

Inheritance diagram for gtsam::Isotropic:

Public Member Functions
void	print (const std::string &name) const override
double	squaredMahalanobisDistance (const Vector &v) const override
	Squared Mahalanobis distance v'R'Rv = <Rv,R*v>.
Vector	whiten (const Vector &v) const override
	Whiten an error vector.
Vector	unwhiten (const Vector &v) const override
	Unwhiten an error vector.
Matrix	Whiten (const Matrix &H) const override
	Whiten a matrix.
void	WhitenInPlace (Matrix &H) const override
	In-place version.
void	WhitenInPlace (Eigen::Block< Matrix > H) const override
	In-place version.
void	whitenInPlace (Vector &v) const override
	in-place whiten, override if can be done more efficiently
void	unwhitenInPlace (Vector &v) const override
	in-place unwhiten, override if can be done more efficiently
void	unwhitenInPlace (Eigen::Block< Vector > &v) const override
	in-place unwhiten, override if can be done more efficiently
double	sigma () const
	Return standard deviation.
Public Member Functions inherited from gtsam::noiseModel::Diagonal
	Diagonal ()
	constructor - no initializations, for serialization
Vector	sigmas () const override
	Calculate standard deviations.
const Vector &	sigmasRef () const
	Return standard deviations without copying.
void	whitenInPlace (Eigen::Block< Vector > &v) const override
	in-place whiten, override if can be done more efficiently
double	sigma (size_t i) const
	Return standard deviations (sqrt of diagonal).
const Vector &	invsigmas () const
	Return sqrt precisions.
double	invsigma (size_t i) const
const Vector &	precisions () const
	Return precisions.
double	precision (size_t i) const
Matrix	R () const override
	Return R itself, but note that Whiten(H) is cheaper than R*H.
Public Member Functions inherited from gtsam::noiseModel::Gaussian
	Gaussian (size_t dim=1, const std::optional< Matrix > &sqrt_information={})
	constructor takes square root information matrix
bool	equals (const Base &expected, double tol=1e-9) const override
Vector	sigmas () const override
	Calculate standard deviations.
void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const override
	Whiten a system, in place as well.
void	WhitenSystem (Matrix &A, Vector &b) const override
void	WhitenSystem (Matrix &A1, Matrix &A2, Vector &b) const override
void	WhitenSystem (Matrix &A1, Matrix &A2, Matrix &A3, Vector &b) const override
virtual std::shared_ptr< Diagonal >	QR (Matrix &Ab) const
	Apply appropriately weighted QR factorization to the system [A b] Q' * [A b] = [R d] Dimensions: (rm) m(n+1) = r(n+1), where r = min(m,n).
virtual Matrix	information () const
	Compute information matrix.
virtual Matrix	covariance () const
	Compute covariance matrix.
double	logDeterminant () const
	Compute the log of \|Σ\|.
double	negLogConstant () const
	Compute the negative log of the normalization constant for a Gaussian noise model k = 1/\sqrt(\|2πΣ\|).
Public Member Functions inherited from gtsam::noiseModel::Base
	Base (size_t dim=1)
	primary constructor
virtual bool	isConstrained () const
	true if a constrained noise model, saves slow/clumsy dynamic casting
virtual bool	isUnit () const
	true if a unit noise model, saves slow/clumsy dynamic casting
size_t	dim () const
	Dimensionality.
virtual double	mahalanobisDistance (const Vector &v) const
	Mahalanobis distance.
virtual double	loss (const double squared_distance) const
	Loss function, input is squared Mahalanobis distance.
virtual double	loss (const Vector &v) const
	Evaluate the loss of an unwhitened residual v.
virtual Vector	unweightedWhiten (const Vector &v) const
	Useful function for robust noise models to get the unweighted but whitened error.
virtual double	weight (const Vector &v) const
	get the weight from the effective loss function on residual vector v

Static Public Member Functions
static shared_ptr	Sigma (size_t dim, double sigma, bool smart=true)
	An isotropic noise model created by specifying a standard deviation sigma.
static shared_ptr	Variance (size_t dim, double variance, bool smart=true)
	An isotropic noise model created by specifying a variance = sigma^2.
static shared_ptr	Precision (size_t dim, double precision, bool smart=true)
	An isotropic noise model created by specifying a precision.
Static Public Member Functions inherited from gtsam::noiseModel::Diagonal
static shared_ptr	Sigmas (const Vector &sigmas, bool smart=true)
	A diagonal noise model created by specifying a Vector of sigmas, i.e.
static shared_ptr	Variances (const Vector &variances, bool smart=true)
	A diagonal noise model created by specifying a Vector of variances, i.e.
static shared_ptr	Precisions (const Vector &precisions, bool smart=true)
	A diagonal noise model created by specifying a Vector of precisions, i.e.
Static Public Member Functions inherited from gtsam::noiseModel::Gaussian
static shared_ptr	SqrtInformation (const Matrix &R, bool smart=true)
	A Gaussian noise model created by specifying a square root information matrix.
static shared_ptr	Information (const Matrix &M, bool smart=true)
	A Gaussian noise model created by specifying an information matrix.
static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)
	A Gaussian noise model created by specifying a covariance matrix.

Public Types
typedef std::shared_ptr< Isotropic >	shared_ptr
Public Types inherited from gtsam::noiseModel::Diagonal
typedef std::shared_ptr< Diagonal >	shared_ptr
Public Types inherited from gtsam::noiseModel::Gaussian
typedef std::shared_ptr< Gaussian >	shared_ptr
Public Types inherited from gtsam::noiseModel::Base
typedef std::shared_ptr< Base >	shared_ptr

Protected Member Functions
	Isotropic (size_t dim, double sigma)
	protected constructor takes sigma
virtual double	logDetR () const override
	Compute the log of \|R\|. Used for computing log(\|Σ\|).
Protected Member Functions inherited from gtsam::noiseModel::Diagonal
	Diagonal (const Vector &sigmas)
	constructor to allow for disabling initialization of invsigmas

Protected Attributes
double	sigma_
double	invsigma_
Protected Attributes inherited from gtsam::noiseModel::Diagonal
Vector	sigmas_
	Standard deviations (sigmas), their inverse and inverse square (weights/precisions) These are all computed at construction: the idea is to use one shared model where computation is done only once, the common use case in many problems.
Vector	invsigmas_
Vector	precisions_
Protected Attributes inherited from gtsam::noiseModel::Gaussian
std::optional< Matrix >	sqrt_information_
	Matrix square root of information matrix (R).
Protected Attributes inherited from gtsam::noiseModel::Base
size_t	dim_

Member Function Documentation

◆ logDetR()

double gtsam::noiseModel::Isotropic::logDetR ( ) const

overrideprotectedvirtual

Compute the log of |R|. Used for computing log(|Σ|).

Reimplemented from gtsam::noiseModel::Diagonal.

◆ print()

void gtsam::noiseModel::Isotropic::print ( const std::string & name ) const

overridevirtual

Reimplemented from gtsam::noiseModel::Diagonal.

◆ squaredMahalanobisDistance()

double gtsam::noiseModel::Isotropic::squaredMahalanobisDistance ( const Vector & v ) const

overridevirtual

Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v>.

Reimplemented from gtsam::noiseModel::Base.

◆ unwhiten()

Vector gtsam::noiseModel::Isotropic::unwhiten ( const Vector & v ) const

overridevirtual

Unwhiten an error vector.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ unwhitenInPlace() [1/2]

void gtsam::noiseModel::Isotropic::unwhitenInPlace ( Eigen::Block< Vector > & v ) const

overridevirtual

in-place unwhiten, override if can be done more efficiently

Reimplemented from gtsam::noiseModel::Diagonal.

◆ unwhitenInPlace() [2/2]

void gtsam::noiseModel::Isotropic::unwhitenInPlace ( Vector & v ) const

overridevirtual

in-place unwhiten, override if can be done more efficiently

Reimplemented from gtsam::noiseModel::Diagonal.

◆ Variance()

Isotropic::shared_ptr gtsam::noiseModel::Isotropic::Variance	(	size_t	dim,
		double	variance,
		bool	smart = true )

static

An isotropic noise model created by specifying a variance = sigma^2.

Parameters

dim	The dimension of this noise model
variance	The variance of this noise model
smart	check if can be simplified to derived class

◆ Whiten()

Matrix gtsam::noiseModel::Isotropic::Whiten ( const Matrix & H ) const

overridevirtual

Whiten a matrix.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ whiten()

Vector gtsam::noiseModel::Isotropic::whiten ( const Vector & v ) const

overridevirtual

Whiten an error vector.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ WhitenInPlace() [1/2]

void gtsam::noiseModel::Isotropic::WhitenInPlace ( Eigen::Block< Matrix > H ) const

overridevirtual

In-place version.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ WhitenInPlace() [2/2]

void gtsam::noiseModel::Isotropic::WhitenInPlace ( Matrix & H ) const

overridevirtual

In-place version.

Reimplemented from gtsam::noiseModel::Diagonal.

◆ whitenInPlace()

void gtsam::noiseModel::Isotropic::whitenInPlace ( Vector & v ) const

overridevirtual

in-place whiten, override if can be done more efficiently

Reimplemented from gtsam::noiseModel::Diagonal.

The documentation for this class was generated from the following files:

/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/NoiseModel.h
/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/NoiseModel.cpp

Detailed Description

Public Member Functions

Static Public Member Functions

Public Types

Protected Member Functions

Protected Attributes

Member Function Documentation

◆ logDetR()

◆ print()

◆ squaredMahalanobisDistance()

◆ unwhiten()

◆ unwhitenInPlace() [1/2]

◆ unwhitenInPlace() [2/2]

◆ Variance()

◆ Whiten()

◆ whiten()

◆ WhitenInPlace() [1/2]

◆ WhitenInPlace() [2/2]

◆ whitenInPlace()