Detailed Description

A diagonal noise model implements a diagonal covariance matrix, with the elements of the diagonal specified in a Vector.

This class has no public constructors, instead, use the static constructor functions Sigmas etc...

Inheritance diagram for gtsam::noiseModel::Diagonal:

Public Member Functions
virtual void	print (const std::string &name) const

virtual Vector	sigmas () const
	Calculate standard deviations.

virtual Vector	whiten (const Vector &v) const
	Whiten an error vector.

virtual Vector	unwhiten (const Vector &v) const
	Unwhiten an error vector.

virtual Matrix	Whiten (const Matrix &H) const
	Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix.

virtual void	WhitenInPlace (Matrix &H) const
	In-place version.

virtual void	WhitenInPlace (Eigen::Block< Matrix > H) const
	In-place version.

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

virtual Matrix	R () const
	Return R itself, but note that Whiten(H) is cheaper than R*H.

Public Member Functions inherited from gtsam::noiseModel::Gaussian
virtual bool	equals (const Base &expected, double tol=1e-9) const

virtual double	Mahalanobis (const Vector &v) const
	Mahalanobis distance v'R'Rv = <Rv,R*v>

virtual double	distance (const Vector &v) const

virtual void	WhitenSystem (std::vector< Matrix > &A, Vector &b) const
	Whiten a system, in place as well.

virtual void	WhitenSystem (Matrix &A, Vector &b) const

virtual void	WhitenSystem (Matrix &A1, Matrix &A2, Vector &b) const

virtual void	WhitenSystem (Matrix &A1, Matrix &A2, Matrix &A3, Vector &b) const

virtual boost::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). More...

virtual Matrix	information () const
	Compute information matrix.

virtual Matrix	covariance () const
	Compute covariance matrix.

Public Member Functions inherited from gtsam::noiseModel::Base
	Base (size_t dim=1)
	primary constructor More...

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 void	whitenInPlace (Vector &v) const
	in-place whiten, override if can be done more efficiently

virtual void	unwhitenInPlace (Vector &v) const
	in-place unwhiten, override if can be done more efficiently

virtual void	whitenInPlace (Eigen::Block< Vector > &v) const
	in-place whiten, override if can be done more efficiently

virtual void	unwhitenInPlace (Eigen::Block< Vector > &v) const
	in-place unwhiten, override if can be done more efficiently

Static Public Member Functions
static shared_ptr	Sigmas (const Vector &sigmas, bool smart=true)
	A diagonal noise model created by specifying a Vector of sigmas, i.e. More...

static shared_ptr	Variances (const Vector &variances, bool smart=true)
	A diagonal noise model created by specifying a Vector of variances, i.e. More...

static shared_ptr	Precisions (const Vector &precisions, bool smart=true)
	A diagonal noise model created by specifying a Vector of precisions, i.e. More...

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. More...

static shared_ptr	Information (const Matrix &M, bool smart=true)
	A Gaussian noise model created by specifying an information matrix. More...

static shared_ptr	Covariance (const Matrix &covariance, bool smart=true)
	A Gaussian noise model created by specifying a covariance matrix. More...

Public Types
typedef boost::shared_ptr< Diagonal >	shared_ptr

Public Types inherited from gtsam::noiseModel::Gaussian
typedef boost::shared_ptr< Gaussian >	shared_ptr

Public Types inherited from gtsam::noiseModel::Base
typedef boost::shared_ptr< Base >	shared_ptr

Protected Member Functions
	Diagonal ()
	protected constructor - no initializations

	Diagonal (const Vector &sigmas)
	constructor to allow for disabling initialization of invsigmas

Protected Member Functions inherited from gtsam::noiseModel::Gaussian
	Gaussian (size_t dim=1, const boost::optional< Matrix > &sqrt_information=boost::none)
	protected constructor takes square root information matrix

Protected Attributes
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
boost::optional< Matrix >	sqrt_information_
	Matrix square root of information matrix (R)

Protected Attributes inherited from gtsam::noiseModel::Base
size_t	dim_

Friends
class	boost::serialization::access
	Serialization function.

Member Function Documentation

◆ Precisions()

static shared_ptr gtsam::noiseModel::Diagonal::Precisions	(	const Vector &	precisions,
		bool	smart = `true`
	)

inlinestatic

A diagonal noise model created by specifying a Vector of precisions, i.e.

i.e. the diagonal of the information matrix, i.e., weights

◆ Sigmas()

Diagonal::shared_ptr gtsam::noiseModel::Diagonal::Sigmas	(	const Vector &	sigmas,
		bool	smart = `true`
	)

static

A diagonal noise model created by specifying a Vector of sigmas, i.e.

standard deviations, the diagonal of the square root covariance matrix.

◆ Variances()

Diagonal::shared_ptr gtsam::noiseModel::Diagonal::Variances	(	const Vector &	variances,
		bool	smart = `true`
	)

static

A diagonal noise model created by specifying a Vector of variances, i.e.

i.e. the diagonal of the covariance matrix.

Parameters

variances	A vector containing the variances of this noise model
smart	check if can be simplified to derived class

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

/Users/dellaert/git/gtsam/gtsam/linear/NoiseModel.h
/Users/dellaert/git/gtsam/gtsam/linear/NoiseModel.cpp

Detailed Description

Public Member Functions

Static Public Member Functions

Public Types

Protected Member Functions

Protected Attributes

Friends

Member Function Documentation

◆ Precisions()

◆ Sigmas()

◆ Variances()