Detailed Description

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

Inheritance diagram for gtsam::noiseModel::Isotropic:

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

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

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

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

double	sigma () const
	Return standard deviation.

Public Member Functions inherited from gtsam::noiseModel::Diagonal
virtual Vector	sigmas () const
	Calculate standard deviations.

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	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	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	Sigma (size_t dim, double sigma, bool smart=true)
	An isotropic noise model created by specifying a standard devation sigma.

static shared_ptr	Variance (size_t dim, double variance, bool smart=true)
	An isotropic noise model created by specifying a variance = sigma^2. More...

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. 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< Isotropic >	shared_ptr

Public Types inherited from gtsam::noiseModel::Diagonal
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
	Isotropic (size_t dim, double sigma)
	protected constructor takes sigma

Protected Member Functions inherited from gtsam::noiseModel::Diagonal
	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
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
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

◆ 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

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

◆ Variance()