Detailed Description

A Constrained constrained model is a specialization of Diagonal which allows some or all of the sigmas to be zero, forcing the error to be zero there.

All other Gaussian models are guaranteed to have a non-singular square-root information matrix, but this class is specifically equipped to deal with singular noise models, specifically: whiten will return zero on those components that have zero sigma and zero error, unchanged otherwise.

While a hard constraint may seem to be a case in which there is infinite error, we do not ever produce an error value of infinity to allow for constraints to actually be optimized rather than self-destructing if not initialized correctly.

Inheritance diagram for gtsam::noiseModel::Constrained:

Public Member Functions
virtual bool	isConstrained () const
	true if a constrained noise mode, saves slow/clumsy dynamic casting

bool	constrained (size_t i) const
	Return true if a particular dimension is free or constrained.

const Vector &	mu () const
	Access mu as a vector.

virtual double	distance (const Vector &v) const
	The distance function for a constrained noisemodel, for non-constrained versions, uses sigmas, otherwise uses the penalty function with mu.

virtual void	print (const std::string &name) const

virtual Vector	whiten (const Vector &v) const
	Calculates error vector with weights applied.

virtual Matrix	Whiten (const Matrix &H) const
	Whitening functions will perform partial whitening on rows with a non-zero sigma. More...

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

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

virtual Diagonal::shared_ptr	QR (Matrix &Ab) const
	Apply QR factorization to the system [A b], taking into account constraints Q' * [A b] = [R d] Dimensions: (rm) m(n+1) = r(n+1), where r = min(m,n). More...

shared_ptr	unit () const
	Returns a Unit version of a constrained noisemodel in which constrained sigmas remain constrained and the rest are unit scaled.

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

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

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 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 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	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	MixedSigmas (const Vector &mu, const Vector &sigmas)
	A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero.

static shared_ptr	MixedSigmas (const Vector &sigmas)
	A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero.

static shared_ptr	MixedSigmas (double m, const Vector &sigmas)
	A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero.

static shared_ptr	MixedVariances (const Vector &mu, const Vector &variances)
	A diagonal noise model created by specifying a Vector of standard devations, some of which might be zero.

static shared_ptr	MixedVariances (const Vector &variances)

static shared_ptr	MixedPrecisions (const Vector &mu, const Vector &precisions)
	A diagonal noise model created by specifying a Vector of precisions, some of which might be inf.

static shared_ptr	MixedPrecisions (const Vector &precisions)

static shared_ptr	All (size_t dim)
	Fully constrained variations.

static shared_ptr	All (size_t dim, const Vector &mu)
	Fully constrained variations.

static shared_ptr	All (size_t dim, double mu)
	Fully constrained variations with a mu parameter.

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< Constrained >	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
	Constrained (const Vector &sigmas=Z_1x1)
	protected constructor takes sigmas. More...

	Constrained (const Vector &mu, const Vector &sigmas)
	Constructor that prevents any inf values from appearing in invsigmas or precisions. More...

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
Vector	mu_
	Penalty function weight - needs to be large enough to dominate soft constraints.

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.

Constructor & Destructor Documentation

◆ Constrained() [1/2]

gtsam::noiseModel::Constrained::Constrained ( const Vector & sigmas = Z_1x1 )

protected

protected constructor takes sigmas.

prevents any inf values from appearing in invsigmas or precisions. mu set to large default value (1000.0)

◆ Constrained() [2/2]

gtsam::noiseModel::Constrained::Constrained	(	const Vector &	mu,
		const Vector &	sigmas
	)

protected

Constructor that prevents any inf values from appearing in invsigmas or precisions.

Allows for specifying mu.

Member Function Documentation

◆ QR()

SharedDiagonal gtsam::noiseModel::Constrained::QR ( Matrix & Ab ) const

virtual

Apply QR factorization to the system [A b], taking into account constraints Q' * [A b] = [R d] Dimensions: (r*m) * m*(n+1) = r*(n+1), where r = min(m,n).

This routine performs an in-place factorization on Ab. Below-diagonal elements are set to zero by this routine.

Parameters

Ab	is the m*(n+1) augmented system matrix [A b]

Returns: diagonal noise model can be all zeros, mixed, or not-constrained

Reimplemented from gtsam::noiseModel::Gaussian.

◆ Whiten()

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

virtual

Whitening functions will perform partial whitening on rows with a non-zero sigma.

Other rows remain untouched.

Reimplemented from gtsam::noiseModel::Diagonal.

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