gtsam  4.0.0 gtsam
gtsam::noiseModel::Gaussian Class Reference

## Detailed Description

Gaussian implements the mathematical model |R*x|^2 = |y|^2 with R'*R=inv(Sigma) where y = whiten(x) = R*x x = unwhiten(x) = inv(R)*y as indeed |y|^2 = y'*y = x'*R'*R*x Various derived classes are available that are more efficient.

The named constructors return a shared_ptr because, when the smart flag is true, the underlying object might be a derived class such as Diagonal. Inheritance diagram for gtsam::noiseModel::Gaussian:

## Public Member Functions

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

virtual bool equals (const Base &expected, double tol=1e-9) 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 double Mahalanobis (const Vector &v) const
Mahalanobis distance v'*R'*R*v = <R*v,R*v>

virtual double distance (const Vector &v) const

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.

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< DiagonalQR (Matrix &Ab) const
Apply appropriately weighted QR factorization to the system [A b] Q' * [A b] = [R d] Dimensions: (r*m) * m*(n+1) = r*(n+1), where r = min(m,n). More...

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

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

## Protected Member Functions

Gaussian (size_t dim=1, const boost::optional< Matrix > &sqrt_information=boost::none)
protected constructor takes square root information matrix

## Protected Attributes

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.

## ◆ Covariance()

 Gaussian::shared_ptr gtsam::noiseModel::Gaussian::Covariance ( const Matrix & covariance, bool smart = true )
static

A Gaussian noise model created by specifying a covariance matrix.

Parameters
 covariance The square covariance Matrix smart check if can be simplified to derived class

## ◆ Information()

 Gaussian::shared_ptr gtsam::noiseModel::Gaussian::Information ( const Matrix & M, bool smart = true )
static

A Gaussian noise model created by specifying an information matrix.

Parameters
 M The information matrix smart check if can be simplified to derived class

## ◆ QR()

 SharedDiagonal gtsam::noiseModel::Gaussian::QR ( Matrix & Ab ) const
virtual

Apply appropriately weighted QR factorization to the system [A b] 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
Empty SharedDiagonal() noise model: R,d are whitened

Reimplemented in gtsam::noiseModel::Constrained.

## ◆ SqrtInformation()

 Gaussian::shared_ptr gtsam::noiseModel::Gaussian::SqrtInformation ( const Matrix & R, bool smart = true )
static

A Gaussian noise model created by specifying a square root information matrix.

Parameters
 R The (upper-triangular) square root information matrix smart check if can be simplified to derived class

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