gtsam 4.1.1
gtsam
gtsam::noiseModel::Diagonal Class Reference

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

 Diagonal ()
 constructor - no initializations, for serialization
 
void print (const std::string &name) const override
 
Vector sigmas () const override
 Calculate standard deviations. More...
 
Vector whiten (const Vector &v) const override
 Whiten an error vector. More...
 
Vector unwhiten (const Vector &v) const override
 Unwhiten an error vector. More...
 
Matrix Whiten (const Matrix &H) const override
 Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix. More...
 
void WhitenInPlace (Matrix &H) const override
 In-place version. More...
 
void WhitenInPlace (Eigen::Block< Matrix > H) const override
 In-place version. More...
 
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. More...
 
- Public Member Functions inherited from gtsam::noiseModel::Gaussian
 Gaussian (size_t dim=1, const boost::optional< Matrix > &sqrt_information=boost::none)
 constructor takes square root information matrix
 
void print (const std::string &name) const override
 
bool equals (const Base &expected, double tol=1e-9) const override
 
Vector sigmas () const override
 Calculate standard deviations. More...
 
Vector whiten (const Vector &v) const override
 Whiten an error vector. More...
 
Vector unwhiten (const Vector &v) const override
 Unwhiten an error vector. More...
 
Matrix Whiten (const Matrix &H) const override
 Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix. More...
 
virtual void WhitenInPlace (Matrix &H) const
 In-place version. More...
 
virtual void WhitenInPlace (Eigen::Block< Matrix > H) const
 In-place version. More...
 
void WhitenSystem (std::vector< Matrix > &A, Vector &b) const override
 Whiten a system, in place as well. More...
 
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 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. 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 More...
 
virtual bool isUnit () const
 true if a unit noise model, saves slow/clumsy dynamic casting More...
 
size_t dim () const
 Dimensionality.
 
virtual void print (const std::string &name="") const =0
 
virtual bool equals (const Base &expected, double tol=1e-9) const =0
 
virtual Vector sigmas () const
 Calculate standard deviations. More...
 
virtual Vector whiten (const Vector &v) const =0
 Whiten an error vector. More...
 
virtual Matrix Whiten (const Matrix &H) const =0
 Whiten a matrix. More...
 
virtual Vector unwhiten (const Vector &v) const =0
 Unwhiten an error vector. More...
 
virtual double squaredMahalanobisDistance (const Vector &v) const
 Squared Mahalanobis distance v'*R'*R*v = <R*v,R*v> More...
 
virtual double mahalanobisDistance (const Vector &v) const
 Mahalanobis distance.
 
virtual double loss (const double squared_distance) const
 loss function, input is Mahalanobis distance More...
 
virtual void WhitenSystem (std::vector< Matrix > &A, Vector &b) const =0
 
virtual void WhitenSystem (Matrix &A, Vector &b) const =0
 
virtual void WhitenSystem (Matrix &A1, Matrix &A2, Vector &b) const =0
 
virtual void WhitenSystem (Matrix &A1, Matrix &A2, Matrix &A3, Vector &b) const =0
 
virtual void whitenInPlace (Vector &v) const
 in-place whiten, override if can be done more efficiently More...
 
virtual void unwhitenInPlace (Vector &v) const
 in-place unwhiten, override if can be done more efficiently More...
 
virtual void whitenInPlace (Eigen::Block< Vector > &v) const
 in-place whiten, override if can be done more efficiently More...
 
virtual void unwhitenInPlace (Eigen::Block< Vector > &v) const
 in-place unwhiten, override if can be done more efficiently More...
 
virtual Vector unweightedWhiten (const Vector &v) const
 Useful function for robust noise models to get the unweighted but whitened error. More...
 
virtual double weight (const Vector &v) const
 get the weight from the effective loss function on residual vector v More...
 

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

Protected Member Functions

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

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

◆ print()

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

Reimplemented from gtsam::noiseModel::Gaussian.

◆ R()

Matrix gtsam::noiseModel::Diagonal::R ( ) const
inlineoverridevirtual

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

Reimplemented from gtsam::noiseModel::Gaussian.

◆ sigmas()

Vector gtsam::noiseModel::Diagonal::sigmas ( ) const
inlineoverridevirtual

Calculate standard deviations.

Reimplemented from gtsam::noiseModel::Gaussian.

◆ 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.

◆ unwhiten()

Vector gtsam::noiseModel::Diagonal::unwhiten ( const Vector &  v) const
overridevirtual

Unwhiten an error vector.

Reimplemented from gtsam::noiseModel::Gaussian.

Reimplemented in gtsam::noiseModel::Isotropic, and gtsam::noiseModel::Unit.

◆ 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
variancesA vector containing the variances of this noise model
smartcheck if can be simplified to derived class

◆ Whiten()

Matrix gtsam::noiseModel::Diagonal::Whiten ( const Matrix &  H) const
overridevirtual

Multiply a derivative with R (derivative of whiten) Equivalent to whitening each column of the input matrix.

Reimplemented from gtsam::noiseModel::Gaussian.

Reimplemented in gtsam::noiseModel::Constrained, gtsam::noiseModel::Isotropic, and gtsam::noiseModel::Unit.

◆ whiten()

Vector gtsam::noiseModel::Diagonal::whiten ( const Vector &  v) const
overridevirtual

Whiten an error vector.

Reimplemented from gtsam::noiseModel::Gaussian.

Reimplemented in gtsam::noiseModel::Constrained, gtsam::noiseModel::Isotropic, and gtsam::noiseModel::Unit.

◆ WhitenInPlace() [1/2]

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

◆ WhitenInPlace() [2/2]

void gtsam::noiseModel::Diagonal::WhitenInPlace ( Matrix &  H) const
overridevirtual

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