#include <LossFunctions.h>

Inheritance diagram for gtsam::noiseModel::mEstimator::Cauchy:

Public Types
typedef std::shared_ptr< Cauchy >	shared_ptr

Public Types inherited from gtsam::noiseModel::mEstimator::Base
enum	ReweightScheme { Scalar, Block }

typedef std::shared_ptr< Base >	shared_ptr

Public Member Functions
	Cauchy (double k=0.1, const ReweightScheme reweight=Block)

bool	equals (const Base &expected, double tol=1e-8) const override

double	loss (double distance) const override

double	modelParameter () const

void	print (const std::string &s) const override

double	weight (double distance) const override

Public Member Functions inherited from gtsam::noiseModel::mEstimator::Base
	Base (const ReweightScheme reweight=Block)

void	reweight (Matrix &A, Vector &error) const

void	reweight (Matrix &A1, Matrix &A2, Matrix &A3, Vector &error) const

void	reweight (Matrix &A1, Matrix &A2, Vector &error) const

void	reweight (std::vector< Matrix > &A, Vector &error) const

void	reweight (Vector &error) const

ReweightScheme	reweightScheme () const
	Returns the reweight scheme, as explained in ReweightScheme. More...

Vector	sqrtWeight (const Vector &error) const

double	sqrtWeight (double distance) const

Vector	weight (const Vector &error) const

virtual	~Base ()

Static Public Member Functions
static shared_ptr	Create (double k, const ReweightScheme reweight=Block)

Protected Attributes
double	k_

double	ksquared_

Protected Attributes inherited from gtsam::noiseModel::mEstimator::Base
ReweightScheme	reweight_
	Strategy for reweighting. More...

Detailed Description

Implementation of the "Cauchy" robust error model (Lee2013IROS). Contributed by: Dipl.-Inform. Jan Oberlaender (M.Sc.), FZI Research Center for Information Technology, Karlsruhe, Germany. oberl.nosp@m.aend.nosp@m.er@fz.nosp@m.i.de Thanks Jan!

This model has a scalar parameter "k".

Loss \rho(x) = 0.5 k² log(1+x²/k²)
Derivative \phi(x) = (k²x)/(x²+k²)
Weight w(x) = \phi(x)/x = k²/(x²+k²)

Definition at line 257 of file LossFunctions.h.

Member Typedef Documentation

◆ shared_ptr

typedef std::shared_ptr<Cauchy> gtsam::noiseModel::mEstimator::Cauchy::shared_ptr

Definition at line 262 of file LossFunctions.h.

Constructor & Destructor Documentation

◆ Cauchy()

gtsam::noiseModel::mEstimator::Cauchy::Cauchy	(	double	k = `0.1`,
		const ReweightScheme	reweight = `Block`
	)

Definition at line 211 of file LossFunctions.cpp.

Member Function Documentation

◆ Create()

Cauchy::shared_ptr gtsam::noiseModel::mEstimator::Cauchy::Create	(	double	k,
		const ReweightScheme	reweight = `Block`
	)

static

Definition at line 236 of file LossFunctions.cpp.

◆ equals()

bool gtsam::noiseModel::mEstimator::Cauchy::equals	(	const Base &	expected,
		double	tol = `1e-8`
	)		const

overridevirtual

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 230 of file LossFunctions.cpp.

◆ loss()

double gtsam::noiseModel::mEstimator::Cauchy::loss ( double distance ) const

overridevirtual

This method is responsible for returning the total penalty for a given amount of error. For example, this method is responsible for implementing the quadratic function for an L2 penalty, the absolute value function for an L1 penalty, etc.

TODO(mikebosse): When the loss function has as input the norm of the error vector, then it prevents implementations of asymmeric loss functions. It would be better for this function to accept the vector and internally call the norm if necessary.

This returns $\rho(x)$ in mEstimator

Reimplemented from gtsam::noiseModel::mEstimator::Base.

Definition at line 221 of file LossFunctions.cpp.

◆ modelParameter()

double gtsam::noiseModel::mEstimator::Cauchy::modelParameter ( ) const

inline

Definition at line 270 of file LossFunctions.h.

◆ print()

void gtsam::noiseModel::mEstimator::Cauchy::print ( const std::string & s = "" ) const

overridevirtual

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 226 of file LossFunctions.cpp.

◆ weight()

double gtsam::noiseModel::mEstimator::Cauchy::weight ( double distance ) const

overridevirtual

This method is responsible for returning the weight function for a given amount of error. The weight function is related to the analytic derivative of the loss function. See https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf for details. This method is required when optimizing cost functions with robust penalties using iteratively re-weighted least squares.

This returns w(x) in mEstimator

Implements gtsam::noiseModel::mEstimator::Base.

Definition at line 217 of file LossFunctions.cpp.

Member Data Documentation

◆ k_

double gtsam::noiseModel::mEstimator::Cauchy::k_

protected

Definition at line 259 of file LossFunctions.h.

◆ ksquared_

double gtsam::noiseModel::mEstimator::Cauchy::ksquared_

protected

Definition at line 259 of file LossFunctions.h.

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ shared_ptr

Constructor & Destructor Documentation

◆ Cauchy()

Member Function Documentation

◆ Create()

◆ equals()

◆ loss()

◆ modelParameter()

◆ print()

◆ weight()

Member Data Documentation

◆ k_

◆ ksquared_