gtsam::noiseModel::mEstimator::Cauchy Class Reference

#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 (Vector &error) const
void	reweight (std::vector< Matrix > &A, Vector &error) const
void	reweight (Matrix &A, Vector &error) const
void	reweight (Matrix &A1, Matrix &A2, Vector &error) const
void	reweight (Matrix &A1, Matrix &A2, Matrix &A3, Vector &error) const
ReweightScheme	reweightScheme () const
	Returns the reweight scheme, as explained in ReweightScheme. More...
double	sqrtWeight (double distance) const
Vector	sqrtWeight (const Vector &error) 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 (x) = 0.5 k² log(1+x²/k²)
• Derivative (x) = (k²x)/(x²+k²)
• Weight w(x) = (x)/x = k²/(x²+k²)

Definition at line 256 of file LossFunctions.h.

Member Typedef Documentation

shared_ptr

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

Definition at line 261 of file LossFunctions.h.

Constructor & Destructor Documentation

Cauchy()

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

Definition at line 210 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 235 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 229 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 (x) in mEstimator

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

Definition at line 220 of file LossFunctions.cpp.

modelParameter()

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

inline

Definition at line 269 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 225 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 216 of file LossFunctions.cpp.

Member Data Documentation

k_

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

protected

Definition at line 258 of file LossFunctions.h.

ksquared_

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

protected

Definition at line 258 of file LossFunctions.h.

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

• LossFunctions.h
• LossFunctions.cpp