gtsam::noiseModel::mEstimator::Null Class Reference

#include <LossFunctions.h>

Inheritance diagram for gtsam::noiseModel::mEstimator::Null:

Public Types
typedef std::shared_ptr< Null >	shared_ptr
Public Types inherited from gtsam::noiseModel::mEstimator::Base
enum	ReweightScheme { Scalar, Block }
typedef std::shared_ptr< Base >	shared_ptr

Public Member Functions
bool	equals (const Base &, double) const override
double	loss (double distance) const override
	Null (const ReweightScheme reweight=Block)
void	print (const std::string &s) const override
double	weight (double) const override
	~Null () 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 ()

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

Detailed Description

"Null" robust loss function, equivalent to a Gaussian pdf noise model, or plain least-squares (non-robust).

This model has no additional parameters.

• Loss
• Derivative
• Weight

Definition at line 151 of file LossFunctions.h.

Member Typedef Documentation

shared_ptr

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

Definition at line 153 of file LossFunctions.h.

Constructor & Destructor Documentation

Null()

gtsam::noiseModel::mEstimator::Null::Null ( const ReweightScheme  reweight = Block )

inline

Definition at line 155 of file LossFunctions.h.

~Null()

gtsam::noiseModel::mEstimator::Null::~Null ( )

inlineoverride

Definition at line 156 of file LossFunctions.h.

Member Function Documentation

Create()

Null::shared_ptr gtsam::noiseModel::mEstimator::Null::Create ( )

static

Definition at line 133 of file LossFunctions.cpp.

equals()

bool gtsam::noiseModel::mEstimator::Null::equals ( const Base &  , double    ) const

inlineoverridevirtual

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

Definition at line 160 of file LossFunctions.h.

loss()

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

inlineoverridevirtual

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 in mEstimator

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

Definition at line 158 of file LossFunctions.h.

print()

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

overridevirtual

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

Definition at line 130 of file LossFunctions.cpp.

weight()

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

inlineoverridevirtual

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 157 of file LossFunctions.h.

---

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

• LossFunctions.h
• LossFunctions.cpp