gtsam  4.0.0 gtsam
gtsam::TransformBtwRobotsUnaryFactorEM< VALUE > Class Template Reference
Inheritance diagram for gtsam::TransformBtwRobotsUnaryFactorEM< VALUE >:

Public Member Functions

TransformBtwRobotsUnaryFactorEM ()
default constructor - only use for serialization

TransformBtwRobotsUnaryFactorEM (Key key, const VALUE &measured, Key keyA, Key keyB, const Values &valA, const Values &valB, const SharedGaussian &model_inlier, const SharedGaussian &model_outlier, const double prior_inlier, const double prior_outlier, const bool flag_bump_up_near_zero_probs=false, const bool start_with_M_step=false)
Constructor.

virtual NonlinearFactor::shared_ptr clone () const
Clone.

virtual void print (const std::string &s, const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
implement functions needed for Testable More...

virtual bool equals (const NonlinearFactor &f, double tol=1e-9) const
equals

void setValAValB (const Values &valA, const Values &valB)
implement functions needed to derive from Factor

virtual double error (const Values &x) const
Calculate the error of the factor This is typically equal to log-likelihood, e.g. More...

virtual boost::shared_ptr< GaussianFactorlinearize (const Values &x) const
Linearize a non-linearFactorN to get a GaussianFactor, $$Ax-b \approx h(x+\delta x)-z = h(x) + A \delta x - z$$ Hence $$b = z - h(x) = - \mathtt{error\_vector}(x)$$.

Vector whitenedError (const Values &x, boost::optional< std::vector< Matrix > & > H=boost::none) const

Vector calcIndicatorProb (const Values &x) const

Vector calcIndicatorProb (const Values &x, const Vector &err) const

Vector unwhitenedError (const Values &x) const

SharedGaussian get_model_inlier () const

SharedGaussian get_model_outlier () const

Matrix get_model_inlier_cov () const

Matrix get_model_outlier_cov () const

void updateNoiseModels (const Values &values, const Marginals &marginals)

void updateNoiseModels (const Values &values, const NonlinearFactorGraph &graph)

void updateNoiseModels_givenCovs (const Values &values, const Matrix &cov1, const Matrix &cov2, const Matrix &cov12)

std::size_t size () const
number of variables attached to this factor

virtual size_t dim () const
get the dimension of the factor (number of rows on linearization)

Public Types

typedef VALUE T

typedef boost::shared_ptr< TransformBtwRobotsUnaryFactorEMshared_ptr
concept check by type

Friends

class boost::serialization::access
Serialization function.

◆ error()

template<class VALUE >
 virtual double gtsam::TransformBtwRobotsUnaryFactorEM< VALUE >::error ( const Values & c ) const
inlinevirtual

Calculate the error of the factor This is typically equal to log-likelihood, e.g.

$$0.5(h(x)-z)^2/sigma^2$$ in case of Gaussian. You can override this for systems with unusual noise models.

Implements gtsam::NonlinearFactor.

◆ print()

template<class VALUE >
 virtual void gtsam::TransformBtwRobotsUnaryFactorEM< VALUE >::print ( const std::string & s, const KeyFormatter & keyFormatter = DefaultKeyFormatter ) const
inlinevirtual

implement functions needed for Testable

print

Reimplemented from gtsam::NonlinearFactor.

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