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

Public Member Functions

TransformBtwRobotsUnaryFactor ()
default constructor - only use for serialization

TransformBtwRobotsUnaryFactor (Key key, const VALUE &measured, Key keyA, Key keyB, const gtsam::Values &valA, const gtsam::Values &valB, const SharedGaussian &model)
Constructor.

virtual gtsam::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 gtsam::Values &valA, const gtsam::Values &valB)
implement functions needed to derive from Factor

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

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

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

gtsam::Vector unwhitenedError (const gtsam::Values &x) const

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< TransformBtwRobotsUnaryFactorshared_ptr
concept check by type

Friends

class boost::serialization::access
Serialization function.

◆ error()

template<class VALUE >
 virtual double gtsam::TransformBtwRobotsUnaryFactor< VALUE >::error ( const gtsam::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::TransformBtwRobotsUnaryFactor< 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: