|
| 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::GaussianFactor > | linearize (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)
|
|
◆ error()
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()
The documentation for this class was generated from the following files: