9 #include <gtsam/slam/JacobianSchurFactor.h>
26 const Matrix& E,
const Matrix3& P,
const Vector& b,
27 const SharedDiagonal& model = SharedDiagonal()) :
33 BOOST_FOREACH(
const typename Base::KeyMatrix2D& it, Fblocks) {
34 gfg.
add(pointKey, E.block<2, 3>(2 * i, 0), it.first, it.second,
35 b.segment<2>(2 * i), model);
41 GaussianBayesNet::shared_ptr bn;
43 std::vector < Key > variables;
44 variables.push_back(pointKey);
Character and index key used in VectorValues, GaussianFactorGraph, GaussianFactor, etc.
Definition: Symbol.h:33
JacobianFactorQR(const std::vector< typename Base::KeyMatrix2D > &Fblocks, const Matrix &E, const Matrix3 &P, const Vector &b, const SharedDiagonal &model=SharedDiagonal())
Constructor.
Definition: JacobianFactorQR.h:25
void add(const GaussianFactor &factor)
Add a factor by value - makes a copy.
Definition: GaussianFactorGraph.h:102
JacobianFactor for Schur complement that uses Q noise model.
Definition: JacobianFactorQR.h:16
This is the base class for all factor types.
Definition: Factor.h:51
JacobianFactor()
default constructor for I/O
Definition: JacobianFactor.cpp:61
boost::shared_ptr< This > shared_ptr
shared_ptr to this class
Definition: GaussianFactorGraph.h:74
JacobianFactor for Schur complement that uses Q noise model.
Definition: JacobianSchurFactor.h:22
A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e.
Definition: GaussianFactorGraph.h:65
std::pair< boost::shared_ptr< BayesNetType >, boost::shared_ptr< FactorGraphType > > eliminatePartialSequential(const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
Do sequential elimination of some variables, in ordering provided, to produce a Bayes net and a remai...
Definition: EliminateableFactorGraph-inst.h:97