49class GTSAM_EXPORT DCSAM {
174 void updateDiscrete(
const DiscreteFactorGraph &dfg = DiscreteFactorGraph(),
188 void updateContinuous(
const NonlinearFactorGraph &newFactors,
189 const Values &initialGuess);
specialized key for discrete variables
A class for computing marginals in a DiscreteFactorGraph.
Nonlinear hybrid factor graph that uses type erasure.
A class for computing marginals in a NonlinearFactorGraph.
Factor Graph consisting of non-linear factors.
Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
Global functions in a separate testing namespace.
Definition chartTesting.h:28
A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e.
Definition DiscreteFactorGraph.h:100
A map from keys to values.
Definition DiscreteValues.h:34
void update(const HybridNonlinearFactorGraph &graph, const DiscreteValues &initialGuessDiscrete)
Inline convenience function to allow "skipping" the initial guess for continuous variables while addi...
Definition DCSAM.h:113
const DiscreteFactorGraph & getDiscreteFactorGraph() const
Used to obtain the marginals from the solver.
Definition DCSAM.h:158
void update(const HybridNonlinearFactorGraph &graph, const HybridValues &initialGuess=HybridValues())
For this solver, runs an iteration of alternating minimization between discrete and continuous variab...
Definition DCSAM.cpp:29
Definition HybridNonlinearFactorGraph.h:34
HybridValues represents a collection of DiscreteValues and VectorValues.
Definition HybridValues.h:37
VectorValues represents a collection of vector-valued variables associated each with a unique integer...
Definition VectorValues.h:73
Implementation of the full ISAM2 algorithm for incremental nonlinear optimization.
Definition ISAM2.h:45
const NonlinearFactorGraph & getFactorsUnsafe() const
Access the set of nonlinear factors.
Definition ISAM2.h:304
Definition ISAM2Params.h:199
Definition NonlinearFactorGraph.h:57
A non-templated config holding any types of Manifold-group elements.
Definition Values.h:65
The Factor::error simply extracts the.
In nonlinear factors, the error function returns the negative log-likelihood as a non-linear function...