gtsam  3.2.0
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Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123456]
oCgtsam::_ValuesConstKeyValuePair< ValueType >
oCgtsam::_ValuesKeyValuePair< ValueType >
oCadjacency_list
oCgtsam::internal::AutoTicTocNo documentation
oCgtsam::noiseModel::BaseNoiseModel::Base is the abstract base class for all noise models
oCgtsam::noiseModel::mEstimator::Base
oCgtsam::BayesTree< CLIQUE >
oCgtsam::BayesTree< DiscreteBayesTreeClique >
oCgtsam::BayesTree< GaussianBayesTreeClique >
oCgtsam::BayesTree< ISAM2Clique >
oCgtsam::BayesTreeCliqueBase< DERIVED, FACTORGRAPH >This is the base class for BayesTree cliques
oCgtsam::BayesTreeCliqueBase< DiscreteBayesTreeClique, DiscreteFactorGraph >
oCgtsam::BayesTreeCliqueBase< GaussianBayesTreeClique, GaussianFactorGraph >
oCgtsam::BayesTreeCliqueBase< ISAM2Clique, GaussianFactorGraph >
oCgtsam::BayesTreeCliqueDataStore all the sizes
oCgtsam::BayesTreeCliqueStatsClique statistics
oCbinary_function
oCgtsam::Cal3_S2Stereo
oCgtsam::Cal3DS2_Base
oCgtsam::CGState< S, V, E >
oCgtsam::ClusterTree< BAYESTREE, GRAPH >::Cluster
oCgtsam::ClusterTree< BAYESTREE, GRAPH >A cluster-tree is associated with a factor graph and is defined as in Koller-Friedman: each node k represents a subset \( C_k \sub X \), and the tree is family preserving, in that each factor \( f_i \) is associated with a single cluster and \( scope(f_i) \sub C_k \)
oCgtsam::ClusterTree< DiscreteBayesTree, DiscreteFactorGraph >
oCgtsam::ClusterTree< GaussianBayesTree, GaussianFactorGraph >
oCgtsam::ClusterTree< ISAM2BayesTree, GaussianFactorGraph >
oCCONCURRENT_MAP_BASE
oCgtsam::Conditional< FACTOR, DERIVEDCONDITIONAL >TODO: Update comments
oCgtsam::Conditional< DecisionTreeFactor, DiscreteConditional >
oCgtsam::Conditional< JacobianFactor, GaussianConditional >
oCConditionalType
oCgtsam::const_selector< TEST_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST >Helper class that uses templates to select between two types based on whether TEST_TYPE is const or not
oCgtsam::const_selector< BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST >Specialization for the non-const version
oCgtsam::const_selector< const BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST >Specialization for the const version
oCgtsam::Values::ConstFiltered< ValueType >A filtered view of a const Values, returned from Values::filter
oCgtsam::Values::ConstKeyValuePairA key-value pair, which you get by dereferencing iterators
oCgtsam::CRefCallAddCopy< C >Helper
oCgtsam::CRefCallPushBack< C >Helper
oCgtsam::DecisionTree< L, Y >Decision Tree L = label for variables Y = function range (any algebra), e.g., bool, int, double
oCgtsam::DecisionTree< Key, double >
oCgtsam::DecisionTree< L, double >
oCdefault_bfs_visitor
oCgtsam::ISAM2Result::DetailedResultsA struct holding detailed results, which must be enabled with ISAM2Params::enableDetailedResults
oCgtsam::DiscreteMarginalsA class for computing marginals of variables in a DiscreteFactorGraph
oCgtsam::DoglegOptimizerImplThis class contains the implementation of the Dogleg algorithm
oCgtsam::DSFBase
oCEigenBase
oCgtsam::EliminateableFactorGraph< FACTORGRAPH >EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms
oCgtsam::EliminateableFactorGraph< DiscreteFactorGraph >
oCgtsam::EliminateableFactorGraph< GaussianFactorGraph >
oCgtsam::EliminationTraits< GRAPH >Traits class for eliminateable factor graphs, specifies the types that result from elimination, etc
oCgtsam::EliminationTraits< DiscreteFactorGraph >
oCgtsam::EliminationTraits< GaussianFactorGraph >
oCgtsam::EliminationTree< BAYESNET, GRAPH >An elimination tree is a data structure used intermediately during elimination
oCgtsam::EliminationTree< DiscreteBayesNet, DiscreteFactorGraph >
oCgtsam::EliminationTree< GaussianBayesNet, GaussianFactorGraph >
oCstd::exceptionSTL class
oCgtsam::ExtendedKalmanFilter< VALUE >This is a generic Extended Kalman Filter class implemented using nonlinear factors
oCgtsam::FactorThis is the base class for all factor types
oCgtsam::FactorGraph< FACTOR >A factor graph is a bipartite graph with factor nodes connected to variable nodes
oCgtsam::FactorGraph< CONDITIONAL >
oCgtsam::FactorGraph< DiscreteConditional >
oCgtsam::FactorGraph< DiscreteFactor >
oCgtsam::FactorGraph< GaussianConditional >
oCgtsam::FactorGraph< GaussianFactor >
oCgtsam::FactorGraph< NonlinearFactor >
oCgtsam::internal::FastDefaultAllocator< T >Default allocator for list, map, and set types
oCgtsam::internal::FastDefaultVectorAllocator< T >Default allocator for vector types (we never use boost pool for vectors)
oCgtsam::FastSetTestableHelper< VALUE, ENABLE >
oCgtsam::FastSetTestableHelper< VALUE, typename boost::enable_if< has_print< VALUE > >::type >
oCgtsam::Values::Filtered< ValueType >A filtered view of a Values, returned from Values::filter
oCgtsam::G_x1< X1, X2 >Helper class that computes the derivative of f w.r.t
oCgtsam::GaussianFactorGraphSystem
oCgtsam::GraphvizFormattingFormatting options when saving in GraphViz format using NonlinearFactorGraph::saveGraph
oCgtsam::GroupConcept< T >This concept check enforces a Group structure on a variable type, in which we require the existence of basic algebraic operations
oCImpl
oCgtsam::ISAM2DoglegParams
oCgtsam::ISAM2GaussNewtonParams
oCgtsam::ISAM2Params
oCgtsam::ISAM2Result
oCgtsam::DoglegOptimizerImpl::IterationResult
oCgtsam::IterativeOptimizationParametersParameters for iterative linear solvers
oCgtsam::IterativeSolver
oCgtsam::JointMarginalA class to store and access a joint marginal, returned from Marginals::jointMarginalCovariance and Marginals::jointMarginalInformation
oCgtsam::KalmanFilterKalman Filter class
oCgtsam::Values::KeyValuePairA key-value pair, which you get by dereferencing iterators
oCgtsam::LabeledSymbolCustomized version of gtsam::Symbol for multi-robot use
oCgtsam::LieConcept< T >Concept check class for Lie group type
oCstd::list< T >STL class
oCgtsam::ListOfOneContainer< T >A helper class that behaves as a container with one element, and works with boost::range
oCgtsam::ManifoldConcept< T >Concept check class for Manifold types Requires a mapping between a linear tangent space and the underlying manifold, of which Lie is a specialization
oCstd::map< K, T >STL class
oCgtsam::MarginalsA class for computing Gaussian marginals of variables in a NonlinearFactorGraph
oCMatrix
oCgtsam::DecisionTree< L, Y >::Node---------------------— Node base class ------------------------—
oCgtsam::EliminationTree< BAYESNET, GRAPH >::Node
oCgtsam::NonlinearISAMWrapper class to manage ISAM in a nonlinear context
oCgtsam::NonlinearOptimizerThis is the abstract interface for classes that can optimize for the maximum-likelihood estimate of a NonlinearFactorGraph
oCgtsam::NonlinearOptimizerParamsThe common parameters for Nonlinear optimizers
oCgtsam::NonlinearOptimizerStateBase class for a nonlinear optimization state, including the current estimate of the variable values, error, and number of iterations
oCgtsam::internal::linearAlgorithms::OptimizeClique< CLIQUE >Pre-order visitor for back-substitution in a Bayes tree
oCgtsam::internal::linearAlgorithms::OptimizeData
oCgtsam::ISAM2::PartialSolveResult
oCgtsam::PoseConcept< POSE >Pose Concept A must contain a translation and a rotation, with each structure accessable directly and a type provided for each
oCgtsam::Preconditioner
oCgtsam::PreconditionerParameters
oCgtsam::RangeMeasurementConcept< V1, V2 >Range measurement concept Given a pair of Lie variables, there must exist a function to calculate range with derivatives
oCgtsam::RefCallPushBack< C >Helper
oCgtsam::ISAM2::ReorderingMode
oCgtsam::AlgebraicDecisionTree< L >::RingThe Real ring with addition and multiplication
oCgtsam::SamplerSampling structure that keeps internal random number generators for diagonal distributions specified by NoiseModel
oCstd::set< K >STL class
oCgtsam::SfM_dataDefine the structure for SfM data
oCgtsam::SfM_TrackDefine the structure for the 3D points
oCgtsam::SignatureSignature for a discrete conditional density, used to construct conditionals
oCgtsam::SlotEntryOne SlotEntry stores the slot index for a variable, as well its dimension
oCgtsam::SmartProjectionFactorState
oCgtsam::Subgraph
oCgtsam::SubgraphBuilder
oCgtsam::SubgraphBuilderParameters
oCgtsam::SubgraphEdge
oCgtsam::SymbolCharacter and index key used in VectorValues, GaussianFactorGraph, GaussianFactor, etc
oCgtsam::SymmetricBlockMatrix
oCgtsam::SystemHelper class encapsulating the combined system |Ax-b_|^2 Needed to run Conjugate Gradients on matrices
oCgtsam::TbbOpenMPMixedScopeAn object whose scope defines a block where TBB and OpenMP parallelism are mixed
oCgtsam::TestableConcept< T >
oCgtsam::internal::TimingOutlineTiming Entry, arranged in a tree
oCtraits
oCtuple
oCgtsam::ValueThis is the interface class for any value that may be used as a variable assignment in a factor graph, and which you must derive to create new variable types to use with gtsam
oCgtsam::ValueCloneAllocator
oCgtsam::ValuesA non-templated config holding any types of Manifold-group elements
oCgtsam::ValueWithDefault< T, defaultValue >Helper struct that encapsulates a value with a default, this is just used as a member object so you don't have to specify defaults in the class constructor
oCgtsam::ValueWithDefault< bool, false >
oCgtsam::VariableIndexComputes and stores the block column structure of a factor graph
oCgtsam::ISAM2Result::DetailedResults::VariableStatusThe status of a single variable, this struct is stored in DetailedResults::variableStatus
oCVector
oCstd::vector< T >STL class
oCgtsam::VectorValuesThis class represents a collection of vector-valued variables associated each with a unique integer index
oCgtsam::VerticalBlockMatrix
\CBAYESTREE