gtsam  3.2.0 gtsam
Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 123456]
 gtsam::_ValuesConstKeyValuePair< ValueType > gtsam::_ValuesKeyValuePair< ValueType > adjacency_list gtsam::internal::AutoTicToc No documentation gtsam::noiseModel::Base NoiseModel::Base is the abstract base class for all noise models gtsam::noiseModel::mEstimator::Base gtsam::BayesTree< CLIQUE > gtsam::BayesTree< DiscreteBayesTreeClique > gtsam::BayesTree< GaussianBayesTreeClique > gtsam::BayesTree< ISAM2Clique > gtsam::BayesTreeCliqueBase< DERIVED, FACTORGRAPH > This is the base class for BayesTree cliques gtsam::BayesTreeCliqueBase< DiscreteBayesTreeClique, DiscreteFactorGraph > gtsam::BayesTreeCliqueBase< GaussianBayesTreeClique, GaussianFactorGraph > gtsam::BayesTreeCliqueBase< ISAM2Clique, GaussianFactorGraph > gtsam::BayesTreeCliqueData Store all the sizes gtsam::BayesTreeCliqueStats Clique statistics binary_function gtsam::Cal3_S2Stereo gtsam::Cal3DS2_Base gtsam::CGState< S, V, E > gtsam::ClusterTree< BAYESTREE, GRAPH >::Cluster gtsam::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 $$scope(f_i) \sub C_k$$ gtsam::ClusterTree< DiscreteBayesTree, DiscreteFactorGraph > gtsam::ClusterTree< GaussianBayesTree, GaussianFactorGraph > gtsam::ClusterTree< ISAM2BayesTree, GaussianFactorGraph > CONCURRENT_MAP_BASE gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL > TODO: Update comments gtsam::Conditional< DecisionTreeFactor, DiscreteConditional > gtsam::Conditional< JacobianFactor, GaussianConditional > ConditionalType gtsam::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 gtsam::const_selector< BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST > Specialization for the non-const version gtsam::const_selector< const BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST > Specialization for the const version gtsam::Values::ConstFiltered< ValueType > A filtered view of a const Values, returned from Values::filter gtsam::Values::ConstKeyValuePair A key-value pair, which you get by dereferencing iterators gtsam::CRefCallAddCopy< C > Helper gtsam::CRefCallPushBack< C > Helper gtsam::DecisionTree< L, Y > Decision Tree L = label for variables Y = function range (any algebra), e.g., bool, int, double gtsam::DecisionTree< Key, double > gtsam::DecisionTree< L, double > default_bfs_visitor gtsam::ISAM2Result::DetailedResults A struct holding detailed results, which must be enabled with ISAM2Params::enableDetailedResults gtsam::DiscreteMarginals A class for computing marginals of variables in a DiscreteFactorGraph gtsam::DoglegOptimizerImpl This class contains the implementation of the Dogleg algorithm gtsam::DSFBase EigenBase gtsam::EliminateableFactorGraph< FACTORGRAPH > EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms gtsam::EliminateableFactorGraph< DiscreteFactorGraph > gtsam::EliminateableFactorGraph< GaussianFactorGraph > gtsam::EliminationTraits< GRAPH > Traits class for eliminateable factor graphs, specifies the types that result from elimination, etc gtsam::EliminationTraits< DiscreteFactorGraph > gtsam::EliminationTraits< GaussianFactorGraph > gtsam::EliminationTree< BAYESNET, GRAPH > An elimination tree is a data structure used intermediately during elimination gtsam::EliminationTree< DiscreteBayesNet, DiscreteFactorGraph > gtsam::EliminationTree< GaussianBayesNet, GaussianFactorGraph > std::exception STL class gtsam::ExtendedKalmanFilter< VALUE > This is a generic Extended Kalman Filter class implemented using nonlinear factors gtsam::Factor This is the base class for all factor types gtsam::FactorGraph< FACTOR > A factor graph is a bipartite graph with factor nodes connected to variable nodes gtsam::FactorGraph< CONDITIONAL > gtsam::FactorGraph< DiscreteConditional > gtsam::FactorGraph< DiscreteFactor > gtsam::FactorGraph< GaussianConditional > gtsam::FactorGraph< GaussianFactor > gtsam::FactorGraph< NonlinearFactor > gtsam::internal::FastDefaultAllocator< T > Default allocator for list, map, and set types gtsam::internal::FastDefaultVectorAllocator< T > Default allocator for vector types (we never use boost pool for vectors) gtsam::FastSetTestableHelper< VALUE, ENABLE > gtsam::FastSetTestableHelper< VALUE, typename boost::enable_if< has_print< VALUE > >::type > gtsam::Values::Filtered< ValueType > A filtered view of a Values, returned from Values::filter gtsam::G_x1< X1, X2 > Helper class that computes the derivative of f w.r.t gtsam::GaussianFactorGraphSystem gtsam::GraphvizFormatting Formatting options when saving in GraphViz format using NonlinearFactorGraph::saveGraph gtsam::GroupConcept< T > This concept check enforces a Group structure on a variable type, in which we require the existence of basic algebraic operations Impl gtsam::ISAM2DoglegParams gtsam::ISAM2GaussNewtonParams gtsam::ISAM2Params gtsam::ISAM2Result gtsam::DoglegOptimizerImpl::IterationResult gtsam::IterativeOptimizationParameters Parameters for iterative linear solvers gtsam::IterativeSolver gtsam::JointMarginal A class to store and access a joint marginal, returned from Marginals::jointMarginalCovariance and Marginals::jointMarginalInformation gtsam::KalmanFilter Kalman Filter class gtsam::Values::KeyValuePair A key-value pair, which you get by dereferencing iterators gtsam::LabeledSymbol Customized version of gtsam::Symbol for multi-robot use gtsam::LieConcept< T > Concept check class for Lie group type std::list< T > STL class gtsam::ListOfOneContainer< T > A helper class that behaves as a container with one element, and works with boost::range gtsam::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 std::map< K, T > STL class gtsam::Marginals A class for computing Gaussian marginals of variables in a NonlinearFactorGraph Matrix gtsam::DecisionTree< L, Y >::Node ---------------------— Node base class ------------------------— gtsam::EliminationTree< BAYESNET, GRAPH >::Node gtsam::NonlinearISAM Wrapper class to manage ISAM in a nonlinear context gtsam::NonlinearOptimizer This is the abstract interface for classes that can optimize for the maximum-likelihood estimate of a NonlinearFactorGraph gtsam::NonlinearOptimizerParams The common parameters for Nonlinear optimizers gtsam::NonlinearOptimizerState Base class for a nonlinear optimization state, including the current estimate of the variable values, error, and number of iterations gtsam::internal::linearAlgorithms::OptimizeClique< CLIQUE > Pre-order visitor for back-substitution in a Bayes tree gtsam::internal::linearAlgorithms::OptimizeData gtsam::ISAM2::PartialSolveResult gtsam::PoseConcept< POSE > Pose Concept A must contain a translation and a rotation, with each structure accessable directly and a type provided for each gtsam::Preconditioner gtsam::PreconditionerParameters gtsam::RangeMeasurementConcept< V1, V2 > Range measurement concept Given a pair of Lie variables, there must exist a function to calculate range with derivatives gtsam::RefCallPushBack< C > Helper gtsam::ISAM2::ReorderingMode gtsam::AlgebraicDecisionTree< L >::Ring The Real ring with addition and multiplication gtsam::Sampler Sampling structure that keeps internal random number generators for diagonal distributions specified by NoiseModel std::set< K > STL class gtsam::SfM_data Define the structure for SfM data gtsam::SfM_Track Define the structure for the 3D points gtsam::Signature Signature for a discrete conditional density, used to construct conditionals gtsam::SlotEntry One SlotEntry stores the slot index for a variable, as well its dimension gtsam::SmartProjectionFactorState gtsam::Subgraph gtsam::SubgraphBuilder gtsam::SubgraphBuilderParameters gtsam::SubgraphEdge gtsam::Symbol Character and index key used in VectorValues, GaussianFactorGraph, GaussianFactor, etc gtsam::SymmetricBlockMatrix gtsam::System Helper class encapsulating the combined system |Ax-b_|^2 Needed to run Conjugate Gradients on matrices gtsam::TbbOpenMPMixedScope An object whose scope defines a block where TBB and OpenMP parallelism are mixed gtsam::TestableConcept< T > gtsam::internal::TimingOutline Timing Entry, arranged in a tree traits tuple gtsam::Value This 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 gtsam::ValueCloneAllocator gtsam::Values A non-templated config holding any types of Manifold-group elements gtsam::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 gtsam::ValueWithDefault< bool, false > gtsam::VariableIndex Computes and stores the block column structure of a factor graph gtsam::ISAM2Result::DetailedResults::VariableStatus The status of a single variable, this struct is stored in DetailedResults::variableStatus Vector std::vector< T > STL class gtsam::VectorValues This class represents a collection of vector-valued variables associated each with a unique integer index gtsam::VerticalBlockMatrix BAYESTREE