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Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:

boost::mp11::detail::_merge_and_renumber< Sequence1, Sequence2 > |
boost::mp11::detail::_merge_and_renumber< make_index_sequence< N/2 >::type, make_index_sequence< N - N/2 >::type > |
boost::mp11::make_index_sequence< sizeof...(T)> |
boost::mp11::make_index_sequence< 0 > |
boost::mp11::make_index_sequence< 1 > |
boost::mp11::make_index_sequence< N > |
gtsam::_ValuesConstKeyValuePair< ValueType > |
gtsam::_ValuesKeyValuePair< ValueType > |
gtsam::additive_group_tag |
internal::AdditiveGroupTraits |
gtsam::traits< Cyclic< N > > | Define cyclic group to be a model of the Additive Group concept
gtsam::traits< DirectSum< G, H > > |
boost::adjacency_list |
gtsam::SDGraph< KEY > | SDGraph is undirected graph with variable keys and double edge weights
gtsam::SGraph< KEY > |
gtsam::internal::apply_compose< T > |
gtsam::internal::apply_compose< double > |
gtsam::AttitudeFactor | Base class for prior on attitude Example:
gtsam::Pose3AttitudeFactor | Version of AttitudeFactor for Pose3
gtsam::Rot3AttitudeFactor | Version of AttitudeFactor for Rot3
gtsam::internal::AutoTicToc | Small class that calls internal::tic at construction, and internol::toc when destroyed
gtsam::noiseModel::Base | NoiseModel::Base is the abstract base class for all noise models
gtsam::noiseModel::Gaussian | Gaussian implements the mathematical model |R*x|^2 = |y|^2 with R'*R=inv(Sigma) where y = whiten(x) = R*x x = unwhiten(x) = inv(R)*y as indeed |y|^2 = y'*y = x'*R'*R*x Various derived classes are available that are more efficient
gtsam::noiseModel::Diagonal | A diagonal noise model implements a diagonal covariance matrix, with the elements of the diagonal specified in a Vector
gtsam::noiseModel::Constrained | A Constrained constrained model is a specialization of Diagonal which allows some or all of the sigmas to be zero, forcing the error to be zero there
gtsam::noiseModel::Isotropic | An isotropic noise model corresponds to a scaled diagonal covariance To construct, use one of the static methods
gtsam::noiseModel::Unit | Unit: i.i.d
gtsam::noiseModel::Robust | Base class for robust error models The robust M-estimators above simply tell us how to re-weight the residual, and are isotropic kernels, in that they do not allow for correlated noise
gtsam::noiseModel::mEstimator::Base | Pure virtual class for all robust error function classes
gtsam::noiseModel::mEstimator::Cauchy | Implementation of the "Cauchy" robust error model (Lee2013IROS)
gtsam::noiseModel::mEstimator::DCS | DCS implements the Dynamic Covariance Scaling robust error model from the paper Robust Map Optimization (Agarwal13icra)
gtsam::noiseModel::mEstimator::Fair | Implementation of the "Fair" robust error model (Zhang97ivc)
gtsam::noiseModel::mEstimator::GemanMcClure | Implementation of the "Geman-McClure" robust error model (Zhang97ivc)
gtsam::noiseModel::mEstimator::Huber | The "Huber" robust error model (Zhang97ivc)
gtsam::noiseModel::mEstimator::L2WithDeadZone | L2WithDeadZone implements a standard L2 penalty, but with a dead zone of width 2*k, centered at the origin
gtsam::noiseModel::mEstimator::Null | "Null" robust loss function, equivalent to a Gaussian pdf noise model, or plain least-squares (non-robust)
gtsam::noiseModel::mEstimator::Tukey | Implementation of the "Tukey" robust error model (Zhang97ivc)
gtsam::noiseModel::mEstimator::Welsch | Implementation of the "Welsch" robust error model (Zhang97ivc)
gtsam::Basis< DERIVED > | CRTP Base class for function bases
gtsam::Basis< Chebyshev1Basis > |
gtsam::Chebyshev1Basis | Basis of Chebyshev polynomials of the first kind https://en.wikipedia.org/wiki/Chebyshev_polynomials#First_kind These are typically denoted with the symbol T_n, where n is the degree
gtsam::Basis< Chebyshev2 > |
gtsam::Chebyshev2 | Chebyshev Interpolation on Chebyshev points of the second kind Note that N here, the number of points, is one less than N from 'Approximation Theory and Approximation Practice by L
gtsam::Basis< Chebyshev2Basis > |
gtsam::Chebyshev2Basis | Basis of Chebyshev polynomials of the second kind
gtsam::Basis< FourierBasis > |
gtsam::FourierBasis | Fourier basis
BAYESTREE |
gtsam::ISAM< BAYESTREE > | A Bayes tree with an update methods that implements the iSAM algorithm
gtsam::BayesTree< CLIQUE > | Bayes tree
gtsam::BayesTree< DiscreteBayesTreeClique > |
gtsam::DiscreteBayesTree | A Bayes tree representing a Discrete density
gtsam::BayesTree< GaussianBayesTreeClique > |
gtsam::GaussianBayesTree | A Bayes tree representing a Gaussian density
gtsam::ISAM< GaussianBayesTree > |
gtsam::GaussianISAM |
gtsam::BayesTree< HybridBayesTreeClique > |
gtsam::HybridBayesTree | A Bayes tree representing a Hybrid density
gtsam::ISAM< HybridBayesTree > |
gtsam::HybridGaussianISAM |
gtsam::BayesTree< ISAM2Clique > |
gtsam::ISAM2 | Implementation of the full ISAM2 algorithm for incremental nonlinear optimization
gtsam::ISAM2BayesTree |
gtsam::BayesTree< SymbolicBayesTreeClique > |
gtsam::SymbolicBayesTree | A Bayes tree that represents the connectivity between variables but is not associated with any probability functions
gtsam::ISAM< SymbolicBayesTree > |
gtsam::SymbolicISAM |
gtsam::BayesTreeCliqueBase< DERIVED, FACTORGRAPH > | This is the base class for BayesTree cliques
gtsam::BayesTreeCliqueBase< DiscreteBayesTreeClique, DiscreteFactorGraph > |
gtsam::DiscreteBayesTreeClique | A clique in a DiscreteBayesTree
gtsam::BayesTreeCliqueBase< GaussianBayesTreeClique, GaussianFactorGraph > |
gtsam::GaussianBayesTreeClique | A clique in a GaussianBayesTree
gtsam::BayesTreeCliqueBase< HybridBayesTreeClique, HybridGaussianFactorGraph > |
gtsam::HybridBayesTreeClique | A clique in a HybridBayesTree which is a HybridConditional internally
gtsam::BayesTreeCliqueBase< ISAM2Clique, GaussianFactorGraph > |
gtsam::ISAM2Clique | Specialized Clique structure for ISAM2, incorporating caching and gradient contribution TODO: more documentation
gtsam::BayesTreeCliqueBase< SymbolicBayesTreeClique, SymbolicFactorGraph > |
gtsam::SymbolicBayesTreeClique | A clique in a SymbolicBayesTree
gtsam::BayesTreeCliqueData | Store all the sizes
gtsam::BayesTreeCliqueStats | Clique statistics
gtsam::Bearing< A1, A2 > |
gtsam::BearingRange< A1, A2, B, R > | Bearing-Range product for a particular A1,A2 combination will use the functors above to create a similar functor of type A1*A2 -> pair<Bearing::return_type,Range::return_type> For example BearingRange<Pose2,Point2>(pose,point) will return pair<Rot2,double> and BearingRange<Pose3,Point3>(pose,point) will return pair<Unit3,double>
gtsam::Expression< T >::BinaryFunction< A1, A2 > |
gtsam::Cal3 | Common base class for all calibration models
gtsam::Cal3Bundler | Calibration used by Bundler
gtsam::Cal3DS2_Base | Calibration of a camera with radial distortion
gtsam::Cal3DS2 | Calibration of a camera with radial distortion that also supports Lie-group behaviors for optimization
gtsam::Cal3Unified | Calibration of a omni-directional camera with mirror + lens radial distortion
gtsam::Cal3Fisheye | Calibration of a fisheye camera
gtsam::Cal3_S2 | The most common 5DOF 3D->2D calibration
gtsam::Cal3_S2Stereo | The most common 5DOF 3D->2D calibration, stereo version
gtsam::Rot3::CayleyChart |
gtsam::CGState< S, V, E > |
gtsam::Pose2::ChartAtOrigin |
gtsam::Pose3::ChartAtOrigin |
gtsam::Rot2::ChartAtOrigin |
gtsam::Rot3::ChartAtOrigin |
gtsam::Similarity2::ChartAtOrigin | Chart at the origin
gtsam::Similarity3::ChartAtOrigin | Chart at the origin
gtsam::SO< N >::ChartAtOrigin |
gtsam::ClusterTree< GRAPH >::Cluster | A Cluster is just a collection of factors
gtsam::ClusterTree< 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::EliminatableClusterTree< BAYESTREE, GRAPH > | A cluster-tree that eliminates to a Bayes tree
gtsam::JunctionTree< DiscreteBayesTree, DiscreteFactorGraph > |
gtsam::DiscreteJunctionTree | An EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net
gtsam::sharedNode< HybridBayesTree, HybridGaussianFactorGraph > |
gtsam::JunctionTree< HybridBayesTree, HybridGaussianFactorGraph > |
gtsam::HybridJunctionTree | An EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net
gtsam::Node< BAYESTREE, GRAPH > |
gtsam::sharedNode< BAYESTREE, GRAPH > |
gtsam::JunctionTree< GaussianBayesTree, GaussianFactorGraph > |
gtsam::GaussianJunctionTree | A junction tree specialized to Gaussian factors, i.e., it is a cluster tree with Gaussian factors stored in each cluster
gtsam::JunctionTree< ISAM2BayesTree, GaussianFactorGraph > |
gtsam::ISAM2JunctionTree |
gtsam::JunctionTree< SymbolicBayesTree, SymbolicFactorGraph > |
gtsam::SymbolicJunctionTree | A EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net
gtsam::JunctionTree< BAYESTREE, GRAPH > | A JunctionTree is a cluster tree, a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes Net
gtsam::Conditional< FACTOR, DERIVEDCONDITIONAL > |
gtsam::Conditional< DecisionTreeFactor, DiscreteConditional > |
gtsam::DiscreteConditional | Discrete Conditional Density Derives from DecisionTreeFactor
gtsam::DiscreteDistribution | A prior probability on a set of discrete variables
gtsam::DiscreteLookupTable | DiscreteLookupTable table for max-product
gtsam::Conditional< HybridFactor, GaussianMixture > |
gtsam::GaussianMixture | A conditional of gaussian mixtures indexed by discrete variables, as part of a Bayes Network
gtsam::Conditional< HybridFactor, HybridConditional > |
gtsam::HybridConditional | Hybrid Conditional Density
gtsam::BayesTreeOrphanWrapper< HybridBayesTreeClique > | Class for Hybrid Bayes tree orphan subtrees
gtsam::Conditional< JacobianFactor, GaussianConditional > |
gtsam::GaussianConditional | A GaussianConditional functions as the node in a Bayes network
gtsam::GaussianDensity | A GaussianDensity is a GaussianConditional without parents
gtsam::Conditional< SymbolicFactor, SymbolicConditional > |
gtsam::SymbolicConditional | SymbolicConditional is a conditional with keys but no probability data, produced by symbolic elimination of SymbolicFactor
CLIQUE::ConditionalType |
gtsam::BayesTreeOrphanWrapper< CLIQUE, typename > |
HybridBayesTree::Clique::ConditionalType |
gtsam::BayesTreeOrphanWrapper< HybridBayesTree::Clique > |
HybridBayesTreeClique::ConditionalType |
gtsam::BayesTreeOrphanWrapper< HybridBayesTreeClique > | Class for Hybrid Bayes tree orphan subtrees
gtsam::vector< T >::const_iterator | STL iterator class
map< K, T >::const_iterator | STL iterator class
string::const_iterator | STL iterator class
unordered_map< K, T >::const_iterator | STL iterator class
unordered_set< K >::const_iterator | STL iterator class
vector< T >::const_iterator | STL iterator class
gtsam::vector< T >::const_reverse_iterator | STL iterator class
map< K, T >::const_reverse_iterator | STL iterator class
string::const_reverse_iterator | STL iterator class
unordered_map< K, T >::const_reverse_iterator | STL iterator class
unordered_set< K >::const_reverse_iterator | STL iterator class
vector< T >::const_reverse_iterator | STL iterator class
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::imuBias::ConstantBias |
gtsam::Values::ConstKeyValuePair | A key-value pair, which you get by dereferencing iterators
gtsam::ConstructorTraversalData< BAYESTREE, GRAPH, ETREE_NODE > |
gtsam::CRefCallAddCopy< C > | Helper
gtsam::CRefCallPushBack< C > | Helper
gtsam::Cyclic< N > | Cyclic group of order N
gtsam::DecisionTree< L, Y > | Decision tree is a function from assignments to values
gtsam::AlgebraicDecisionTree< Key > |
gtsam::DecisionTreeFactor | A discrete probabilistic factor
gtsam::DiscreteConditional | Discrete Conditional Density Derives from DecisionTreeFactor
gtsam::DecisionTree< L, double > |
gtsam::AlgebraicDecisionTree< L > | An algebraic decision tree fixes the range of a DecisionTree to double
boost::default_bfs_visitor |
gtsam::compose_key_visitor< V, POSE, KEY > |
gtsam::ordering_key_visitor< KEY > |
gtsam::DeltaImpl |
gtsam::Values::deref_iterator |
gtsam::Basis< DERIVED >::DerivativeFunctorBase | Base class for functors below that calculate derivative weights
gtsam::Basis< DERIVED >::ComponentDerivativeFunctor< M > | Given a M*N Matrix of M-vectors at N polynomial points, an instance of ComponentDerivativeFunctor computes the N-vector derivative for a specific row component of the M-vectors at all the polynomial points
gtsam::Basis< DERIVED >::DerivativeFunctor | An instance of a DerivativeFunctor calculates f'(x;p) at a given x, applied to Parameters p
gtsam::Basis< DERIVED >::VectorDerivativeFunctor< M > | VectorDerivativeFunctor at a given x, applied to ParameterMatrix<M>
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
DiscreteValues | The Factor::error simply extracts the
gtsam::DoglegOptimizerImpl | This class contains the implementation of the Dogleg algorithm
gtsam::DotWriter | DotWriter is a helper class for writing graphviz .dot files
gtsam::GraphvizFormatting | Formatting options and functions for saving a NonlinearFactorGraph instance in GraphViz format
gtsam::DSFBase | A fast implementation of disjoint set forests that uses vector as underly data structure
gtsam::DSFVector | DSFVector additionally keeps a vector of keys to support more expensive operations
gtsam::DSFMap< KEY > | Disjoint set forest using an STL map data structure underneath Uses rank compression and union by rank, iterator version
gtsam::internal::DynamicTraits< M, N, Options, MaxRows, MaxCols > |
gtsam::internal::DynamicTraits< 1, -1, Options, MaxRows, MaxCols > |
gtsam::traits< Eigen::Matrix< double, 1, -1, Options, MaxRows, MaxCols > > |
gtsam::internal::DynamicTraits<-1, -1, Options, MaxRows, MaxCols > |
gtsam::traits< Eigen::Matrix< double, -1, -1, Options, MaxRows, MaxCols > > |
gtsam::internal::DynamicTraits<-1, 1, Options, MaxRows, MaxCols > |
gtsam::traits< Eigen::Matrix< double, -1, 1, Options, MaxRows, MaxCols > > |
gtsam::Subgraph::Edge |
gtsam::EliminateableFactorGraph< FACTOR_GRAPH > | EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms
gtsam::EliminateableFactorGraph< DiscreteFactorGraph > |
gtsam::DiscreteFactorGraph | A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e
gtsam::EliminateableFactorGraph< GaussianFactorGraph > |
gtsam::GaussianFactorGraph | A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e
gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph > |
gtsam::HybridGaussianFactorGraph |
gtsam::EliminateableFactorGraph< SymbolicFactorGraph > |
gtsam::SymbolicFactorGraph | Symbolic Factor Graph
gtsam::EliminationData< CLUSTERTREE > |
gtsam::EliminationData< CLUSTERTREE >::EliminationPostOrderVisitor |
gtsam::EliminationTraits< GRAPH > | Traits class for eliminateable factor graphs, specifies the types that result from elimination, etc
gtsam::EliminationTraits< DiscreteFactorGraph > |
gtsam::EliminationTraits< GaussianFactorGraph > |
gtsam::EliminationTraits< HybridGaussianFactorGraph > |
gtsam::EliminationTraits< SymbolicFactorGraph > |
gtsam::EliminationTree< BAYESNET, GRAPH > | An elimination tree is a data structure used intermediately during elimination
gtsam::EliminationTree< DiscreteBayesNet, DiscreteFactorGraph > |
gtsam::DiscreteEliminationTree | Elimination tree for discrete factors
gtsam::EliminationTree< GaussianBayesNet, GaussianFactorGraph > |
gtsam::GaussianEliminationTree |
gtsam::EliminationTree< HybridBayesNet, HybridGaussianFactorGraph > |
gtsam::HybridEliminationTree | Elimination Tree type for Hybrid Factor Graphs
gtsam::EliminationTree< SymbolicBayesNet, SymbolicFactorGraph > |
gtsam::SymbolicEliminationTree |
gtsam::EmptyCal | Empty calibration
gtsam::DSFMap< KEY >::Entry | We store the forest in an STL map, but parents are done with pointers
gtsam::EssentialMatrix | An essential matrix is like a Pose3, except with translation up to scale It is named after the 3*3 matrix aEb = [aTb]x aRb from computer vision, but here we choose instead to parameterize it as a (Rot3,Unit3) pair
gtsam::Basis< DERIVED >::EvaluationFunctor | An instance of an EvaluationFunctor calculates f(x;p) at a given x, applied to Parameters p
gtsam::Basis< DERIVED >::VectorEvaluationFunctor< traits< T >::dimension > |
gtsam::Basis< DERIVED >::ManifoldEvaluationFunctor< T > | Manifold EvaluationFunctor at a given x, applied to ParameterMatrix<M>
gtsam::Basis< DERIVED >::VectorComponentFunctor< M > | Given a M*N Matrix of M-vectors at N polynomial points, an instance of VectorComponentFunctor computes the N-vector value for a specific row component of the M-vectors at all the polynomial points
gtsam::Basis< DERIVED >::VectorEvaluationFunctor< M > | VectorEvaluationFunctor at a given x, applied to ParameterMatrix<M>
std::exception | STL class
gtsam::ThreadsafeException< CheiralityException > |
gtsam::CheiralityException |
gtsam::ThreadsafeException< CholeskyFailed > |
gtsam::CholeskyFailed | Indicate Cholesky factorization failure
gtsam::ThreadsafeException< IndeterminantLinearSystemException > |
gtsam::IndeterminantLinearSystemException | Thrown when a linear system is ill-posed
gtsam::ThreadsafeException< InvalidArgumentThreadsafe > |
gtsam::InvalidArgumentThreadsafe | Thread-safe invalid argument exception
gtsam::ThreadsafeException< InvalidDenseElimination > |
gtsam::InvalidDenseElimination |
gtsam::ThreadsafeException< InvalidMatrixBlock > |
gtsam::InvalidMatrixBlock | An exception indicating that a matrix block passed into a JacobianFactor has a different dimensionality than the factor
gtsam::ThreadsafeException< InvalidNoiseModel > |
gtsam::InvalidNoiseModel | An exception indicating that the noise model dimension passed into a JacobianFactor has a different dimensionality than the factor
gtsam::ThreadsafeException< OutOfRangeThreadsafe > |
gtsam::OutOfRangeThreadsafe | Thread-safe out of range exception
gtsam::ThreadsafeException< RuntimeErrorThreadsafe > |
gtsam::RuntimeErrorThreadsafe | Thread-safe runtime error exception
gtsam::DynamicValuesMismatched |
gtsam::InconsistentEliminationRequested | An inference algorithm was called with inconsistent arguments
gtsam::MarginalizeNonleafException | Thrown when requesting to marginalize out variables from ISAM2 that are not leaves
gtsam::NoMatchFoundForFixed |
gtsam::ThreadsafeException< DERIVED > | Base exception type that uses tbb_allocator if GTSAM is compiled with TBB
gtsam::ValuesIncorrectType |
gtsam::ValuesKeyAlreadyExists |
gtsam::ValuesKeyDoesNotExist |
std::runtime_error | STL class
gtsam::StereoCheiralityException |
gtsam::TriangulationCheiralityException | Exception thrown by triangulateDLT when landmark is behind one or more of the cameras
gtsam::TriangulationUnderconstrainedException | Exception thrown by triangulateDLT when SVD returns rank < 3
gtsam::internal::ExecutionTrace< T > |
gtsam::so3::ExpmapFunctor | Functor implementing Exponential map
gtsam::so3::DexpFunctor | Functor that implements Exponential map and its derivatives
gtsam::Expression< T > | Expression class that supports automatic differentiation
gtsam::BinarySumExpression< T > | A BinarySumExpression is a specialization of Expression that adds two expressions together It optimizes the Jacobian calculation for this specific case
gtsam::ScalarMultiplyExpression< T > | A ScalarMultiplyExpression is a specialization of Expression that multiplies with a scalar It optimizes the Jacobian calculation for this specific case
gtsam::internal::ExpressionNode< T > |
gtsam::ExtendedKalmanFilter< VALUE > | This is a generic Extended Kalman Filter class implemented using nonlinear factors
gtsam::Factor |
gtsam::BinaryMeasurement< T > |
gtsam::DiscreteFactor | Base class for discrete probabilistic factors The most general one is the derived DecisionTreeFactor
gtsam::DecisionTreeFactor | A discrete probabilistic factor
gtsam::GaussianFactor | An abstract virtual base class for JacobianFactor and HessianFactor
gtsam::HessianFactor | A Gaussian factor using the canonical parameters (information form)
gtsam::RegularHessianFactor< D > |
gtsam::JacobianFactor | A Gaussian factor in the squared-error form
gtsam::BinaryJacobianFactor< M, N1, N2 > | A binary JacobianFactor specialization that uses fixed matrix math for speed
gtsam::GaussianConditional | A GaussianConditional functions as the node in a Bayes network
gtsam::RegularJacobianFactor< D > | JacobianFactor with constant sized blocks Provides raw memory access versions of linear operator
gtsam::JacobianFactorQ< D, ZDim > | JacobianFactor for Schur complement that uses Q noise model
gtsam::JacobianFactorQR< D, ZDim > | JacobianFactor for Schur complement that uses Q noise model
gtsam::JacobianFactorSVD< D, ZDim > | JacobianFactor for Schur complement that uses the "Nullspace Trick" by Mourikis et al
gtsam::RegularImplicitSchurFactor< CAMERA > | RegularImplicitSchurFactor
gtsam::HybridFactor | Base class for truly hybrid probabilistic factors
gtsam::GaussianMixture | A conditional of gaussian mixtures indexed by discrete variables, as part of a Bayes Network
gtsam::GaussianMixtureFactor | Implementation of a discrete conditional mixture factor
gtsam::HybridConditional | Hybrid Conditional Density
gtsam::MixtureFactor | Implementation of a discrete conditional mixture factor
gtsam::NonlinearFactor | Nonlinear factor base class
gtsam::SmartFactorBase< PinholePose< CALIBRATION > > |
gtsam::SmartProjectionFactor< PinholePose< CALIBRATION > > |
gtsam::SmartProjectionPoseFactor< CALIBRATION > | If you are using the factor, please cite: L
gtsam::AntiFactor | A class for downdating an existing factor from a graph
gtsam::KarcherMeanFactor< T > | The KarcherMeanFactor creates a constraint on all SO(n) variables with given keys that the Karcher mean (see above) will stay the same
gtsam::LinearContainerFactor | Dummy version of a generic linear factor to be injected into a nonlinear factor graph
gtsam::NoiseModelFactor | A nonlinear sum-of-squares factor with a zero-mean noise model implementing the density \( P(z|x) \propto exp -0.5*|z-h(x)|^2_C \) Templated on the parameter type X and the values structure Values There is no return type specified for h(x)
gtsam::ExpressionFactor< typename Bearing< A1, A2 >::result_type > |
gtsam::ExpressionFactorN< typename Bearing< A1, A2 >::result_type, A1, A2 > |
gtsam::ExpressionFactor< BearingRange< A1, A2 > > |
gtsam::ExpressionFactorN< BearingRange< A1, A2 >, A1, A2 > |
gtsam::BearingRangeFactor< A1, A2, B, R > | Binary factor for a bearing/range measurement
gtsam::ExpressionFactor< double > |
gtsam::ExpressionFactorN< double, A1, A1 > |
gtsam::RangeFactor< A1, A2, T > | Binary factor for a range measurement Works for any two types A1,A2 for which the functor Range<A1,A2>() is defined
gtsam::ExpressionFactor< typename Range< A1, A1 >::result_type > |
gtsam::ExpressionFactorN< typename Range< A1, A1 >::result_type, A1, A1 > |
gtsam::NoiseModelFactorN< Rot3, Rot3, Vector3 > |
gtsam::AHRSFactor |
gtsam::NoiseModelFactorN< Rot3 > |
gtsam::MagFactor1 | Factor to estimate rotation given magnetometer reading This version uses model measured bM = scale * bRn * direction + bias and assumes scale, direction, and the bias are given
gtsam::Rot3AttitudeFactor | Version of AttitudeFactor for Rot3
gtsam::RotateDirectionsFactor | Factor on unknown rotation iRc that relates two directions c Directions provide less constraints than a full rotation
gtsam::RotateFactor | Factor on unknown rotation iRC that relates two incremental rotations c1Rc2 = iRc' * i1Ri2 * iRc Which we can write (see doc/math.lyx) e^[z] = iRc' * e^[p] * iRc = e^([iRc'*p]) with z and p measured and predicted angular velocities, and hence p = iRc * z
gtsam::NoiseModelFactorN< Pose3 > |
gtsam::GPSFactor | Prior on position in a Cartesian frame
gtsam::Pose3AttitudeFactor | Version of AttitudeFactor for Pose3
gtsam::NoiseModelFactorN< Pose3, double > |
gtsam::BarometricFactor | Prior on height in a cartesian frame
gtsam::NoiseModelFactorN< Pose3, Vector3, Pose3, Vector3, imuBias::ConstantBias, imuBias::ConstantBias > |
gtsam::CombinedImuFactor | CombinedImuFactor is a 6-ways factor involving previous state (pose and velocity of the vehicle, as well as bias at previous time step), and current state (pose, velocity, bias at current time step)
gtsam::NoiseModelFactorN< NavState, NavState > |
gtsam::ConstantVelocityFactor | Binary factor for applying a constant velocity model to a moving body represented as a NavState
gtsam::NoiseModelFactorN< NavState > |
gtsam::GPSFactor2 | Version of GPSFactor for NavState
gtsam::NoiseModelFactorN< Pose3, Vector3, Pose3, Vector3, imuBias::ConstantBias > |
gtsam::ImuFactor | ImuFactor is a 5-ways factor involving previous state (pose and velocity of the vehicle at previous time step), current state (pose and velocity at current time step), and the bias estimate
gtsam::NoiseModelFactorN< NavState, NavState, imuBias::ConstantBias > |
gtsam::ImuFactor2 | ImuFactor2 is a ternary factor that uses NavStates rather than Pose/Velocity
gtsam::NoiseModelFactorN< POSE > |
gtsam::MagPoseFactor< POSE > | Factor to estimate rotation of a Pose2 or Pose3 given a magnetometer reading
gtsam::PoseRotationPrior< POSE > |
gtsam::PoseTranslationPrior< POSE > | A prior on the translation part of a pose
gtsam::NoiseModelFactorN< T > |
gtsam::FunctorizedFactor< T, ParameterMatrix< traits< T >::dimension > > |
gtsam::ManifoldEvaluationFactor< BASIS, T > | For a measurement value of type T i.e
gtsam::FunctorizedFactor< R, T > | Factor which evaluates provided unary functor and uses the result to compute error with respect to the provided measurement
gtsam::NoiseModelFactorN< T1, T2 > |
gtsam::FunctorizedFactor2< R, T1, T2 > | Factor which evaluates provided binary functor and uses the result to compute error with respect to the provided measurement
gtsam::NoiseModelFactorN< VALUE > |
gtsam::BoundingConstraint1< VALUE > | Unary inequality constraint forcing a scalar to be greater/less than a fixed threshold
gtsam::NonlinearEquality< VALUE > | An equality factor that forces either one variable to a constant, or a set of variables to be equal to each other
gtsam::NonlinearEquality1< VALUE > | Simple unary equality constraint - fixes a value for a variable
gtsam::PriorFactor< VALUE > | A class for a soft prior on any Value type
gtsam::NoiseModelFactorN< T, T > |
gtsam::NonlinearEquality2< T > | Simple binary equality constraint - this constraint forces two variables to be the same
gtsam::NoiseModelFactorN< ValueTypes... > |
gtsam::NoiseModelFactorN< Point3, Point3 > |
gtsam::MagFactor2 | Factor to calibrate local Earth magnetic field as well as magnetometer bias This version uses model measured bM = bRn * nM + bias and optimizes for both nM and the bias, where nM is in units defined by magnetometer
gtsam::TranslationFactor | Binary factor for a relative translation direction measurement w_aZb
gtsam::NoiseModelFactorN< VALUE, VALUE > |
gtsam::BetweenFactor< VALUE > | A class for a measurement predicted by "between(config[key1],config[key2])"
gtsam::BetweenConstraint< VALUE > | Binary between constraint - forces between to a given value This constraint requires the underlying type to a Lie type
gtsam::NoiseModelFactorN< VALUE1, VALUE2 > |
gtsam::BoundingConstraint2< VALUE1, VALUE2 > | Binary scalar inequality constraint, with a similar value() function to implement for specific systems
gtsam::NoiseModelFactorN< Pose3, Pose3 > |
gtsam::EssentialMatrixConstraint | Binary factor between two Pose3 variables induced by an EssentialMatrix measurement
gtsam::NoiseModelFactorN< EssentialMatrix > |
gtsam::EssentialMatrixFactor | Factor that evaluates epipolar error p'Ep for given essential matrix
gtsam::NoiseModelFactorN< EssentialMatrix, double > |
gtsam::EssentialMatrixFactor2 | Binary factor that optimizes for E and inverse depth d: assumes measurement in image 2 is perfect, and returns re-projection error in image 1
gtsam::EssentialMatrixFactor3 | Binary factor that optimizes for E and inverse depth d: assumes measurement in image 2 is perfect, and returns re-projection error in image 1 This version takes an extrinsic rotation to allow for omni-directional rigs
gtsam::NoiseModelFactorN< EssentialMatrix, CALIBRATION > |
gtsam::EssentialMatrixFactor4< CALIBRATION > | Binary factor that optimizes for E and calibration K using the algebraic epipolar error (K^-1 pA)'E (K^-1 pB)
gtsam::NoiseModelFactorN< CAMERA, LANDMARK > |
gtsam::GeneralSFMFactor< CAMERA, LANDMARK > | Non-linear factor for a constraint derived from a 2D measurement
gtsam::NoiseModelFactorN< Pose3, Point3, CALIBRATION > |
gtsam::GeneralSFMFactor2< CALIBRATION > | Non-linear factor for a constraint derived from a 2D measurement
gtsam::NoiseModelFactorN< Pose3, OrientedPlane3 > |
gtsam::OrientedPlane3Factor | Factor to measure a planar landmark from a given pose
gtsam::NoiseModelFactorN< OrientedPlane3 > |
gtsam::OrientedPlane3DirectionPrior |
gtsam::NoiseModelFactorN< POSE, LANDMARK > |
gtsam::GenericProjectionFactor< POSE, LANDMARK, CALIBRATION > |
gtsam::GenericStereoFactor< POSE, LANDMARK > | A Generic Stereo Factor
gtsam::NoiseModelFactorN< POINT, TRANSFORM, POINT > |
gtsam::ReferenceFrameFactor< POINT, TRANSFORM > | A constraint between two landmarks in separate maps Templated on: Point : Type of landmark Transform : Transform variable class
gtsam::NoiseModelFactorN< Point3 > |
gtsam::TriangulationFactor< CAMERA > | Non-linear factor for a constraint derived from a 2D measurement
gtsam::NoiseModelFactorN< BASIS::Parameters > |
gtsam::FunctorizedFactor< double, BASIS::Parameters > |
gtsam::DerivativeFactor< BASIS > | A unary factor which enforces the evaluation of the derivative of a BASIS polynomial at a specified pointx is equal to the scalar measurement z
gtsam::NoiseModelFactorN< Rot, Rot > |
gtsam::FrobeniusBetweenFactor< Rot > | FrobeniusBetweenFactor is a BetweenFactor that evaluates the Frobenius norm of the rotation error between measured and predicted (rather than the Logmap of the error)
gtsam::FrobeniusFactor< Rot > | FrobeniusFactor calculates the Frobenius norm between rotation matrices
gtsam::NoiseModelFactorN< Rot > |
gtsam::FrobeniusPrior< Rot > | FrobeniusPrior calculates the Frobenius norm between a given matrix and an element of SO(3) or SO(4)
gtsam::NoiseModelFactorN< Pose3, Point3 > |
gtsam::GenericProjectionFactor< POSE, LANDMARK, CALIBRATION > | Non-linear factor for a constraint derived from a 2D measurement
gtsam::NoiseModelFactorN< Rot2 > |
gtsam::MagFactor | Factor to estimate rotation given magnetometer reading This version uses model measured bM = scale * bRn * direction + bias and assumes scale, direction, and the bias are given
gtsam::NoiseModelFactorN< double, Unit3, Point3 > |
gtsam::MagFactor3 | Factor to calibrate local Earth magnetic field as well as magnetometer bias This version uses model measured bM = scale * bRn * direction + bias and optimizes for both scale, direction, and the bias
gtsam::NoiseModelFactorN< SOn, SOn > |
gtsam::ShonanFactor< 2 > |
gtsam::ShonanFactor< 3 > |
gtsam::ShonanFactor< d > | ShonanFactor is a BetweenFactor that moves in SO(p), but will land on the SO(d) sub-manifold of SO(p) at the global minimum
gtsam::CustomFactor |
gtsam::ExpressionFactor< T > | Factor that supports arbitrary expressions via AD
gtsam::ExpressionFactorN< T, A1, A2 > |
gtsam::ExpressionFactorN< T, Args > | N-ary variadic template for ExpressionFactor meant as a base class for N-ary factors
gtsam::RangeFactor< A1, A2 > |
gtsam::RangeFactorWithTransform< A1, A2 > |
gtsam::BearingFactor< A1, A2, T > | Binary factor for a bearing measurement Works for any two types A1,A2 for which the functor Bearing<A1,A2>() is defined
gtsam::RangeFactorWithTransform< A1, A2, T > | Binary factor for a range measurement, with a transform applied
gtsam::NoiseModelFactorN< ValueTypes > | A convenient base class for creating your own NoiseModelFactor with n variables
gtsam::FunctorizedFactor< double, Vector > |
gtsam::EvaluationFactor< BASIS > | Factor for enforcing the scalar value of the polynomial BASIS representation at x is the same as the measurement z when using a pseudo-spectral parameterization
gtsam::FunctorizedFactor< Vector, ParameterMatrix< M > > |
gtsam::VectorDerivativeFactor< BASIS, M > | A unary factor which enforces the evaluation of the derivative of a BASIS polynomial at a specified point x is equal to the vector value z
gtsam::VectorEvaluationFactor< BASIS, M > | Unary factor for enforcing BASIS polynomial evaluation on a ParameterMatrix of size (M, N) is equal to a vector-valued measurement at the same point, when using a pseudo-spectral parameterization
gtsam::FunctorizedFactor< double, ParameterMatrix< P > > |
gtsam::ComponentDerivativeFactor< BASIS, P > | A unary factor which enforces the evaluation of the derivative of a BASIS polynomial is equal to the scalar value at a specific index i of a vector-valued measurement z
gtsam::VectorComponentFactor< BASIS, P > | Unary factor for enforcing BASIS polynomial evaluation on a ParameterMatrix of size (P, N) is equal to specified measurement at the same point, when using a pseudo-spectral parameterization
gtsam::FunctorizedFactor< double, typename BASIS::Parameters > |
gtsam::ShonanGaugeFactor | The ShonanGaugeFactor creates a constraint on a single SO(n) to avoid moving in the stabilizer
gtsam::SmartFactorBase< CAMERA > | Base class for smart factors
gtsam::SmartProjectionFactor< Camera > |
gtsam::SmartProjectionFactor< CAMERA > | SmartProjectionFactor: triangulates point and keeps an estimate of it around
gtsam::SmartProjectionRigFactor< CAMERA > | If you are using the factor, please cite: L
gtsam::WhiteNoiseFactor | Binary factor to estimate parameters of zero-mean Gaussian white noise
gtsam::SymbolicFactor | SymbolicFactor represents a symbolic factor that specifies graph topology but is not associated with any numerical function
gtsam::SymbolicConditional | SymbolicConditional is a conditional with keys but no probability data, produced by symbolic elimination of SymbolicFactor
gtsam::FactorGraph< FACTOR > | A factor graph is a bipartite graph with factor nodes connected to variable nodes
gtsam::BayesNet< DiscreteConditional > |
gtsam::DiscreteBayesNet | A Bayes net made from discrete conditional distributions
gtsam::BayesNet< DiscreteLookupTable > |
gtsam::DiscreteLookupDAG | A DAG made from lookup tables, as defined above
gtsam::BayesNet< HybridConditional > |
gtsam::HybridBayesNet | A hybrid Bayes net is a collection of HybridConditionals, which can have discrete conditionals, Gaussian mixtures, or pure Gaussian conditionals
gtsam::BayesNet< GaussianConditional > |
gtsam::GaussianBayesNet | GaussianBayesNet is a Bayes net made from linear-Gaussian conditionals
gtsam::BayesNet< SymbolicConditional > |
gtsam::SymbolicBayesNet | A SymbolicBayesNet is a Bayes Net of purely symbolic conditionals
gtsam::FactorGraph< CONDITIONAL > |
gtsam::BayesNet< CONDITIONAL > | A BayesNet is a tree of conditionals, stored in elimination order
gtsam::FactorGraph< DiscreteFactor > |
gtsam::DiscreteFactorGraph | A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e
gtsam::FactorGraph< Factor > |
gtsam::ConstructorTraversalData< BAYESTREE, GRAPH, ETREE_NODE >::SymbolicFactors |
gtsam::HybridFactorGraph | Hybrid Factor Graph Factor graph with utilities for hybrid factors
gtsam::HybridGaussianFactorGraph |
gtsam::HybridNonlinearFactorGraph |
gtsam::FactorGraph< GaussianFactor > |
gtsam::GaussianFactorGraph | A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e
gtsam::FactorGraph< NonlinearFactor > |
gtsam::NonlinearFactorGraph |
gtsam::ExpressionFactorGraph | Factor graph that supports adding ExpressionFactors directly
gtsam::FactorGraph< SymbolicFactor > |
gtsam::SymbolicFactorGraph | Symbolic Factor Graph
std::false_type |
gtsam::needs_eigen_aligned_allocator< T, void_t< typename T::_eigen_aligned_allocator_trait > > |
gtsam::needs_eigen_aligned_allocator< typename, typename > | A SFINAE trait to mark classes that need special alignment
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)
FastVector |
gtsam::Scatter | Scatter is an intermediate data structure used when building a HessianFactor incrementally, to get the keys in the right order
gtsam::FitBasis< Basis > | Class that does regression via least squares Example usage: size_t N = 3; auto fit = FitBasis<Chebyshev2>(data_points, noise_model, N); Vector coefficients = fit.parameters();
gtsam::FixedDimension< T > | Give fixed size dimension of a type, fails at compile time if dynamic
gtsam::internal::FixedSizeMatrix< Y, X > |
std::function |
gtsam::equals< V > | Template to create a binary predicate
gtsam::equals_star< V > | Binary predicate on shared pointers
gtsam::G_x1< X1, X2 > | Helper class that computes the derivative of f w.r.t
GaussianConditional | Normalization constant
gtsam::GaussianFactorGraphSystem | System class needed for calling preconditionedConjugateGradient
gtsam::internal::GetDimensionImpl< Class, N > | Extra manifold traits for fixed-dimension types
gtsam::internal::GetDimensionImpl< BearingRange< A1, A2 >, BearingRange< A1, A2 >::dimension > |
gtsam::internal::ManifoldTraits< BearingRange< A1, A2 > > |
gtsam::traits< BearingRange< A1, A2 > > |
gtsam::internal::GetDimensionImpl< Cal3_S2, Cal3_S2::dimension > |
gtsam::internal::ManifoldTraits< Cal3_S2 > |
gtsam::internal::Manifold< Cal3_S2 > |
gtsam::traits< Cal3_S2 > |
gtsam::traits< const Cal3_S2 > |
gtsam::internal::GetDimensionImpl< Cal3_S2Stereo, Cal3_S2Stereo::dimension > |
gtsam::internal::ManifoldTraits< Cal3_S2Stereo > |
gtsam::internal::Manifold< Cal3_S2Stereo > |
gtsam::traits< Cal3_S2Stereo > |
gtsam::traits< const Cal3_S2Stereo > |
gtsam::internal::GetDimensionImpl< Cal3Bundler, Cal3Bundler::dimension > |
gtsam::internal::ManifoldTraits< Cal3Bundler > |
gtsam::internal::Manifold< Cal3Bundler > |
gtsam::traits< Cal3Bundler > |
gtsam::traits< const Cal3Bundler > |
gtsam::internal::GetDimensionImpl< Cal3DS2, Cal3DS2::dimension > |
gtsam::internal::ManifoldTraits< Cal3DS2 > |
gtsam::internal::Manifold< Cal3DS2 > |
gtsam::traits< Cal3DS2 > |
gtsam::traits< const Cal3DS2 > |
gtsam::internal::GetDimensionImpl< Cal3Fisheye, Cal3Fisheye::dimension > |
gtsam::internal::ManifoldTraits< Cal3Fisheye > |
gtsam::internal::Manifold< Cal3Fisheye > |
gtsam::traits< Cal3Fisheye > |
gtsam::traits< const Cal3Fisheye > |
gtsam::internal::GetDimensionImpl< Cal3Unified, Cal3Unified::dimension > |
gtsam::internal::ManifoldTraits< Cal3Unified > |
gtsam::internal::Manifold< Cal3Unified > |
gtsam::traits< Cal3Unified > |
gtsam::traits< const Cal3Unified > |
gtsam::internal::GetDimensionImpl< CalibratedCamera, CalibratedCamera::dimension > |
gtsam::internal::ManifoldTraits< CalibratedCamera > |
gtsam::internal::Manifold< CalibratedCamera > |
gtsam::traits< CalibratedCamera > |
gtsam::traits< const CalibratedCamera > |
gtsam::internal::GetDimensionImpl< Class, Class::dimension > |
gtsam::internal::LieGroupTraits< Class > | A helper class that implements the traits interface for GTSAM lie groups
gtsam::internal::LieGroup< Class > | Both LieGroupTraits and Testable
gtsam::internal::ManifoldTraits< Class > | A helper that implements the traits interface for GTSAM manifolds
gtsam::internal::Manifold< Class > | Both ManifoldTraits and Testable
gtsam::internal::GetDimensionImpl< Class, Eigen::Dynamic > | Extra manifold traits for variable-dimension types
gtsam::internal::GetDimensionImpl< EssentialMatrix, EssentialMatrix::dimension > |
gtsam::internal::ManifoldTraits< EssentialMatrix > |
gtsam::internal::Manifold< EssentialMatrix > |
gtsam::traits< EssentialMatrix > |
gtsam::traits< const EssentialMatrix > |
gtsam::internal::GetDimensionImpl< Line3, Line3::dimension > |
gtsam::internal::ManifoldTraits< Line3 > |
gtsam::internal::Manifold< Line3 > |
gtsam::traits< Line3 > |
gtsam::traits< const Line3 > |
gtsam::internal::GetDimensionImpl< NavState, NavState::dimension > |
gtsam::internal::ManifoldTraits< NavState > |
gtsam::internal::Manifold< NavState > |
gtsam::traits< NavState > |
gtsam::internal::GetDimensionImpl< OrientedPlane3, OrientedPlane3::dimension > |
gtsam::internal::ManifoldTraits< OrientedPlane3 > |
gtsam::internal::Manifold< OrientedPlane3 > |
gtsam::traits< OrientedPlane3 > |
gtsam::traits< const OrientedPlane3 > |
gtsam::internal::GetDimensionImpl< PinholeCamera< Calibration >, PinholeCamera< Calibration >::dimension > |
gtsam::internal::ManifoldTraits< PinholeCamera< Calibration > > |
gtsam::internal::Manifold< PinholeCamera< Calibration > > |
gtsam::traits< PinholeCamera< Calibration > > |
gtsam::traits< const PinholeCamera< Calibration > > |
gtsam::internal::GetDimensionImpl< PinholePose< CALIBRATION >, PinholePose< CALIBRATION >::dimension > |
gtsam::internal::ManifoldTraits< PinholePose< CALIBRATION > > |
gtsam::internal::Manifold< PinholePose< CALIBRATION > > |
gtsam::traits< PinholePose< CALIBRATION > > |
gtsam::traits< const PinholePose< CALIBRATION > > |
gtsam::internal::GetDimensionImpl< Pose2, Pose2::dimension > |
gtsam::internal::LieGroupTraits< Pose2 > |
gtsam::internal::LieGroup< Pose2 > |
gtsam::traits< Pose2 > |
gtsam::traits< const Pose2 > |
gtsam::internal::GetDimensionImpl< Pose3, Pose3::dimension > |
gtsam::internal::LieGroupTraits< Pose3 > |
gtsam::internal::LieGroup< Pose3 > |
gtsam::traits< Pose3 > |
gtsam::traits< SphericalCamera > |
gtsam::traits< const Pose3 > |
gtsam::traits< const SphericalCamera > |
gtsam::internal::GetDimensionImpl< ProductLieGroup< G, H >, ProductLieGroup< G, H >::dimension > |
gtsam::internal::LieGroupTraits< ProductLieGroup< G, H > > |
gtsam::traits< ProductLieGroup< G, H > > |
gtsam::internal::GetDimensionImpl< Rot2, Rot2::dimension > |
gtsam::internal::LieGroupTraits< Rot2 > |
gtsam::internal::LieGroup< Rot2 > |
gtsam::traits< Rot2 > |
gtsam::traits< const Rot2 > |
gtsam::internal::GetDimensionImpl< Rot3, Rot3::dimension > |
gtsam::internal::LieGroupTraits< Rot3 > |
gtsam::internal::LieGroup< Rot3 > |
gtsam::traits< Rot3 > |
gtsam::traits< const Rot3 > |
gtsam::internal::GetDimensionImpl< Similarity2, Similarity2::dimension > |
gtsam::internal::LieGroupTraits< Similarity2 > |
gtsam::internal::LieGroup< Similarity2 > |
gtsam::traits< Similarity2 > |
gtsam::traits< const Similarity2 > |
gtsam::internal::GetDimensionImpl< Similarity3, Similarity3::dimension > |
gtsam::internal::LieGroupTraits< Similarity3 > |
gtsam::internal::LieGroup< Similarity3 > |
gtsam::traits< Similarity3 > |
gtsam::traits< const Similarity3 > |
gtsam::internal::GetDimensionImpl< SO3, SO3::dimension > |
gtsam::internal::LieGroupTraits< SO3 > |
gtsam::internal::LieGroup< SO3 > |
gtsam::traits< SO3 > |
gtsam::traits< const SO3 > |
gtsam::internal::GetDimensionImpl< SO4, SO4::dimension > |
gtsam::internal::LieGroupTraits< SO4 > |
gtsam::internal::LieGroup< SO4 > |
gtsam::traits< SO4 > |
gtsam::traits< const SO4 > |
gtsam::internal::GetDimensionImpl< SO< N >, SO< N >::dimension > |
gtsam::internal::LieGroupTraits< SO< N > > |
gtsam::internal::LieGroup< SO< N > > |
gtsam::traits< SO< N > > |
gtsam::traits< const SO< N > > |
gtsam::internal::GetDimensionImpl< StereoCamera, StereoCamera::dimension > |
gtsam::internal::ManifoldTraits< StereoCamera > |
gtsam::internal::Manifold< StereoCamera > |
gtsam::traits< StereoCamera > |
gtsam::traits< const StereoCamera > |
gtsam::internal::GetDimensionImpl< Unit3, Unit3::dimension > |
gtsam::internal::ManifoldTraits< Unit3 > |
gtsam::internal::Manifold< Unit3 > |
gtsam::traits< Unit3 > |
gtsam::traits< const Unit3 > |
gtsam::GncOptimizer< GncParameters > |
gtsam::GncParams< BaseOptimizerParameters > |
gtsam::group_tag | Tag to assert a type is a group
gtsam::lie_group_tag | Tag to assert a type is a Lie group
gtsam::vector_space_tag | Tag to assert a type is a vector space
gtsam::internal::handle< ValueType > |
gtsam::internal::handle< Eigen::Matrix< double, M, N > > |
gtsam::internal::handle_matrix< MatrixType, isDynamic > |
gtsam::internal::handle_matrix< Eigen::Matrix< double, M, N >, false > |
gtsam::internal::handle_matrix< Eigen::Matrix< double, M, N >, true > |
gtsam::HasBearing< A1, A2, RT > |
gtsam::HasBearing< Pose2, T, Rot2 > |
gtsam::Bearing< Pose2, T > |
gtsam::HasBearing< Pose3, Point3, Unit3 > |
gtsam::Bearing< Pose3, Point3 > |
gtsam::HasBearing< Pose3, Pose3, Unit3 > |
gtsam::Bearing< Pose3, Pose3 > |
gtsam::internal::HasManifoldPrereqs< Class > | Requirements on type to pass it to Manifold template below
gtsam::HasRange< A1, A2, RT > |
gtsam::HasRange< CalibratedCamera, T, double > |
gtsam::Range< CalibratedCamera, T > |
gtsam::HasRange< PinholeCamera< Calibration >, T, double > |
gtsam::Range< PinholeCamera< Calibration >, T > |
gtsam::HasRange< Pose2, T, double > |
gtsam::Range< Pose2, T > |
gtsam::HasRange< Pose3, T, double > |
gtsam::Range< Pose3, T > |
gtsam::HasTestablePrereqs< T > | Requirements on type to pass it to Testable template below
gtsam::internal::HasVectorSpacePrereqs< Class > | Requirements on type to pass it to Manifold template below
gtsam::HybridNonlinearISAM | Wrapper class to manage ISAM in a nonlinear context
gtsam::HybridSmoother |
gtsam::HybridValues | HybridValues represents a collection of DiscreteValues and VectorValues
HybridValues | Error
boost::mp11::index_sequence< Ints > |
boost::mp11::index_sequence< 0 > |
boost::mp11::make_index_sequence< 1 > |
boost::mp11::index_sequence< I1...,(sizeof...(I1)+I2)... > |
boost::mp11::detail::_merge_and_renumber< index_sequence< I1... >, index_sequence< I2... > > |
boost::mp11::index_sequence<> |
boost::mp11::make_index_sequence< 0 > |
gtsam::InitializePose3 |
gtsam::ISAM2DoglegParams | Parameters for ISAM2 using Dogleg optimization
gtsam::ISAM2GaussNewtonParams | Parameters for ISAM2 using Gauss-Newton optimization
gtsam::ISAM2Params |
gtsam::ISAM2Result | This struct is returned from ISAM2::update() and contains information about the update that is useful for determining whether the solution is converging, and about how much work was required for the update
gtsam::ISAM2UpdateParams | This struct is used by ISAM2::update() to pass additional parameters to give the user a fine-grained control on how factors and relinearized, etc
gtsam::IsGroup< G > | Group Concept
gtsam::IsGroup< T > |
gtsam::IsLieGroup< T > | Lie Group Concept
gtsam::IsVectorSpace< T > | Vector Space concept
IsManifold |
gtsam::IsLieGroup< T > | Lie Group Concept
gtsam::IsTestable< T > | A testable concept check that should be placed in applicable unit tests and in generic algorithms
gtsam::DoglegOptimizerImpl::IterationResult |
gtsam::IterativeOptimizationParameters | Parameters for iterative linear solvers
gtsam::ConjugateGradientParameters | Parameters for the conjugate gradient method
gtsam::PCGSolverParameters | Parameters for PCG
gtsam::SubgraphSolverParameters |
gtsam::IterativeSolver | Base class for Iterative Solvers like SubgraphSolver
gtsam::PCGSolver | A virtual base class for the preconditioned conjugate gradient solver
gtsam::SubgraphSolver | This class implements the linear SPCG solver presented in Dellaert et al in IROS'10
gtsam::vector< T >::iterator | STL iterator class
map< K, T >::iterator | STL iterator class
string::iterator | STL iterator class
unordered_map< K, T >::iterator | STL iterator class
unordered_set< K >::iterator | STL iterator class
vector< T >::iterator | STL iterator class
JacobianFactor | In Gaussian factors, the error function returns either the negative log-likelihood, e.g., 0.5*(A*x-b)'D(A*x-b) for a negative log-density, e.g., 0.5*(A*x-b)'D(A*x-b) - log(k) for a
gtsam::JointMarginal | A class to store and access a joint marginal, returned from Marginals::jointMarginalCovariance and Marginals::jointMarginalInformation
gtsam::KalmanFilter | Kalman Filter class
gtsam::key_formatter | Output stream manipulator that will format gtsam::Keys according to the given KeyFormatter, as long as Key values are wrapped in a gtsam::StreamedKey
gtsam::KeyInfoEntry | Handy data structure for iterative solvers key to (index, dimension, start)
gtsam::gtsfm::Keypoints |
gtsam::Values::KeyValuePair | A key-value pair, which you get by dereferencing iterators
KeyVector |
gtsam::Ordering |
gtsam::LabeledSymbol | Customized version of gtsam::Symbol for multi-robot use
gtsam::LieGroup< Class, N > | A CRTP helper class that implements Lie group methods Prerequisites: methods operator*, inverse, and AdjointMap, as well as a ChartAtOrigin struct that will be used to define the manifold Chart To use, simply derive, but also say "using LieGroup<Class,N>::inverse" For derivative math, see doc/math.pdf
gtsam::LieGroup< Pose2, 3 > |
gtsam::Pose2 | A 2D pose (Point2,Rot2)
gtsam::LieGroup< Pose3, 6 > |
gtsam::Pose3 | A 3D pose (R,t) : (Rot3,Point3)
gtsam::LieGroup< Rot2, 1 > |
gtsam::Rot2 | Rotation matrix NOTE: the angle theta is in radians unless explicitly stated
gtsam::LieGroup< Rot3, 3 > |
gtsam::Rot3 | Rot3 is a 3D rotation represented as a rotation matrix if the preprocessor symbol GTSAM_USE_QUATERNIONS is not defined, or as a quaternion if it is defined
gtsam::LieGroup< Similarity2, 4 > |
gtsam::Similarity2 | 2D similarity transform
gtsam::LieGroup< Similarity3, 7 > |
gtsam::Similarity3 | 3D similarity transform
gtsam::LieGroup< SO< N >, internal::DimensionSO(N)> |
gtsam::SO< 3 > |
gtsam::SO< 4 > |
gtsam::SO< Eigen::Dynamic > |
gtsam::SO< N > | Manifold of special orthogonal rotation matrices SO<N>
gtsam::Line3 | A 3D line (R,a,b) : (Rot3,Scalar,Scalar)
std::list< T > | STL class
gtsam::FastList< sharedClique > |
gtsam::FastList< Key > |
gtsam::FastList< Vector > |
gtsam::FastList< VALUE > | FastList is a thin wrapper around std::list that uses the boost fast_pool_allocator instead of the default STL allocator
gtsam::ListOfOneContainer< T > | A helper class that behaves as a container with one element, and works with boost::range
gtsam::MakeJacobian< T, A > | : meta-function to generate Jacobian
gtsam::MakeOptionalJacobian< T, A > | : meta-function to generate JacobianTA optional reference Used mainly by Expressions
gtsam::manifold_tag | Tag to assert a type is a manifold
gtsam::lie_group_tag | Tag to assert a type is a Lie group
map< K, T > | STL class
gtsam::Assignment< Key > |
gtsam::DiscreteValues | A map from keys to values
gtsam::FastMap< size_t, boost::shared_ptr< TimingOutline > > |
gtsam::FastMap< Key, int > |
gtsam::FastMap< Key, FactorIndices > |
gtsam::FastMap< Key, FastVector< size_t > > |
gtsam::VariableSlots | A combined factor is assembled as one block of rows for each component factor
gtsam::FastMap< char, Vector > |
gtsam::FastMap< Key, VariableStatus > |
gtsam::FastMap< Key, size_t > |
gtsam::FastMap< Key, VectorValues::const_iterator > |
gtsam::Assignment< L > | An assignment from labels to value index (size_t)
gtsam::FastMap< KEY, VALUE > | FastMap is a thin wrapper around std::map that uses the boost fast_pool_allocator instead of the default STL allocator
gtsam::ConcurrentMap< Key, sharedClique > |
gtsam::ConcurrentMap< Key, Vector > |
gtsam::ConcurrentMap< KEY, VALUE > | FastMap is a thin wrapper around std::map that uses the boost fast_pool_allocator instead of the default STL allocator
gtsam::KeyInfo | Handy data structure for iterative solvers
gtsam::PredecessorMap< KEY > | Map from variable key to parent key
std::map< K, T > | STL class
gtsam::Assignment< Key > |
gtsam::FastMap< size_t, boost::shared_ptr< TimingOutline > > |
gtsam::FastMap< Key, int > |
gtsam::FastMap< Key, FactorIndices > |
gtsam::FastMap< Key, FastVector< size_t > > |
gtsam::FastMap< char, Vector > |
gtsam::FastMap< Key, VariableStatus > |
gtsam::FastMap< Key, size_t > |
gtsam::FastMap< Key, VectorValues::const_iterator > |
gtsam::Assignment< L > | An assignment from labels to value index (size_t)
gtsam::FastMap< KEY, VALUE > | FastMap is a thin wrapper around std::map that uses the boost fast_pool_allocator instead of the default STL allocator
gtsam::KeyInfo | Handy data structure for iterative solvers
gtsam::PredecessorMap< KEY > | Map from variable key to parent key
gtsam::Marginals | A class for computing Gaussian marginals of variables in a NonlinearFactorGraph
gtsam::MetisIndex | Converts a factor graph into the Compressed Sparse Row format for use in METIS algorithms
gtsam::MFAS | To solve a Minimum feedback arc set (MFAS) problem
gtsam::multiplicative_group_tag | Group operator syntax flavors
internal::MultiplicativeGroupTraits |
gtsam::traits< DirectProduct< G, H > > |
gtsam::MultiplyWithInverse< N > | Functor that implements multiplication of a vector b with the inverse of a matrix A
gtsam::MultiplyWithInverseFunction< T, N > | Functor that implements multiplication with the inverse of a matrix, itself the result of a function f
gtsam::NavState | Navigation state: Pose (rotation, translation) + velocity NOTE(frank): it does not make sense to make this a Lie group, but it is a 9D manifold
gtsam::DecisionTree< L, Y >::Node | ---------------------— Node base class ------------------------—
gtsam::DecisionTree< L, Y >::Choice< L, Y > |
gtsam::DecisionTree< L, Y >::Leaf< L, Y > |
gtsam::EliminationTree< BAYESNET, GRAPH >::Node |
NoiseModelFactor | Noise model to the factor, and calculates the error by asking the user to implement the method
gtsam::detail::NoiseModelFactorAliases< typename,... > | Convenience base class to add aliases X1, X2, ..., X6 -> ValueType<N>
gtsam::detail::NoiseModelFactorAliases< T1 > |
gtsam::detail::NoiseModelFactorAliases< T1, T2 > |
gtsam::detail::NoiseModelFactorAliases< T1, T2, T3 > |
gtsam::detail::NoiseModelFactorAliases< T1, T2, T3, T4 > |
gtsam::detail::NoiseModelFactorAliases< T1, T2, T3, T4, T5 > |
gtsam::detail::NoiseModelFactorAliases< T1, T2, T3, T4, T5, T6, TExtra... > |
gtsam::detail::NoiseModelFactorAliases< ValueTypes... > |
gtsam::NoiseModelFactorN< Rot3, Rot3, Vector3 > |
gtsam::NoiseModelFactorN< Rot3 > |
gtsam::NoiseModelFactorN< Pose3 > |
gtsam::NoiseModelFactorN< Pose3, double > |
gtsam::NoiseModelFactorN< Pose3, Vector3, Pose3, Vector3, imuBias::ConstantBias, imuBias::ConstantBias > |
gtsam::NoiseModelFactorN< NavState, NavState > |
gtsam::NoiseModelFactorN< NavState > |
gtsam::NoiseModelFactorN< Pose3, Vector3, Pose3, Vector3, imuBias::ConstantBias > |
gtsam::NoiseModelFactorN< NavState, NavState, imuBias::ConstantBias > |
gtsam::NoiseModelFactorN< POSE > |
gtsam::NoiseModelFactorN< T > |
gtsam::NoiseModelFactorN< T1, T2 > |
gtsam::NoiseModelFactorN< VALUE > |
gtsam::NoiseModelFactorN< T, T > |
gtsam::NoiseModelFactorN< ValueTypes... > |
gtsam::NoiseModelFactorN< Point3, Point3 > |
gtsam::NoiseModelFactorN< VALUE, VALUE > |
gtsam::NoiseModelFactorN< VALUE1, VALUE2 > |
gtsam::NoiseModelFactorN< Pose3, Pose3 > |
gtsam::NoiseModelFactorN< EssentialMatrix > |
gtsam::NoiseModelFactorN< EssentialMatrix, double > |
gtsam::NoiseModelFactorN< EssentialMatrix, CALIBRATION > |
gtsam::NoiseModelFactorN< CAMERA, LANDMARK > |
gtsam::NoiseModelFactorN< Pose3, Point3, CALIBRATION > |
gtsam::NoiseModelFactorN< Pose3, OrientedPlane3 > |
gtsam::NoiseModelFactorN< OrientedPlane3 > |
gtsam::NoiseModelFactorN< POSE, LANDMARK > |
gtsam::NoiseModelFactorN< POINT, TRANSFORM, POINT > |
gtsam::NoiseModelFactorN< Point3 > |
gtsam::NoiseModelFactorN< BASIS::Parameters > |
gtsam::NoiseModelFactorN< Rot, Rot > |
gtsam::NoiseModelFactorN< Rot > |
gtsam::NoiseModelFactorN< Pose3, Point3 > |
gtsam::NoiseModelFactorN< Rot2 > |
gtsam::NoiseModelFactorN< double, Unit3, Point3 > |
gtsam::NoiseModelFactorN< SOn, SOn > |
gtsam::NoiseModelFactorN< ValueTypes > | A convenient base class for creating your own NoiseModelFactor with n variables
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::DoglegOptimizer | This class performs Dogleg nonlinear optimization
gtsam::GaussNewtonOptimizer | This class performs Gauss-Newton nonlinear optimization
gtsam::LevenbergMarquardtOptimizer | This class performs Levenberg-Marquardt nonlinear optimization
gtsam::NonlinearConjugateGradientOptimizer | An implementation of the nonlinear CG method using the template below
gtsam::NonlinearOptimizerParams | The common parameters for Nonlinear optimizers
gtsam::DoglegParams | Parameters for Levenberg-Marquardt optimization
gtsam::GaussNewtonParams | Parameters for Gauss-Newton optimization, inherits from NonlinearOptimizationParams
gtsam::LevenbergMarquardtParams | Parameters for Levenberg-Marquardt optimization
Only | Symbolic elimination etc
gtsam::internal::linearAlgorithms::OptimizeClique< CLIQUE > | Pre-order visitor for back-substitution in a Bayes tree
gtsam::internal::linearAlgorithms::OptimizeData |
boost::optional |
gtsam::TriangulationResult | TriangulationResult is an optional point, along with the reasons why it is invalid
gtsam::OptionalJacobian< Rows, Cols > | OptionalJacobian is an Eigen::Ref like class that can take be constructed using either a fixed size or dynamic Eigen matrix
gtsam::OptionalJacobian< Eigen::Dynamic, Eigen::Dynamic > |
gtsam::OrientedPlane3 | Represents an infinite plane in 3D, which is composed of a planar normal and its perpendicular distance to the origin
ostream | STL class
pair< T1, T2 > | STL class
gtsam::DirectProduct |
gtsam::DirectSum< G, H > | Template to construct the direct sum of two additive groups Assumes existence of three additive operators for both groups
gtsam::IndexPair | Small utility class for representing a wrappable pairs of ints
gtsam::ProductLieGroup< G, H > | Template to construct the product Lie group of two other Lie groups Assumes Lie group structure for G and H
std::pair< T1, T2 > | STL class
gtsam::DirectProduct |
gtsam::DirectSum< G, H > | Template to construct the direct sum of two additive groups Assumes existence of three additive operators for both groups
gtsam::IndexPair | Small utility class for representing a wrappable pairs of ints
gtsam::ProductLieGroup< G, H > | Template to construct the product Lie group of two other Lie groups Assumes Lie group structure for G and H
gtsam::ParameterMatrix< M > | A matrix abstraction of MxN values at the Basis points
gtsam::DeltaImpl::PartialSolveResult |
gtsam::PinholeBase | A pinhole camera class that has a Pose3, functions as base class for all pinhole cameras
gtsam::PinholeBaseK< Calibration > |
gtsam::PinholeCamera< Calibration > | A pinhole camera class that has a Pose3 and a Calibration
gtsam::CalibratedCamera | A Calibrated camera class [R|-R't], calibration K=I
gtsam::PinholeBaseK< CALIBRATION > | A pinhole camera class that has a Pose3 and a fixed Calibration
gtsam::PinholeCamera< gtsam::Cal3_S2 > |
gtsam::PinholeCamera< gtsam::Cal3Bundler > |
gtsam::PinholeCamera< gtsam::Cal3DS2 > |
gtsam::PinholeCamera< gtsam::Cal3Unified > |
gtsam::PinholeCamera< gtsam::Cal3Fisheye > |
gtsam::PinholeCamera< Cal3Bundler > |
gtsam::PinholeCamera< CALIBRATION > |
gtsam::PinholePose< CALIBRATION > | A pinhole camera class that has a Pose3 and a fixed Calibration
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::PowerMethod< Operator > | Compute maximum Eigenpair with power method
gtsam::AcceleratedPowerMethod< Operator > | Compute maximum Eigenpair with accelerated power method
gtsam::Preconditioner |
gtsam::BlockJacobiPreconditioner |
gtsam::DummyPreconditioner |
gtsam::SubgraphPreconditioner | Subgraph conditioner class, as explained in the RSS 2010 submission
gtsam::PreconditionerParameters |
gtsam::BlockJacobiPreconditionerParameters |
gtsam::DummyPreconditionerParameters |
gtsam::SubgraphPreconditionerParameters |
gtsam::PreintegratedRotation | PreintegratedRotation is the base class for all PreintegratedMeasurements classes (in AHRSFactor, ImuFactor, and CombinedImuFactor)
gtsam::PreintegratedAhrsMeasurements | PreintegratedAHRSMeasurements accumulates (integrates) the Gyroscope measurements (rotation rates) and the corresponding covariance matrix
gtsam::PreintegratedRotationParams | Parameters for pre-integration: Usage: Create just a single Params and pass a shared pointer to the constructor
gtsam::PreintegrationParams | Parameters for pre-integration: Usage: Create just a single Params and pass a shared pointer to the constructor
gtsam::PreintegrationCombinedParams | Parameters for pre-integration using PreintegratedCombinedMeasurements: Usage: Create just a single Params and pass a shared pointer to the constructor
gtsam::PreintegrationBase | PreintegrationBase is the base class for PreintegratedMeasurements (in ImuFactor) and CombinedPreintegratedMeasurements (in CombinedImuFactor)
gtsam::ManifoldPreintegration | IMU pre-integration on NavSatet manifold
gtsam::PreintegratedCombinedMeasurements | PreintegratedCombinedMeasurements integrates the IMU measurements (rotation rates and accelerations) and the corresponding covariance matrix
gtsam::PreintegratedImuMeasurements | PreintegratedImuMeasurements accumulates (integrates) the IMU measurements (rotation rates and accelerations) and the corresponding covariance matrix
gtsam::TangentPreintegration | Integrate on the 9D tangent space of the NavState manifold
gtsam::Range< A1, A2 > |
gtsam::Range< Point2, Point2 > |
gtsam::Range< Point3, Point3 > |
gtsam::RedirectCout | For Python str()
gtsam::RefCallPushBack< C > | Helper
gtsam::DeltaImpl::ReorderingMode |
gtsam::Reshape< OutM, OutN, OutOptions, InM, InN, InOptions > | Reshape functor
gtsam::Reshape< M, M, InOptions, M, M, InOptions > | Reshape specialization that does nothing as shape stays the same (needed to not be ambiguous for square input equals square output)
gtsam::Reshape< M, N, InOptions, M, N, InOptions > | Reshape specialization that does nothing as shape stays the same
gtsam::Reshape< N, M, InOptions, M, N, InOptions > | Reshape specialization that does transpose
gtsam::vector< T >::reverse_iterator | STL iterator class
map< K, T >::reverse_iterator | STL iterator class
string::reverse_iterator | STL iterator class
unordered_map< K, T >::reverse_iterator | STL iterator class
unordered_set< K >::reverse_iterator | STL iterator class
vector< T >::reverse_iterator | STL iterator class
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
gtsam::Scenario | Simple trajectory simulator
gtsam::AcceleratingScenario | Accelerating from an arbitrary initial state, with optional rotation
gtsam::ConstantTwistScenario | Scenario with constant twist 3D trajectory
gtsam::ScenarioRunner |
gtsam::CombinedScenarioRunner |
std::set< K > | STL class
gtsam::FastSet< FactorIndex > |
gtsam::FastSet< Key > |
gtsam::FastSet< VALUE > | FastSet is a thin wrapper around std::set that uses the boost fast_pool_allocator instead of the default STL allocator
gtsam::SfmData | SfmData stores a bunch of SfmTracks
gtsam::SfmTrack2d | Track containing 2D measurements associated with a single 3D point
gtsam::SfmTrack |
gtsam::ShonanAveraging< d > | Class that implements Shonan Averaging from our ECCV'20 paper
gtsam::ShonanAveraging< 2 > |
gtsam::ShonanAveraging2 |
gtsam::ShonanAveraging< 3 > |
gtsam::ShonanAveraging3 |
gtsam::ShonanAveragingParameters< d > | Parameters governing optimization etc
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 dim
gtsam::SmartProjectionParams |
gtsam::SphericalCamera | A spherical camera class that has a Pose3 and measures bearing vectors
gtsam::StereoCamera | A stereo camera class, parameterize by left camera pose and stereo calibration
gtsam::StereoPoint2 | A 2D stereo point, v will be same for rectified images
gtsam::StreamedKey | To use the key_formatter on Keys, they must be wrapped in a StreamedKey
string | STL class
stringstream | STL class
gtsam::Subgraph |
gtsam::SubgraphBuilder |
gtsam::SubgraphBuilderParameters |
gtsam::Symbol | Character and index key used to refer to variables
gtsam::SymbolGenerator | Generates symbol shorthands with alternative names different than the one-letter predefined ones
gtsam::SymmetricBlockMatrix | This class stores a dense matrix and allows it to be accessed as a collection of blocks
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::Expression< T >::TernaryFunction< A1, A2, A3 > |
gtsam::Testable< T > | A helper that implements the traits interface for GTSAM types
gtsam::Testable< AlgebraicDecisionTree< T > > |
gtsam::traits< AlgebraicDecisionTree< T > > |
gtsam::Testable< BearingFactor< A1, A2, T > > |
gtsam::traits< BearingFactor< A1, A2, T > > | Traits
gtsam::Testable< BearingRange< A1, A2 > > |
gtsam::traits< BearingRange< A1, A2 > > |
gtsam::Testable< BearingRangeFactor< A1, A2, B, R > > |
gtsam::traits< BearingRangeFactor< A1, A2, B, R > > | Traits
gtsam::Testable< BetweenConstraint< VALUE > > |
gtsam::traits< BetweenConstraint< VALUE > > | Traits
gtsam::Testable< BetweenFactor< VALUE > > |
gtsam::traits< BetweenFactor< VALUE > > | Traits
gtsam::Testable< BinaryJacobianFactor< M, N1, N2 > > |
gtsam::traits< BinaryJacobianFactor< M, N1, N2 > > |
gtsam::Testable< Cal3_S2 > |
gtsam::internal::Manifold< Cal3_S2 > |
gtsam::Testable< Cal3_S2Stereo > |
gtsam::internal::Manifold< Cal3_S2Stereo > |
gtsam::Testable< Cal3Bundler > |
gtsam::internal::Manifold< Cal3Bundler > |
gtsam::Testable< Cal3DS2 > |
gtsam::internal::Manifold< Cal3DS2 > |
gtsam::Testable< Cal3Fisheye > |
gtsam::internal::Manifold< Cal3Fisheye > |
gtsam::Testable< Cal3Unified > |
gtsam::internal::Manifold< Cal3Unified > |
gtsam::Testable< CalibratedCamera > |
gtsam::internal::Manifold< CalibratedCamera > |
gtsam::Testable< CameraSet< CAMERA > > |
gtsam::traits< CameraSet< CAMERA > > |
gtsam::traits< const CameraSet< CAMERA > > |
gtsam::Testable< Class > |
gtsam::internal::LieGroup< Class > | Both LieGroupTraits and Testable
gtsam::internal::Manifold< Class > | Both ManifoldTraits and Testable
gtsam::internal::VectorSpace< Class > | VectorSpace provides both Testable and VectorSpaceTraits
gtsam::Testable< CombinedImuFactor > |
gtsam::traits< CombinedImuFactor > |
gtsam::Testable< Cyclic< N > > |
gtsam::traits< Cyclic< N > > | Define cyclic group to be a model of the Additive Group concept
gtsam::Testable< DecisionTree< L, Y > > |
gtsam::traits< DecisionTree< L, Y > > |
gtsam::Testable< DecisionTreeFactor > |
gtsam::traits< DecisionTreeFactor > |
gtsam::Testable< DiscreteBayesNet > |
gtsam::traits< DiscreteBayesNet > |
gtsam::Testable< DiscreteConditional > |
gtsam::traits< DiscreteConditional > |
gtsam::Testable< DiscreteDistribution > |
gtsam::traits< DiscreteDistribution > |
gtsam::Testable< DiscreteFactor > |
gtsam::traits< DiscreteFactor > |
gtsam::Testable< DiscreteFactorGraph > |
gtsam::traits< DiscreteFactorGraph > |
gtsam::Testable< DiscreteKeys > |
gtsam::traits< DiscreteKeys > |
gtsam::Testable< DiscreteLookupDAG > |
gtsam::traits< DiscreteLookupDAG > |
gtsam::Testable< DiscreteValues > |
gtsam::traits< DiscreteValues > |
gtsam::Testable< EssentialMatrix > |
gtsam::internal::Manifold< EssentialMatrix > |
gtsam::Testable< ExpressionFactor< T > > |
gtsam::traits< ExpressionFactor< T > > | Traits
gtsam::Testable< ExpressionFactorN< T, Args... > > |
gtsam::traits< ExpressionFactorN< T, Args... > > | Traits
gtsam::Testable< FunctorizedFactor2< R, T1, T2 > > |
gtsam::traits< FunctorizedFactor2< R, T1, T2 > > | Traits
gtsam::Testable< FunctorizedFactor< R, T > > |
gtsam::traits< FunctorizedFactor< R, T > > | Traits
gtsam::Testable< GaussianBayesNet > |
gtsam::traits< GaussianBayesNet > | Traits
gtsam::Testable< GaussianBayesTree > |
gtsam::traits< GaussianBayesTree > | Traits
gtsam::Testable< GaussianConditional > |
gtsam::traits< GaussianConditional > | Traits
gtsam::Testable< GaussianFactor > |
gtsam::traits< GaussianFactor > | Traits
gtsam::Testable< GaussianFactorGraph > |
gtsam::traits< GaussianFactorGraph > | Traits
gtsam::Testable< GaussianISAM > |
gtsam::traits< GaussianISAM > | Traits
gtsam::Testable< GaussianMixture > |
gtsam::traits< GaussianMixture > |
gtsam::Testable< GaussianMixtureFactor > |
gtsam::traits< GaussianMixtureFactor > |
gtsam::Testable< GeneralSFMFactor2< CALIBRATION > > |
gtsam::traits< GeneralSFMFactor2< CALIBRATION > > |
gtsam::Testable< GeneralSFMFactor< CAMERA, LANDMARK > > |
gtsam::traits< GeneralSFMFactor< CAMERA, LANDMARK > > |
gtsam::Testable< GenericProjectionFactor< POSE, LANDMARK, CALIBRATION > > |
gtsam::traits< GenericProjectionFactor< POSE, LANDMARK, CALIBRATION > > | Traits
gtsam::Testable< GenericStereoFactor< T1, T2 > > |
gtsam::traits< GenericStereoFactor< T1, T2 > > | Traits
gtsam::Testable< GenericValue< ValueType > > |
gtsam::traits< GenericValue< ValueType > > |
gtsam::Testable< HessianFactor > |
gtsam::traits< HessianFactor > | Traits
gtsam::Testable< HybridBayesNet > |
gtsam::traits< HybridBayesNet > | Traits
gtsam::Testable< HybridBayesTree > |
gtsam::traits< HybridBayesTree > | Traits
gtsam::Testable< HybridConditional > |
gtsam::traits< HybridConditional > |
gtsam::Testable< HybridFactor > |
gtsam::traits< HybridFactor > |
gtsam::Testable< HybridGaussianISAM > |
gtsam::traits< HybridGaussianISAM > | Traits
gtsam::Testable< HybridNonlinearFactorGraph > |
gtsam::traits< HybridNonlinearFactorGraph > |
gtsam::Testable< HybridValues > |
gtsam::traits< HybridValues > |
gtsam::Testable< imuBias::ConstantBias > |
gtsam::internal::VectorSpace< imuBias::ConstantBias > |
gtsam::traits< imuBias::ConstantBias > |
gtsam::Testable< ImuFactor > |
gtsam::traits< ImuFactor > |
gtsam::Testable< ImuFactor2 > |
gtsam::traits< ImuFactor2 > |
gtsam::Testable< ISAM2 > |
gtsam::traits< ISAM2 > | Traits
gtsam::Testable< JacobianFactor > |
gtsam::traits< JacobianFactor > | Traits
gtsam::Testable< JacobianFactorQ< D, ZDim > > |
gtsam::traits< JacobianFactorQ< D, ZDim > > |
gtsam::Testable< LabeledSymbol > |
gtsam::traits< LabeledSymbol > | Traits
gtsam::Testable< Line3 > |
gtsam::internal::Manifold< Line3 > |
gtsam::Testable< LinearContainerFactor > |
gtsam::traits< LinearContainerFactor > |
gtsam::Testable< NavState > |
gtsam::internal::Manifold< NavState > |
gtsam::Testable< noiseModel::Constrained > |
gtsam::traits< noiseModel::Constrained > |
gtsam::Testable< noiseModel::Diagonal > |
gtsam::traits< noiseModel::Diagonal > |
gtsam::Testable< noiseModel::Gaussian > |
gtsam::traits< noiseModel::Gaussian > | Traits
gtsam::Testable< noiseModel::Isotropic > |
gtsam::traits< noiseModel::Isotropic > |
gtsam::Testable< noiseModel::Unit > |
gtsam::traits< noiseModel::Unit > |
gtsam::Testable< NonlinearEquality1< VALUE > > |
gtsam::traits< NonlinearEquality1< VALUE > > |
gtsam::Testable< NonlinearEquality2< VALUE > > |
gtsam::traits< NonlinearEquality2< VALUE > > |
gtsam::Testable< NonlinearEquality< VALUE > > |
gtsam::traits< NonlinearEquality< VALUE > > |
gtsam::Testable< NonlinearFactor > |
gtsam::traits< NonlinearFactor > | Traits
gtsam::Testable< NonlinearFactorGraph > |
gtsam::traits< NonlinearFactorGraph > | Traits
gtsam::Testable< Ordering > |
gtsam::traits< Ordering > | Traits
gtsam::Testable< OrientedPlane3 > |
gtsam::internal::Manifold< OrientedPlane3 > |
gtsam::Testable< ParameterMatrix< M > > |
gtsam::internal::VectorSpace< ParameterMatrix< M > > |
gtsam::traits< ParameterMatrix< M > > |
gtsam::Testable< PinholeCamera< Calibration > > |
gtsam::internal::Manifold< PinholeCamera< Calibration > > |
gtsam::Testable< PinholePose< CALIBRATION > > |
gtsam::internal::Manifold< PinholePose< CALIBRATION > > |
gtsam::Testable< PinholeSet< CAMERA > > |
gtsam::traits< PinholeSet< CAMERA > > |
gtsam::traits< const PinholeSet< CAMERA > > |
gtsam::Testable< Pose2 > |
gtsam::internal::LieGroup< Pose2 > |
gtsam::Testable< Pose3 > |
gtsam::internal::LieGroup< Pose3 > |
gtsam::Testable< Pose3AttitudeFactor > |
gtsam::traits< Pose3AttitudeFactor > | Traits
gtsam::Testable< PreintegratedCombinedMeasurements > |
gtsam::traits< PreintegratedCombinedMeasurements > |
gtsam::Testable< PreintegratedImuMeasurements > |
gtsam::traits< PreintegratedImuMeasurements > |
gtsam::Testable< PreintegratedRotation > |
gtsam::traits< PreintegratedRotation > |
gtsam::Testable< PreintegrationCombinedParams > |
gtsam::traits< PreintegrationCombinedParams > |
gtsam::Testable< PriorFactor< VALUE > > |
gtsam::traits< PriorFactor< VALUE > > | Traits
gtsam::Testable< RangeFactor< A1, A2, T > > |
gtsam::traits< RangeFactor< A1, A2, T > > | Traits
gtsam::Testable< RangeFactorWithTransform< A1, A2, T > > |
gtsam::traits< RangeFactorWithTransform< A1, A2, T > > | Traits
gtsam::Testable< ReferenceFrameFactor< T1, T2 > > |
gtsam::traits< ReferenceFrameFactor< T1, T2 > > | Traits
gtsam::Testable< RegularHessianFactor< D > > |
gtsam::traits< RegularHessianFactor< D > > |
gtsam::Testable< RegularImplicitSchurFactor< CAMERA > > |
gtsam::traits< RegularImplicitSchurFactor< CAMERA > > |
gtsam::Testable< Rot2 > |
gtsam::internal::LieGroup< Rot2 > |
gtsam::Testable< Rot3 > |
gtsam::internal::LieGroup< Rot3 > |
gtsam::Testable< Rot3AttitudeFactor > |
gtsam::traits< Rot3AttitudeFactor > | Traits
gtsam::Testable< SfmData > |
gtsam::traits< SfmData > | Traits
gtsam::Testable< SfmTrack > |
gtsam::traits< SfmTrack > |
gtsam::Testable< Similarity2 > |
gtsam::internal::LieGroup< Similarity2 > |
gtsam::Testable< Similarity3 > |
gtsam::internal::LieGroup< Similarity3 > |
gtsam::Testable< SmartProjectionFactor< CAMERA > > |
gtsam::traits< SmartProjectionFactor< CAMERA > > | Traits
gtsam::Testable< SmartProjectionPoseFactor< CALIBRATION > > |
gtsam::traits< SmartProjectionPoseFactor< CALIBRATION > > | Traits
gtsam::Testable< SmartProjectionRigFactor< CAMERA > > |
gtsam::traits< SmartProjectionRigFactor< CAMERA > > | Traits
gtsam::Testable< SO3 > |
gtsam::internal::LieGroup< SO3 > |
gtsam::Testable< SO4 > |
gtsam::internal::LieGroup< SO4 > |
gtsam::Testable< SO< N > > |
gtsam::internal::LieGroup< SO< N > > |
gtsam::Testable< StereoCamera > |
gtsam::internal::Manifold< StereoCamera > |
gtsam::Testable< StereoPoint2 > |
gtsam::internal::VectorSpace< StereoPoint2 > |
gtsam::traits< StereoPoint2 > |
gtsam::traits< const StereoPoint2 > |
gtsam::Testable< Symbol > |
gtsam::traits< Symbol > | Traits
gtsam::Testable< SymbolicBayesNet > |
gtsam::traits< SymbolicBayesNet > | Traits
gtsam::Testable< SymbolicBayesTree > |
gtsam::traits< SymbolicBayesTree > |
gtsam::Testable< SymbolicBayesTreeClique > |
gtsam::traits< SymbolicBayesTreeClique > | Traits
gtsam::Testable< SymbolicConditional > |
gtsam::traits< SymbolicConditional > | Traits
gtsam::Testable< SymbolicEliminationTree > |
gtsam::traits< SymbolicEliminationTree > | Traits
gtsam::Testable< SymbolicFactor > |
gtsam::traits< SymbolicFactor > | Traits
gtsam::Testable< SymbolicFactorGraph > |
gtsam::traits< SymbolicFactorGraph > | Traits
gtsam::Testable< Unit3 > |
gtsam::internal::Manifold< Unit3 > |
gtsam::Testable< Values > |
gtsam::traits< Values > | Traits
gtsam::Testable< VariableIndex > |
gtsam::traits< VariableIndex > | Traits
gtsam::Testable< VariableSlots > |
gtsam::traits< VariableSlots > | Traits
gtsam::Testable< VectorValues > |
gtsam::traits< VectorValues > | Traits
gtsam::internal::TimingOutline | Timing Entry, arranged in a tree
gtsam::traits< T > | A manifold defines a space in which there is a notion of a linear tangent space that can be centered around a given point on the manifold
gtsam::traits< Errors > | Traits
gtsam::traits< Key > |
gtsam::traits< QUATERNION_TYPE > |
gtsam::TransformCovariance< T > | Functor for transforming covariance of T
gtsam::TranslationRecovery |
gtsam::TriangulationParameters |
std::true_type |
gtsam::needs_eigen_aligned_allocator< T, void_t< typename T::_eigen_aligned_allocator_trait > > |
gtsam::Expression< T >::UnaryFunction< A1 > |
gtsam::Unit3 | Represents a 3D point on a unit sphere
unordered_map< K, T > | STL class
unordered_set< K > | STL class
gtsam::UpdateImpl | Implementation functions for update method All of the methods below have clear inputs and outputs, even if not functional: iSAM2 is inherintly imperative
gtsam::Value | This is the base class for any type to be stored in Values
gtsam::GenericValue< T > | Wraps any type T so it can play as a Value
gtsam::ValueCloneAllocator |
gtsam::Values | A non-templated config holding any types of Manifold-group elements
Values | In nonlinear factors, the error function returns the negative log-likelihood as a non-linear function of the values in a
gtsam::ValuesCastHelper< ValueType, CastedKeyValuePairType, KeyValuePairType > |
gtsam::ValuesCastHelper< const Value, CastedKeyValuePairType, KeyValuePairType > |
gtsam::ValuesCastHelper< Value, CastedKeyValuePairType, KeyValuePairType > |
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::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
gtsam::vector< T > | STL class
gtsam::CameraSet< PinholeCamera< Cal3Bundler > > |
gtsam::CameraSet< PinholeCamera< Cal3_S2 > > |
gtsam::CameraSet< PinholeCamera< Cal3DS2 > > |
gtsam::CameraSet< PinholeCamera< Cal3Fisheye > > |
gtsam::CameraSet< PinholeCamera< Cal3Unified > > |
gtsam::CameraSet< SphericalCamera > |
gtsam::CameraSet< CAMERA > | A set of cameras, all with their own calibration
gtsam::PinholeSet< CAMERA > | PinholeSet: triangulates point and keeps an estimate of it around
gtsam::DiscreteKeys | DiscreteKeys is a set of keys that can be assembled using the & operator
std::vector< T > | STL class
gtsam::CameraSet< PinholeCamera< Cal3Bundler > > |
gtsam::CameraSet< PinholeCamera< Cal3_S2 > > |
gtsam::CameraSet< PinholeCamera< Cal3DS2 > > |
gtsam::CameraSet< PinholeCamera< Cal3Fisheye > > |
gtsam::CameraSet< PinholeCamera< Cal3Unified > > |
gtsam::CameraSet< SphericalCamera > |
gtsam::CameraSet< CAMERA > | A set of cameras, all with their own calibration
gtsam::DiscreteKeys | DiscreteKeys is a set of keys that can be assembled using the & operator
vector< T > | STL class
gtsam::CameraSet< PinholeCamera< Cal3Bundler > > |
gtsam::CameraSet< PinholeCamera< Cal3_S2 > > |
gtsam::CameraSet< PinholeCamera< Cal3DS2 > > |
gtsam::CameraSet< PinholeCamera< Cal3Fisheye > > |
gtsam::CameraSet< PinholeCamera< Cal3Unified > > |
gtsam::CameraSet< SphericalCamera > |
gtsam::CameraSet< CAMERA > | A set of cameras, all with their own calibration
gtsam::DiscreteKeys | DiscreteKeys is a set of keys that can be assembled using the & operator
gtsam::internal::VectorSpaceImpl< Class, N > | VectorSpaceTraits Implementation for Fixed sizes
gtsam::internal::VectorSpaceImpl< Class, Class::dimension > |
gtsam::internal::VectorSpaceTraits< Class > | A helper that implements the traits interface for classes that define vector spaces To use this for your class, define: template<> struct traits<Class> : public VectorSpaceTraits<Class> {}; The class needs to support the requirements defined by HasVectorSpacePrereqs above
gtsam::internal::VectorSpace< Class > | VectorSpace provides both Testable and VectorSpaceTraits
gtsam::internal::VectorSpaceImpl< Class, Eigen::Dynamic > | VectorSpaceTraits implementation for dynamic types
gtsam::internal::VectorSpaceImpl< double, 1 > |
gtsam::internal::ScalarTraits< double > |
gtsam::traits< double > | Double
gtsam::internal::VectorSpaceImpl< Eigen::Matrix< double, M, N, Options, MaxRows, MaxCols >, M *N > |
gtsam::traits< Eigen::Matrix< double, M, N, Options, MaxRows, MaxCols > > |
gtsam::internal::VectorSpaceImpl< float, 1 > |
gtsam::internal::ScalarTraits< float > |
gtsam::traits< float > | Float
gtsam::internal::VectorSpaceImpl< imuBias::ConstantBias, imuBias::ConstantBias::dimension > |
gtsam::internal::VectorSpaceTraits< imuBias::ConstantBias > |
gtsam::internal::VectorSpace< imuBias::ConstantBias > |
gtsam::internal::VectorSpaceImpl< ParameterMatrix< M >, ParameterMatrix< M >::dimension > |
gtsam::internal::VectorSpaceTraits< ParameterMatrix< M > > |
gtsam::internal::VectorSpace< ParameterMatrix< M > > |
gtsam::internal::VectorSpaceImpl< Scalar, 1 > |
gtsam::internal::ScalarTraits< Scalar > | A helper that implements the traits interface for scalar vector spaces
gtsam::internal::VectorSpaceImpl< StereoPoint2, StereoPoint2::dimension > |
gtsam::internal::VectorSpaceTraits< StereoPoint2 > |
gtsam::internal::VectorSpace< StereoPoint2 > |
gtsam::VectorValues | VectorValues represents a collection of vector-valued variables associated each with a unique integer index
VectorValues | The Factor::error simply extracts the
gtsam::VerticalBlockMatrix | This class stores a dense matrix and allows it to be accessed as a collection of vertical blocks
gtsam::Visit< L, Y > | Functor performing depth-first visit to each leaf with the leaf value as the argument
gtsam::VisitLeaf< L, Y > | Functor performing depth-first visit to each leaf with the Leaf object passed as an argument
gtsam::VisitWith< L, Y > | Functor performing depth-first visit to each leaf with the leaf's Assignment<L> and value passed as arguments
gtsam::WeightedSampler< Engine > |