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