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gtsam
3.2.0
gtsam
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gtsam
3.2.0
gtsam
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  Eigen | |
  internal | |
   traits< gtsam::SymmetricBlockMatrixBlockExpr< SymmetricBlockMatrixType > > | |
| gtsam | Global functions in a separate testing namespace |
| internal | |
| linearAlgorithms | |
 OptimizeData | |
| OptimizeClique | Pre-order visitor for back-substitution in a Bayes tree |
| FastDefaultAllocator | Default allocator for list, map, and set types |
| FastDefaultVectorAllocator | Default allocator for vector types (we never use boost pool for vectors) |
| TimingOutline | Timing Entry, arranged in a tree |
| AutoTicToc | No documentation |
| noiseModel | All noise models live in the noiseModel namespace |
| mEstimator | The mEstimator namespace contains all robust error functions (not models) |
| Base | |
| Null | Null class is not robust so is a Gaussian ? |
| Fair | Fair implements the "Fair" robust error model (Zhang97ivc) |
| Huber | Huber implements the "Huber" robust error model (Zhang97ivc) |
| Cauchy | Cauchy implements the "Cauchy" robust error model (Lee2013IROS) |
| Tukey | Tukey implements the "Tukey" robust error model (Zhang97ivc) |
| Welsh | Welsh implements the "Welsh" robust error model (Zhang97ivc) |
| Base | NoiseModel::Base is the abstract base class for all noise models |
| 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 |
| Diagonal | A diagonal noise model implements a diagonal covariance matrix, with the elements of the diagonal specified in a Vector |
| 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 |
| Isotropic | An isotropic noise model corresponds to a scaled diagonal covariance To construct, use one of the static methods |
| Unit | Unit: i.i.d |
| Robust | Base class for robust error models |
| treeTraversal | Internal functions used for traversing trees |
| ConcurrentMap | |
| DerivedValue | |
| DSFBase | |
| DSFVector | |
| FastList | |
| FastMap | |
| FastSetTestableHelper | |
| FastSet | |
| FastSetTestableHelper< VALUE, typename boost::enable_if< has_print< VALUE > >::type > | |
| FastVector | |
| GroupConcept | This concept check enforces a Group structure on a variable type, in which we require the existence of basic algebraic operations |
| LieConcept | Concept check class for Lie group type |
| LieMatrix | LieVector is a wrapper around vector to allow it to be a Lie type |
| LieScalar | LieScalar is a wrapper around double to allow it to be a Lie type |
| LieVector | LieVector is a wrapper around vector to allow it to be a Lie type |
| ManifoldConcept | Concept check class for Manifold types Requires a mapping between a linear tangent space and the underlying manifold, of which Lie is a specialization |
| G_x1 | Helper class that computes the derivative of f w.r.t |
| SymmetricBlockMatrix | |
| CholeskyFailed | |
| SymmetricBlockMatrixBlockExpr | A matrix expression that references a single block of a SymmetricBlockMatrix |
| TestableConcept | |
| equals | Template to create a binary predicate |
| equals_star | Binary predicate on shared pointers |
| const_selector | Helper class that uses templates to select between two types based on whether TEST_TYPE is const or not |
| const_selector< BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST > | Specialization for the non-const version |
| const_selector< const BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST > | Specialization for the const version |
| ValueWithDefault | 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 |
| ListOfOneContainer | A helper class that behaves as a container with one element, and works with boost::range |
| ThreadsafeException | Base exception type that uses tbb_exception if GTSAM is compiled with TBB |
| RuntimeErrorThreadsafe | Threadsafe runtime error exception |
| OutOfRangeThreadsafe | Threadsafe runtime error exception |
| InvalidArgumentThreadsafe | Threadsafe invalid argument exception |
| TbbOpenMPMixedScope | An object whose scope defines a block where TBB and OpenMP parallelism are mixed |
| Value | This is the interface class for any value that may be used as a variable assignment in a factor graph, and which you must derive to create new variable types to use with gtsam |
| VerticalBlockMatrix | |
| Cal3_S2 | |
| Cal3_S2Stereo | |
| Cal3Bundler | |
| Cal3DS2 | |
| Cal3DS2_Base | |
| Cal3Unified | |
| CheiralityException | |
| CalibratedCamera | |
| PoseConcept | Pose Concept A must contain a translation and a rotation, with each structure accessable directly and a type provided for each |
| RangeMeasurementConcept | Range measurement concept Given a pair of Lie variables, there must exist a function to calculate range with derivatives |
| 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 |
| PinholeCamera | |
| Point2 | |
| Point3 | |
| Pose2 | |
| Pose3 | |
| Rot2 | |
| Rot3 | |
| StereoCheiralityException | |
| StereoCamera | |
| StereoPoint2 | |
| TriangulationUnderconstrainedException | Exception thrown by triangulateDLT when SVD returns rank < 3 |
| TriangulationCheiralityException | Exception thrown by triangulateDLT when landmark is behind one or more of the cameras |
| TriangulationFactor | |
| Unit3 | Represents a 3D point on a unit sphere |
| BayesNet | A BayesNet is a tree of conditionals, stored in elimination order |
| FactorGraph | A factor graph is a bipartite graph with factor nodes connected to variable nodes |
| ClusterTree | 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 \( scope(f_i) \sub C_k \) |
| Cluster | |
| BayesTreeCliqueStats | Clique statistics |
| BayesTreeCliqueData | Store all the sizes |
| BayesTree | |
| BayesTreeOrphanWrapper | |
| EliminationTraits | Traits class for eliminateable factor graphs, specifies the types that result from elimination, etc |
| BayesTreeCliqueBase | This is the base class for BayesTree cliques |
| Conditional | TODO: Update comments |
| EliminateableFactorGraph | EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms |
| EliminationTree | An elimination tree is a data structure used intermediately during elimination |
| Node | |
| Factor | This is the base class for all factor types |
| CRefCallPushBack | Helper |
| RefCallPushBack | Helper |
| CRefCallAddCopy | Helper |
| ordering_key_visitor | |
| compose_key_visitor | |
| SDGraph | SDGraph is undirected graph with variable keys and double edge weights |
| SGraph | |
| PredecessorMap | Map from variable key to parent key |
| InconsistentEliminationRequested | An inference algorithm was called with inconsistent arguments |
| ISAM | A Bayes tree with an update methods that implements the iSAM algorithm |
| JunctionTree | |
| LabeledSymbol | Customized version of gtsam::Symbol for multi-robot use |
| Ordering | |
| Symbol | Character and index key used in VectorValues, GaussianFactorGraph, GaussianFactor, etc |
| VariableIndex | Computes and stores the block column structure of a factor graph |
| VariableSlots | A combined factor is assembled as one block of rows for each component factor |
| AlgebraicDecisionTree | Algebraic Decision Trees fix the range to double Just has some nice constructors and some syntactic sugar TODO: consider eliminating this class altogether? |
| Ring | The Real ring with addition and multiplication |
| Assignment | An assignment from labels to value index (size_t) |
| DecisionTree | Decision Tree L = label for variables Y = function range (any algebra), e.g., bool, int, double |
| Choice | |
| Leaf | |
| Node | ---------------------— Node base class ------------------------— |
| DecisionTreeFactor | A discrete probabilistic factor |
| DiscreteBayesNet | A Bayes net made from linear-Discrete densities |
| DiscreteBayesTreeClique | A clique in a DiscreteBayesTree |
| DiscreteBayesTree | A Bayes tree representing a Discrete density |
| DiscreteConditional | Discrete Conditional Density Derives from DecisionTreeFactor |
| DiscreteEliminationTree | |
| DiscreteFactor | Base class for discrete probabilistic factors The most general one is the derived DecisionTreeFactor |
| EliminationTraits< DiscreteFactorGraph > | |
| DiscreteFactorGraph | A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e |
| DiscreteJunctionTree | |
| DiscreteKeys | DiscreteKeys is a set of keys that can be assembled using the & operator |
| DiscreteMarginals | A class for computing marginals of variables in a DiscreteFactorGraph |
| Potentials | A base class for both DiscreteFactor and DiscreteConditional |
| Signature | Signature for a discrete conditional density, used to construct conditionals |
| ConjugateGradientParameters | Parameters for the conjugate gradient method |
| Errors | Vector of errors |
| GaussianBayesNet | A Bayes net made from linear-Gaussian densities |
| GaussianBayesTreeClique | A clique in a GaussianBayesTree |
| GaussianBayesTree | A Bayes tree representing a Gaussian density |
| GaussianConditional | A conditional Gaussian functions as the node in a Bayes network It has a set of parents y,z, etc |
| GaussianDensity | A Gaussian density |
| GaussianEliminationTree | |
| GaussianFactor | An abstract virtual base class for JacobianFactor and HessianFactor |
| EliminationTraits< GaussianFactorGraph > | |
| GaussianFactorGraph | A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e |
| GaussianISAM | |
| GaussianJunctionTree | |
| SlotEntry | One SlotEntry stores the slot index for a variable, as well its dimension |
| Scatter | Scatter is an intermediate data structure used when building a HessianFactor incrementally, to get the keys in the right order |
| HessianFactor | A Gaussian factor using the canonical parameters (information form) |
| CGState | |
| System | Helper class encapsulating the combined system |Ax-b_|^2 Needed to run Conjugate Gradients on matrices |
| IterativeOptimizationParameters | Parameters for iterative linear solvers |
| IterativeSolver | |
| KeyInfoEntry | |
| KeyInfo | |
| JacobianFactor | A Gaussian factor in the squared-error form |
| KalmanFilter | Kalman Filter class |
| IndeterminantLinearSystemException | Thrown when a linear system is ill-posed |
| InvalidNoiseModel | An exception indicating that the noise model dimension passed into a JacobianFactor has a different dimensionality than the factor |
| InvalidMatrixBlock | An exception indicating that a matrix block passed into a JacobianFactor has a different dimensionality than the factor |
| InvalidDenseElimination | |
| PCGSolverParameters | |
| PCGSolver | |
| GaussianFactorGraphSystem | |
| PreconditionerParameters | |
| Preconditioner | |
| DummyPreconditionerParameters | |
| DummyPreconditioner | |
| BlockJacobiPreconditionerParameters | |
| BlockJacobiPreconditioner | |
| Sampler | Sampling structure that keeps internal random number generators for diagonal distributions specified by NoiseModel |
| SubgraphEdge | |
| Subgraph | |
| SubgraphBuilderParameters | |
| SubgraphBuilder | |
| SubgraphPreconditionerParameters | |
| SubgraphPreconditioner | Subgraph conditioner class, as explained in the RSS 2010 submission |
| SubgraphSolverParameters | |
| SubgraphSolver | This class implements the SPCG solver presented in Dellaert et al in IROS'10 |
| VectorValues | This class represents a collection of vector-valued variables associated each with a unique integer index |
| DoglegParams | Parameters for Levenberg-Marquardt optimization |
| DoglegState | State for DoglegOptimizer |
| DoglegOptimizer | This class performs Dogleg nonlinear optimization |
| DoglegOptimizerImpl | This class contains the implementation of the Dogleg algorithm |
| IterationResult | |
| ExtendedKalmanFilter | This is a generic Extended Kalman Filter class implemented using nonlinear factors |
| GaussNewtonParams | Parameters for Gauss-Newton optimization, inherits from NonlinearOptimizationParams |
| GaussNewtonState | |
| GaussNewtonOptimizer | This class performs Gauss-Newton nonlinear optimization |
| ISAM2 | |
| PartialSolveResult | |
| ReorderingMode | |
| ISAM2GaussNewtonParams | |
| ISAM2DoglegParams | |
| ISAM2Params | |
| ISAM2Result | |
| DetailedResults | A struct holding detailed results, which must be enabled with ISAM2Params::enableDetailedResults |
| VariableStatus | The status of a single variable, this struct is stored in DetailedResults::variableStatus |
| ISAM2Clique | Specialized Clique structure for ISAM2, incorporating caching and gradient contribution TODO: more documentation |
| LevenbergMarquardtParams | Parameters for Levenberg-Marquardt optimization |
| LevenbergMarquardtState | State for LevenbergMarquardtOptimizer |
| LevenbergMarquardtOptimizer | This class performs Levenberg-Marquardt nonlinear optimization |
| LinearContainerFactor | Dummy version of a generic linear factor to be injected into a nonlinear factor graph |
| Marginals | A class for computing Gaussian marginals of variables in a NonlinearFactorGraph |
| JointMarginal | A class to store and access a joint marginal, returned from Marginals::jointMarginalCovariance and Marginals::jointMarginalInformation |
| NonlinearConjugateGradientState | An implementation of the nonlinear cg method using the template below |
| NonlinearConjugateGradientOptimizer | |
| NonlinearEquality | An equality factor that forces either one variable to a constant, or a set of variables to be equal to each other |
| NonlinearEquality1 | Simple unary equality constraint - fixes a value for a variable |
| NonlinearEquality2 | Simple binary equality constraint - this constraint forces two factors to be the same |
| MarginalizeNonleafException | Thrown when requesting to marginalize out variables from ISAM2 that are not leaves |
| NonlinearFactor | Nonlinear factor base class |
| 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) |
| NoiseModelFactor1 | A convenient base class for creating your own NoiseModelFactor with 1 variable |
| NoiseModelFactor2 | A convenient base class for creating your own NoiseModelFactor with 2 variables |
| NoiseModelFactor3 | A convenient base class for creating your own NoiseModelFactor with 3 variables |
| NoiseModelFactor4 | A convenient base class for creating your own NoiseModelFactor with 4 variables |
| NoiseModelFactor5 | A convenient base class for creating your own NoiseModelFactor with 5 variables |
| NoiseModelFactor6 | A convenient base class for creating your own NoiseModelFactor with 6 variables |
| GraphvizFormatting | Formatting options when saving in GraphViz format using NonlinearFactorGraph::saveGraph |
| NonlinearFactorGraph | A non-linear factor graph is a graph of non-Gaussian, i.e |
| NonlinearISAM | Wrapper class to manage ISAM in a nonlinear context |
| NonlinearOptimizerState | Base class for a nonlinear optimization state, including the current estimate of the variable values, error, and number of iterations |
| NonlinearOptimizer | This is the abstract interface for classes that can optimize for the maximum-likelihood estimate of a NonlinearFactorGraph |
| NonlinearOptimizerParams | The common parameters for Nonlinear optimizers |
| _ValuesKeyValuePair | |
| _ValuesConstKeyValuePair | |
| ValueCloneAllocator | |
| Values | A non-templated config holding any types of Manifold-group elements |
| ConstFiltered | A filtered view of a const Values, returned from Values::filter |
| ConstKeyValuePair | A key-value pair, which you get by dereferencing iterators |
| Filtered | A filtered view of a Values, returned from Values::filter |
| KeyValuePair | A key-value pair, which you get by dereferencing iterators |
| ValuesKeyAlreadyExists | |
| ValuesKeyDoesNotExist | |
| ValuesIncorrectType | |
| DynamicValuesMismatched | |
| WhiteNoiseFactor | Binary factor to estimate parameters of zero-mean Gaussian white noise |
| AntiFactor | |
| BearingFactor | |
| BearingRangeFactor | |
| BetweenFactor | |
| BetweenConstraint | Binary between constraint - forces between to a given value This constraint requires the underlying type to a Lie type |
| BoundingConstraint1 | |
| BoundingConstraint2 | Binary scalar inequality constraint, with a similar value() function to implement for specific systems |
| SfM_Track | Define the structure for the 3D points |
| SfM_data | Define the structure for SfM data |
| EssentialMatrixConstraint | |
| EssentialMatrixFactor | Factor that evaluates epipolar error p'Ep for given essential matrix |
| 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 |
| 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 |
| GeneralSFMFactor | |
| GeneralSFMFactor2 | Non-linear factor for a constraint derived from a 2D measurement |
| ImplicitSchurFactor | ImplicitSchurFactor |
| JacobianFactorQ | JacobianFactor for Schur complement that uses Q noise model |
| JacobianFactorQR | JacobianFactor for Schur complement that uses Q noise model |
| JacobianFactorSVD | JacobianFactor for Schur complement that uses Q noise model |
| JacobianSchurFactor | JacobianFactor for Schur complement that uses Q noise model |
| PoseRotationPrior | |
| PoseTranslationPrior | A prior on the translation part of a pose |
| PriorFactor | |
| GenericProjectionFactor | |
| RangeFactor | |
| ReferenceFrameFactor | A constraint between two landmarks in separate maps Templated on: Point : Type of landmark Transform : Transform variable class |
| RegularHessianFactor | |
| 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 |
| RotateDirectionsFactor | Factor on unknown rotation R that relates two directions p_i = iRc * z_c Directions provide less constraints than a full rotation |
| SmartFactorBase | Base class with no internal point, completely functional |
| SmartProjectionFactorState | |
| SmartProjectionFactor | SmartProjectionFactor: triangulates point TODO: why LANDMARK parameter? |
| SmartProjectionPoseFactor | |
| GenericStereoFactor |
1.8.6 
