gtsam
3.2.0
gtsam

Eigen  
internal  
traits< gtsam::SymmetricBlockMatrixBlockExpr< SymmetricBlockMatrixType > >  
gtsam  Global functions in a separate testing namespace 
internal  
linearAlgorithms  
OptimizeData  
OptimizeClique  Preorder visitor for backsubstitution 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 nonconst 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 clustertree is associated with a factor graph and is defined as in KollerFriedman: 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 multirobot 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 linearDiscrete 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 linearGaussian 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 Axb_^2 Needed to run Conjugate Gradients on matrices 
IterativeOptimizationParameters  Parameters for iterative linear solvers 
IterativeSolver  
KeyInfoEntry  
KeyInfo  
JacobianFactor  A Gaussian factor in the squarederror form 
KalmanFilter  Kalman Filter class 
IndeterminantLinearSystemException  Thrown when a linear system is illposed 
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 vectorvalued variables associated each with a unique integer index 
DoglegParams  Parameters for LevenbergMarquardt 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 GaussNewton optimization, inherits from NonlinearOptimizationParams 
GaussNewtonState  
GaussNewtonOptimizer  This class performs GaussNewton 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 LevenbergMarquardt optimization 
LevenbergMarquardtState  State for LevenbergMarquardtOptimizer 
LevenbergMarquardtOptimizer  This class performs LevenbergMarquardt 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 sumofsquares factor with a zeromean noise model implementing the density \( P(zx) \propto exp 0.5*zh(x)^2_C \) 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 nonlinear factor graph is a graph of nonGaussian, 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 maximumlikelihood estimate of a NonlinearFactorGraph 
NonlinearOptimizerParams  The common parameters for Nonlinear optimizers 
_ValuesKeyValuePair  
_ValuesConstKeyValuePair  
ValueCloneAllocator  
Values  A nontemplated config holding any types of Manifoldgroup elements 
ConstFiltered  A filtered view of a const Values, returned from Values::filter 
ConstKeyValuePair  A keyvalue pair, which you get by dereferencing iterators 
Filtered  A filtered view of a Values, returned from Values::filter 
KeyValuePair  A keyvalue pair, which you get by dereferencing iterators 
ValuesKeyAlreadyExists  
ValuesKeyDoesNotExist  
ValuesIncorrectType  
DynamicValuesMismatched  
WhiteNoiseFactor  Binary factor to estimate parameters of zeromean 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 reprojection error in image 1 
EssentialMatrixFactor3  Binary factor that optimizes for E and inverse depth d: assumes measurement in image 2 is perfect, and returns reprojection error in image 1 This version takes an extrinsic rotation to allow for omnidirectional rigs 
GeneralSFMFactor  
GeneralSFMFactor2  Nonlinear 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 