gtsam Namespace Reference

Global functions in a separate testing namespace. More...

Namespaces
	imuBias
	All bias models live in the imuBias namespace.
	noiseModel
	All noise models live in the noiseModel namespace.
	treeTraversal
	Internal functions used for traversing trees.

Classes
struct	_ValuesConstKeyValuePair
struct	_ValuesKeyValuePair
class	AcceleratingScenario
	Accelerating from an arbitrary initial state, with optional rotation. More...
class	AdaptAutoDiff
	The AdaptAutoDiff class uses ceres-style autodiff to adapt a ceres-style Function evaluation, i.e., a function FUNCTOR that defines an operator template<typename T> bool operator()(const T* const, const T* const, T* predicted) const; For now only binary operators are supported. More...
struct	additive_group_tag
class	AHRSFactor
class	AlgebraicDecisionTree
	Algebraic Decision Trees fix the range to double Just has some nice constructors and some syntactic sugar TODO: consider eliminating this class altogether? More...
class	AntiFactor
class	Assignment
	An assignment from labels to value index (size_t). More...
class	AttitudeFactor
class	BayesNet
	A BayesNet is a tree of conditionals, stored in elimination order. More...
class	BayesTree
class	BayesTreeCliqueBase
	This is the base class for BayesTree cliques. More...
struct	BayesTreeCliqueData
	store all the sizes More...
struct	BayesTreeCliqueStats
	clique statistics More...
class	BayesTreeOrphanWrapper
struct	Bearing
struct	Bearing< Pose2, T >
struct	Bearing< Pose3, Point3 >
struct	BearingFactor
struct	BearingRange
	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> More...
class	BearingRangeFactor
class	BetweenConstraint
	Binary between constraint - forces between to a given value This constraint requires the underlying type to a Lie type. More...
class	BetweenFactor
struct	BinaryJacobianFactor
	A binary JacobianFactor specialization that uses fixed matrix math for speed. More...
class	BinarySumExpression
	A BinarySumExpression is a specialization of Expression that adds two expressions together It optimizes the Jacobian calculation for this specific case. More...
class	BlockJacobiPreconditioner
struct	BlockJacobiPreconditionerParameters
struct	BoundingConstraint1
struct	BoundingConstraint2
	Binary scalar inequality constraint, with a similar value() function to implement for specific systems. More...
class	Cal3_S2
class	Cal3_S2Stereo
class	Cal3Bundler
class	Cal3DS2
class	Cal3DS2_Base
class	Cal3Unified
class	CalibratedCamera
struct	CameraProjectionMatrix
	Create a 3*4 camera projection matrix from calibration and pose. More...
class	CameraSet
	A set of cameras, all with their own calibration. More...
struct	CGState
class	CheiralityException
class	CholeskyFailed
	Indicate Cholesky factorization failure. More...
class	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 \( scope(f_i) \sub C_k \). More...
class	CombinedImuFactor
class	compose_key_visitor
class	ConcurrentMap
class	Conditional
	TODO: Update comments. More...
class	ConjugateGradientParameters
	parameters for the conjugate gradient method More...
struct	const_selector
	Helper class that uses templates to select between two types based on whether TEST_TYPE is const or not. More...
struct	const_selector< BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST >
	Specialization for the non-const version. More...
struct	const_selector< const BASIC_TYPE, BASIC_TYPE, AS_NON_CONST, AS_CONST >
	Specialization for the const version. More...
class	ConstantTwistScenario
	Scenario with constant twist 3D trajectory. More...
struct	ConstructorTraversalData
class	CRefCallAddCopy
	Helper. More...
class	CRefCallPushBack
	Helper. More...
class	Cyclic
	Cyclic group of order N. More...
class	DecisionTree
	Decision Tree L = label for variables Y = function range (any algebra), e.g., bool, int, double. More...
class	DecisionTreeFactor
	A discrete probabilistic factor. More...
class	DerivedValue
class	DirectProduct
class	DirectSum
	Template to construct the direct sum of two additive groups Assumes existence of three additive operators for both groups. More...
class	DiscreteBayesNet
	A Bayes net made from linear-Discrete densities. More...
class	DiscreteBayesTree
	A Bayes tree representing a Discrete density. More...
class	DiscreteBayesTreeClique
	A clique in a DiscreteBayesTree. More...
class	DiscreteConditional
	Discrete Conditional Density Derives from DecisionTreeFactor. More...
class	DiscreteEliminationTree
class	DiscreteFactor
	Base class for discrete probabilistic factors The most general one is the derived DecisionTreeFactor. More...
class	DiscreteFactorGraph
	A Discrete Factor Graph is a factor graph where all factors are Discrete, i.e. More...
class	DiscreteJunctionTree
struct	DiscreteKeys
	DiscreteKeys is a set of keys that can be assembled using the & operator. More...
class	DiscreteMarginals
	A class for computing marginals of variables in a DiscreteFactorGraph. More...
class	DoglegOptimizer
	This class performs Dogleg nonlinear optimization. More...
struct	DoglegOptimizerImpl
	This class contains the implementation of the Dogleg algorithm. More...
class	DoglegParams
	Parameters for Levenberg-Marquardt optimization. More...
class	DSFBase
class	DSFVector
class	DummyPreconditioner
struct	DummyPreconditionerParameters
class	DynamicValuesMismatched
class	EliminatableClusterTree
	A cluster-tree that eliminates to a Bayes tree. More...
class	EliminateableFactorGraph
	EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms. More...
struct	EliminationData
struct	EliminationTraits
	Traits class for eliminateable factor graphs, specifies the types that result from elimination, etc. More...
struct	EliminationTraits< DiscreteFactorGraph >
struct	EliminationTraits< GaussianFactorGraph >
struct	EliminationTraits< SymbolicFactorGraph >
class	EliminationTree
	An elimination tree is a data structure used intermediately during elimination. More...
struct	equals
	Template to create a binary predicate. More...
struct	equals_star
	Binary predicate on shared pointers. More...
class	Errors
	vector of errors More...
class	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. More...
class	EssentialMatrixConstraint
class	EssentialMatrixFactor
	Factor that evaluates epipolar error p'Ep for given essential matrix. More...
class	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. More...
class	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. More...
class	Expression
	Expression class that supports automatic differentiation. More...
class	ExpressionFactor
	Factor that supports arbitrary expressions via AD. More...
class	ExpressionFactor2
	Binary specialization of ExpressionFactor meant as a base class for binary factors Enforces expression method with two keys, and provides evaluateError Derived needs to call initialize. More...
class	ExpressionFactorGraph
	Factor graph that supports adding ExpressionFactors directly. More...
class	ExtendedKalmanFilter
	This is a generic Extended Kalman Filter class implemented using nonlinear factors. More...
class	Factor
	This is the base class for all factor types. More...
class	FactorGraph
	A factor graph is a bipartite graph with factor nodes connected to variable nodes. More...
class	FastList
class	FastMap
class	FastSet
class	FastVector
struct	FixedDimension
	Give fixed size dimension of a type, fails at compile time if dynamic. More...
class	G_x1
	Helper class that computes the derivative of f w.r.t. More...
class	GaussianBayesNet
	A Bayes net made from linear-Gaussian densities. More...
class	GaussianBayesTree
	A Bayes tree representing a Gaussian density. More...
class	GaussianBayesTreeClique
	A clique in a GaussianBayesTree. More...
class	GaussianConditional
	A conditional Gaussian functions as the node in a Bayes network It has a set of parents y,z, etc. More...
class	GaussianDensity
	A Gaussian density. More...
class	GaussianEliminationTree
class	GaussianFactor
	An abstract virtual base class for JacobianFactor and HessianFactor. More...
class	GaussianFactorGraph
	A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e. More...
class	GaussianFactorGraphSystem
	System class needed for calling preconditionedConjugateGradient. More...
class	GaussianISAM
class	GaussianJunctionTree
class	GaussNewtonOptimizer
	This class performs Gauss-Newton nonlinear optimization. More...
class	GaussNewtonParams
	Parameters for Gauss-Newton optimization, inherits from NonlinearOptimizationParams. More...
class	GeneralSFMFactor
class	GeneralSFMFactor2
	Non-linear factor for a constraint derived from a 2D measurement. More...
class	GenericProjectionFactor
class	GenericStereoFactor
class	GenericValue
	Wraps any type T so it can play as a Value. More...
class	GPSFactor
class	GPSFactor2
struct	GraphvizFormatting
	Formatting options when saving in GraphViz format using NonlinearFactorGraph::saveGraph. More...
struct	group_tag
	tag to assert a type is a group More...
struct	HasBearing
struct	HasRange
struct	HasTestablePrereqs
	Requirements on type to pass it to Testable template below. More...
class	HessianFactor
	A Gaussian factor using the canonical parameters (information form) More...
class	ImuFactor
class	ImuFactor2
class	InconsistentEliminationRequested
	An inference algorithm was called with inconsistent arguments. More...
class	IndeterminantLinearSystemException
	Thrown when a linear system is ill-posed. More...
class	InvalidArgumentThreadsafe
	Thread-safe invalid argument exception. More...
class	InvalidDenseElimination
class	InvalidMatrixBlock
	An exception indicating that a matrix block passed into a JacobianFactor has a different dimensionality than the factor. More...
class	InvalidNoiseModel
	An exception indicating that the noise model dimension passed into a JacobianFactor has a different dimensionality than the factor. More...
class	ISAM
	A Bayes tree with an update methods that implements the iSAM algorithm. More...
class	ISAM2
class	ISAM2Clique
	Specialized Clique structure for ISAM2, incorporating caching and gradient contribution TODO: more documentation. More...
struct	ISAM2DoglegParams
struct	ISAM2GaussNewtonParams
struct	ISAM2Params
struct	ISAM2Result
class	IsGroup
	Group Concept. More...
class	IsLieGroup
	Lie Group Concept. More...
class	IsTestable
class	IsVectorSpace
	Vector Space concept. More...
class	IterativeOptimizationParameters
	parameters for iterative linear solvers More...
class	IterativeSolver
	Base class for Iterative Solvers like SubgraphSolver. More...
class	JacobianFactor
	A Gaussian factor in the squared-error form. More...
class	JacobianFactorQ
	JacobianFactor for Schur complement that uses Q noise model. More...
class	JacobianFactorQR
	JacobianFactor for Schur complement that uses Q noise model. More...
class	JacobianFactorSVD
	JacobianFactor for Schur complement that uses the "Nullspace Trick" by Mourikis. More...
class	JointMarginal
	A class to store and access a joint marginal, returned from Marginals::jointMarginalCovariance and Marginals::jointMarginalInformation. More...
class	JunctionTree
class	KalmanFilter
	Kalman Filter class. More...
class	KeyInfo
	Handy data structure for iterative solvers. More...
class	KeyInfoEntry
	Handy data structure for iterative solvers key to (index, dimension, colstart) More...
class	LabeledSymbol
	Customized version of gtsam::Symbol for multi-robot use. More...
class	LevenbergMarquardtOptimizer
	This class performs Levenberg-Marquardt nonlinear optimization. More...
class	LevenbergMarquardtParams
	Parameters for Levenberg-Marquardt optimization. More...
struct	lie_group_tag
	tag to assert a type is a Lie group More...
struct	LieGroup
	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. More...
class	LinearContainerFactor
	Dummy version of a generic linear factor to be injected into a nonlinear factor graph. More...
class	ListOfOneContainer
	A helper class that behaves as a container with one element, and works with boost::range. More...
class	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. More...
class	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. More...
class	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. More...
class	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. More...
struct	MakeJacobian
	: meta-function to generate Jacobian More...
struct	MakeOptionalJacobian
	: meta-function to generate JacobianTA optional reference Used mainly by Expressions More...
struct	manifold_tag
	tag to assert a type is a manifold More...
class	ManifoldPreintegration
	IMU pre-integration on NavSatet manifold. More...
class	MarginalizeNonleafException
	Thrown when requesting to marginalize out variables from ISAM2 that are not leaves. More...
class	Marginals
	A class for computing Gaussian marginals of variables in a NonlinearFactorGraph. More...
class	MetisIndex
	The MetisIndex class converts a factor graph into the Compressed Sparse Row format for use in METIS algorithms. More...
struct	multiplicative_group_tag
	Group operator syntax flavors. More...
struct	MultiplyWithInverse
	Functor that implements multiplication of a vector b with the inverse of a matrix A. More...
struct	MultiplyWithInverseFunction
	Functor that implements multiplication with the inverse of a matrix, itself the result of a function f. More...
class	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. More...
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). More...
class	NoiseModelFactor1
	A convenient base class for creating your own NoiseModelFactor with 1 variable. More...
class	NoiseModelFactor2
	A convenient base class for creating your own NoiseModelFactor with 2 variables. More...
class	NoiseModelFactor3
	A convenient base class for creating your own NoiseModelFactor with 3 variables. More...
class	NoiseModelFactor4
	A convenient base class for creating your own NoiseModelFactor with 4 variables. More...
class	NoiseModelFactor5
	A convenient base class for creating your own NoiseModelFactor with 5 variables. More...
class	NoiseModelFactor6
	A convenient base class for creating your own NoiseModelFactor with 6 variables. More...
class	NoMatchFoundForFixed
class	NonlinearConjugateGradientOptimizer
	An implementation of the nonlinear CG method using the template below. More...
class	NonlinearEquality
	An equality factor that forces either one variable to a constant, or a set of variables to be equal to each other. More...
class	NonlinearEquality1
	Simple unary equality constraint - fixes a value for a variable. More...
class	NonlinearEquality2
	Simple binary equality constraint - this constraint forces two factors to be the same. More...
class	NonlinearFactor
	Nonlinear factor base class. More...
class	NonlinearFactorGraph
	A non-linear factor graph is a graph of non-Gaussian, i.e. More...
class	NonlinearISAM
	Wrapper class to manage ISAM in a nonlinear context. More...
class	NonlinearOptimizer
	This is the abstract interface for classes that can optimize for the maximum-likelihood estimate of a NonlinearFactorGraph. More...
class	NonlinearOptimizerParams
	The common parameters for Nonlinear optimizers. More...
class	OptionalJacobian
	OptionalJacobian is an Eigen::Ref like class that can take be constructed using either a fixed size or dynamic Eigen matrix. More...
class	OptionalJacobian< Eigen::Dynamic, Eigen::Dynamic >
class	Ordering
class	ordering_key_visitor
class	OrientedPlane3
	Represents an infinite plane in 3D, which is composed of a planar normal and its perpendicular distance to the origin. More...
class	OrientedPlane3DirectionPrior
class	OrientedPlane3Factor
	Factor to measure a planar landmark from a given pose. More...
class	OutOfRangeThreadsafe
	Thread-safe out of range exception. More...
class	PCGSolver
	A virtual base class for the preconditioned conjugate gradient solver. More...
struct	PCGSolverParameters
	Parameters for PCG. More...
class	PinholeBase
class	PinholeBaseK
class	PinholeCamera
class	PinholePose
class	PinholeSet
	PinholeSet: triangulates point and keeps an estimate of it around. More...
class	Point2
class	Point3
class	Pose2
class	Pose3
class	Pose3AttitudeFactor
class	PoseConcept
	Pose Concept A must contain a translation and a rotation, with each structure accessable directly and a type provided for each. More...
class	PoseRotationPrior
class	PoseTranslationPrior
	A prior on the translation part of a pose. More...
class	Potentials
	A base class for both DiscreteFactor and DiscreteConditional. More...
class	Preconditioner
struct	PreconditionerParameters
class	PredecessorMap
	Map from variable key to parent key. More...
class	PreintegratedAhrsMeasurements
	PreintegratedAHRSMeasurements accumulates (integrates) the Gyroscope measurements (rotation rates) and the corresponding covariance matrix. More...
class	PreintegratedCombinedMeasurements
class	PreintegratedImuMeasurements
class	PreintegratedRotation
	PreintegratedRotation is the base class for all PreintegratedMeasurements classes (in AHRSFactor, ImuFactor, and CombinedImuFactor). More...
struct	PreintegratedRotationParams
	Parameters for pre-integration: Usage: Create just a single Params and pass a shared pointer to the constructor. More...
class	PreintegrationBase
	PreintegrationBase is the base class for PreintegratedMeasurements (in ImuFactor) and CombinedPreintegratedMeasurements (in CombinedImuFactor). More...
struct	PreintegrationParams
	Parameters for pre-integration: Usage: Create just a single Params and pass a shared pointer to the constructor. More...
class	PriorFactor
class	ProductLieGroup
	Template to construct the product Lie group of two other Lie groups Assumes Lie group structure for G and H. More...
class	ProductManifold
	Helper class to construct the product manifold of two other manifolds, M1 and M2 Assumes nothing except manifold structure for M1 and M2, and the existence of default constructor for those types. More...
struct	Range
struct	Range< CalibratedCamera, T >
struct	Range< PinholeCamera< Calibration >, T >
struct	Range< Point3, Point3 >
struct	Range< Pose2, T >
struct	Range< Pose3, T >
struct	Range< SimpleCamera, T >
class	RangeFactor
class	RangeFactorWithTransform
class	RefCallPushBack
	Helper. More...
class	ReferenceFrameFactor
	A constraint between two landmarks in separate maps Templated on: Point : Type of landmark Transform : Transform variable class. More...
class	RegularHessianFactor
class	RegularImplicitSchurFactor
	RegularImplicitSchurFactor. More...
class	RegularJacobianFactor
	JacobianFactor with constant sized blocks Provides raw memory access versions of linear operator. More...
struct	Reshape
	Reshape functor. More...
struct	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) More...
struct	Reshape< M, N, InOptions, M, N, InOptions >
	Reshape specialization that does nothing as shape stays the same. More...
struct	Reshape< N, M, InOptions, M, N, InOptions >
	Reshape specialization that does transpose. More...
class	Rot2
class	Rot3
class	Rot3AttitudeFactor
class	RotateDirectionsFactor
	Factor on unknown rotation iRc that relates two directions c Directions provide less constraints than a full rotation. More...
class	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. More...
class	RuntimeErrorThreadsafe
	Thread-safe runtime error exception. More...
class	Sampler
	Sampling structure that keeps internal random number generators for diagonal distributions specified by NoiseModel. More...
class	ScalarMultiplyExpression
	A ScalarMultiplyExpression is a specialization of Expression that multiplies with a scalar It optimizes the Jacobian calculation for this specific case. More...
class	Scatter
	Scatter is an intermediate data structure used when building a HessianFactor incrementally, to get the keys in the right order. More...
class	Scenario
	Simple trajectory simulator. More...
class	ScenarioRunner
class	SDGraph
	SDGraph is undirected graph with variable keys and double edge weights. More...
struct	SfM_data
	Define the structure for SfM data. More...
struct	SfM_Track
	Define the structure for the 3D points. More...
class	SGraph
class	Signature
	Signature for a discrete conditional density, used to construct conditionals. More...
class	SimpleCamera
struct	SlotEntry
	One SlotEntry stores the slot index for a variable, as well its dim. More...
class	SmartFactorBase
	Base class for smart factors This base class has no internal point, but it has a measurement, noise model and an optional sensor pose. More...
class	SmartProjectionFactor
	SmartProjectionFactor: triangulates point and keeps an estimate of it around. More...
struct	SmartProjectionParams
class	SmartProjectionPoseFactor
class	SO3
	True SO(3), i.e., 3*3 matrix subgroup We guarantee (all but first) constructors only generate from sub-manifold. More...
class	StereoCamera
class	StereoCheiralityException
class	StereoPoint2
class	Subgraph
class	SubgraphBuilder
struct	SubgraphBuilderParameters
struct	SubgraphEdge
class	SubgraphPreconditioner
	Subgraph conditioner class, as explained in the RSS 2010 submission. More...
struct	SubgraphPreconditionerParameters
class	SubgraphSolver
	This class implements the SPCG solver presented in Dellaert et al in IROS'10. More...
class	SubgraphSolverParameters
class	Symbol
	Character and index key used in VectorValues, GaussianFactorGraph, GaussianFactor, etc. More...
class	SymbolicBayesNet
	Symbolic Bayes Net. More...
class	SymbolicBayesTree
	A Bayes tree that represents the connectivity between variables but is not associated with any probability functions. More...
class	SymbolicBayesTreeClique
	A clique in a SymbolicBayesTree. More...
class	SymbolicConditional
	SymbolicConditional is a conditional with keys but no probability data, produced by symbolic elimination of SymbolicFactor. More...
class	SymbolicEliminationTree
class	SymbolicFactor
	SymbolicFactor represents a symbolic factor that specifies graph topology but is not associated with any numerical function. More...
class	SymbolicFactorGraph
	Symbolic Factor Graph. More...
class	SymbolicISAM
class	SymbolicJunctionTree
class	SymmetricBlockMatrix
class	System
	Helper class encapsulating the combined system |Ax-b_|^2 Needed to run Conjugate Gradients on matrices. More...
class	TangentPreintegration
	Integrate on the 9D tangent space of the NavState manifold. More...
class	TbbOpenMPMixedScope
	An object whose scope defines a block where TBB and OpenMP parallelism are mixed. More...
struct	Testable
	A helper that implements the traits interface for GTSAM types. More...
class	ThreadsafeException
	Base exception type that uses tbb_exception if GTSAM is compiled with TBB. More...
struct	traits
	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. More...
struct	traits< BearingFactor< A1, A2, T > >
	traits More...
struct	traits< BearingRange< A1, A2 > >
struct	traits< BearingRangeFactor< A1, A2, B, R > >
	traits More...
struct	traits< BetweenConstraint< VALUE > >
	traits More...
struct	traits< BetweenFactor< VALUE > >
	traits More...
struct	traits< BinaryJacobianFactor< M, N1, N2 > >
struct	traits< Cal3_S2 >
struct	traits< Cal3_S2Stereo >
struct	traits< Cal3Bundler >
struct	traits< Cal3DS2 >
struct	traits< Cal3Unified >
struct	traits< CalibratedCamera >
struct	traits< CameraSet< CAMERA > >
struct	traits< const Cal3_S2 >
struct	traits< const Cal3_S2Stereo >
struct	traits< const Cal3Bundler >
struct	traits< const Cal3DS2 >
struct	traits< const Cal3Unified >
struct	traits< const CalibratedCamera >
struct	traits< const CameraSet< CAMERA > >
struct	traits< const EssentialMatrix >
struct	traits< const OrientedPlane3 >
struct	traits< const PinholeCamera< Calibration > >
struct	traits< const PinholePose< CALIBRATION > >
struct	traits< const PinholeSet< CAMERA > >
struct	traits< const Point3 >
struct	traits< const Pose2 >
struct	traits< const Pose3 >
struct	traits< const Rot2 >
struct	traits< const Rot3 >
struct	traits< const SimpleCamera >
struct	traits< const SO3 >
struct	traits< const StereoCamera >
struct	traits< const StereoPoint2 >
struct	traits< const Unit3 >
struct	traits< Cyclic< N > >
	Define cyclic group to be a model of the Additive Group concept. More...
struct	traits< DecisionTreeFactor >
struct	traits< DirectProduct< G, H > >
struct	traits< DirectSum< G, H > >
struct	traits< DiscreteBayesNet >
struct	traits< DiscreteConditional >
struct	traits< DiscreteFactor >
struct	traits< DiscreteFactor::Values >
struct	traits< DiscreteFactorGraph >
	traits More...
struct	traits< double >
	double More...
struct	traits< Eigen::Matrix< double, 1,-1, Options, MaxRows, MaxCols > >
struct	traits< Eigen::Matrix< double, M, N, Options, MaxRows, MaxCols > >
struct	traits< Eigen::Matrix< double,-1, 1, Options, MaxRows, MaxCols > >
struct	traits< Eigen::Matrix< double,-1,-1, Options, MaxRows, MaxCols > >
struct	traits< Errors >
	traits More...
struct	traits< EssentialMatrix >
struct	traits< ExpressionFactor< T > >
	traits More...
struct	traits< float >
	float More...
struct	traits< GaussianBayesNet >
	traits More...
struct	traits< GaussianBayesTree >
	traits More...
struct	traits< GaussianConditional >
	traits More...
struct	traits< GaussianFactor >
	traits More...
struct	traits< GaussianFactorGraph >
	traits More...
struct	traits< GeneralSFMFactor2< CALIBRATION > >
struct	traits< GeneralSFMFactor< CAMERA, LANDMARK > >
struct	traits< GenericProjectionFactor< POSE, LANDMARK, CALIBRATION > >
	traits More...
struct	traits< GenericStereoFactor< T1, T2 > >
	traits More...
struct	traits< GenericValue< ValueType > >
struct	traits< HessianFactor >
	traits More...
struct	traits< imuBias::ConstantBias >
struct	traits< ImuFactor >
struct	traits< ImuFactor2 >
struct	traits< ISAM2 >
	traits More...
struct	traits< JacobianFactor >
	traits More...
struct	traits< JacobianFactorQ< D, ZDim > >
struct	traits< Key >
struct	traits< LabeledSymbol >
	traits More...
struct	traits< LinearContainerFactor >
struct	traits< NavState >
struct	traits< noiseModel::Constrained >
struct	traits< noiseModel::Diagonal >
struct	traits< noiseModel::Gaussian >
	traits More...
struct	traits< noiseModel::Isotropic >
struct	traits< noiseModel::Unit >
struct	traits< NonlinearEquality1< VALUE > >
struct	traits< NonlinearEquality2< VALUE > >
struct	traits< NonlinearEquality< VALUE > >
struct	traits< NonlinearFactor >
	traits More...
struct	traits< NonlinearFactorGraph >
	traits More...
struct	traits< Ordering >
	traits More...
struct	traits< OrientedPlane3 >
struct	traits< PinholeCamera< Calibration > >
struct	traits< PinholePose< CALIBRATION > >
struct	traits< PinholeSet< CAMERA > >
struct	traits< Point2 >
struct	traits< Point3 >
struct	traits< Pose2 >
struct	traits< Pose3 >
struct	traits< Pose3AttitudeFactor >
	traits More...
struct	traits< Potentials >
struct	traits< Potentials::ADT >
struct	traits< PreintegratedImuMeasurements >
struct	traits< PreintegratedRotation >
struct	traits< ProductLieGroup< G, H > >
struct	traits< ProductManifold< M1, M2 > >
struct	traits< QUATERNION_TYPE >
struct	traits< RangeFactor< A1, A2, T > >
	traits More...
struct	traits< RangeFactorWithTransform< A1, A2, T > >
	traits More...
struct	traits< ReferenceFrameFactor< T1, T2 > >
	traits More...
struct	traits< RegularHessianFactor< D > >
struct	traits< RegularImplicitSchurFactor< CAMERA > >
struct	traits< Rot2 >
struct	traits< Rot3 >
struct	traits< Rot3AttitudeFactor >
	traits More...
struct	traits< SimpleCamera >
struct	traits< SmartProjectionFactor< CAMERA > >
	traits More...
struct	traits< SmartProjectionPoseFactor< CALIBRATION > >
	traits More...
struct	traits< SO3 >
struct	traits< StereoCamera >
struct	traits< StereoPoint2 >
struct	traits< Symbol >
	traits More...
struct	traits< SymbolicBayesNet >
	traits More...
struct	traits< SymbolicBayesTree >
struct	traits< SymbolicBayesTreeClique >
	traits More...
struct	traits< SymbolicConditional >
	traits More...
struct	traits< SymbolicEliminationTree >
	traits More...
struct	traits< SymbolicFactor >
	traits More...
struct	traits< SymbolicFactorGraph >
	traits More...
struct	traits< Unit3 >
struct	traits< Values >
	traits More...
struct	traits< VariableIndex >
	traits More...
struct	traits< VariableSlots >
	traits More...
struct	traits< VectorValues >
	traits More...
class	TriangulationCheiralityException
	Exception thrown by triangulateDLT when landmark is behind one or more of the cameras. More...
class	TriangulationFactor
struct	TriangulationParameters
class	TriangulationResult
	TriangulationResult is an optional point, along with the reasons why it is invalid. More...
class	TriangulationUnderconstrainedException
	Exception thrown by triangulateDLT when SVD returns rank < 3. More...
class	Unit3
	Represents a 3D point on a unit sphere. More...
class	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. More...
class	ValueCloneAllocator
class	Values
	A non-templated config holding any types of Manifold-group elements. More...
struct	ValuesCastHelper
struct	ValuesCastHelper< const Value, CastedKeyValuePairType, KeyValuePairType >
struct	ValuesCastHelper< Value, CastedKeyValuePairType, KeyValuePairType >
class	ValuesIncorrectType
class	ValuesKeyAlreadyExists
class	ValuesKeyDoesNotExist
struct	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. More...
class	VariableIndex
	The VariableIndex class computes and stores the block column structure of a factor graph. More...
class	VariableSlots
	A combined factor is assembled as one block of rows for each component factor. More...
struct	vector_space_tag
	tag to assert a type is a vector space More...
class	VectorValues
	This class represents a collection of vector-valued variables associated each with a unique integer index. More...
class	VerticalBlockMatrix
class	WhiteNoiseFactor
	Binary factor to estimate parameters of zero-mean Gaussian white noise. More...

Typedefs
typedef Eigen::MatrixXd	Matrix
typedef Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor >	MatrixRowMajor
typedef Eigen::Block< Matrix >	SubMatrix
typedef Eigen::Block< const Matrix >	ConstSubMatrix
typedef std::uint64_t	Key
	Integer nonlinear key type.
typedef ptrdiff_t	DenseIndex
	The index type for Eigen objects.
typedef Eigen::VectorXd	Vector
typedef Eigen::Matrix< double, 1, 1 >	Vector1
typedef Eigen::Vector2d	Vector2
typedef Eigen::Vector3d	Vector3
typedef Eigen::VectorBlock< Vector >	SubVector
typedef Eigen::VectorBlock< const Vector >	ConstSubVector
typedef std::pair< Key, size_t >	DiscreteKey
	Key type for discrete conditionals Includes name and cardinality.
typedef std::pair< Point2, Point2 >	Point2Pair
	Calculate pose between a vector of 2D point correspondences (p,q) where q = Pose2::transform_from(p) = t + R*p.
typedef std::vector< Point2 >	Point2Vector
typedef std::pair< Point3, Point3 >	Point3Pair
typedef std::vector< Pose3 >	Pose3Vector
typedef Eigen::Quaternion< double, Eigen::DontAlign >	Quaternion
typedef gtsam::PinholeCamera< gtsam::Cal3_S2 >	PinholeCameraCal3_S2
	A simple camera class with a Cal3_S2 calibration.
typedef boost::function< std::string(Key)>	KeyFormatter
	Typedef for a function to format a key, i.e. to convert it to a string.
typedef FastVector< Key >	KeyVector
	Useful typedef for operations with Values - allows for matlab interface.
typedef FastList< Key >	KeyList
typedef FastSet< Key >	KeySet
typedef FastMap< Key, int >	KeyGroupMap
typedef noiseModel::Base::shared_ptr	SharedNoiseModel
	Note, deliberately not in noiseModel namespace. More...
typedef noiseModel::Gaussian::shared_ptr	SharedGaussian
typedef noiseModel::Diagonal::shared_ptr	SharedDiagonal
typedef noiseModel::Constrained::shared_ptr	SharedConstrained
typedef noiseModel::Isotropic::shared_ptr	SharedIsotropic
typedef ManifoldPreintegration	PreintegrationType
typedef Vector3	Velocity3
	Velocity is currently typedef'd to Vector3.
typedef internal::DoglegState	State
typedef Expression< double >	Double_
typedef Expression< Vector1 >	Vector1_
typedef Expression< Vector2 >	Vector2_
typedef Expression< Vector3 >	Vector3_
typedef Expression< Vector4 >	Vector4_
typedef Expression< Vector5 >	Vector5_
typedef Expression< Vector6 >	Vector6_
typedef Expression< Vector7 >	Vector7_
typedef Expression< Vector8 >	Vector8_
typedef Expression< Vector9 >	Vector9_
typedef FastMap< char, Vector >	ISAM2ThresholdMap
typedef ISAM2ThresholdMap::value_type	ISAM2ThresholdMapValue
typedef FastVector< size_t >	FactorIndices
typedef NonlinearOptimizerParams	SuccessiveLinearizationParams
typedef std::pair< NonlinearFactorGraph::shared_ptr, Values::shared_ptr >	GraphAndValues
	Return type for load functions.
typedef std::pair< size_t, Point2 >	SfM_Measurement
	A measurement with its camera index.
typedef std::pair< size_t, size_t >	SIFT_Index
	SfM_Track.
typedef PinholeCamera< Cal3Bundler >	SfM_Camera
	Define the structure for the camera poses.
typedef Expression< Point2 >	Point2_
typedef Expression< Rot2 >	Rot2_
typedef Expression< Pose2 >	Pose2_
typedef Expression< Point3 >	Point3_
typedef Expression< Unit3 >	Unit3_
typedef Expression< Rot3 >	Rot3_
typedef Expression< Pose3 >	Pose3_
typedef Expression< Cal3_S2 >	Cal3_S2_
typedef Expression< Cal3Bundler >	Cal3Bundler_
typedef std::map< Key, std::vector< size_t > >	KeyVectorMap
typedef std::map< Key, Rot3 >	KeyRotMap

Enumerations
enum	NoiseFormat {   NoiseFormatG2O, NoiseFormatTORO, NoiseFormatGRAPH, NoiseFormatCOV,   NoiseFormatAUTO }
	Indicates how noise parameters are stored in file. More...
enum	KernelFunctionType { KernelFunctionTypeNONE, KernelFunctionTypeHUBER, KernelFunctionTypeTUKEY }
	Robust kernel type to wrap around quadratic noise model.
enum	LinearizationMode { HESSIAN, IMPLICIT_SCHUR, JACOBIAN_Q, JACOBIAN_SVD }
	Linearization mode: what factor to linearize to.
enum	DegeneracyMode { IGNORE_DEGENERACY, ZERO_ON_DEGENERACY, HANDLE_INFINITY }
	How to manage degeneracy.

Functions
template<typename T >
void	testDefaultChart (TestResult &result_, const std::string &name_, const T &value)
pair< size_t, bool >	choleskyCareful (Matrix &ATA, int order=-1)
	"Careful" Cholesky computes the positive square-root of a positive symmetric semi-definite matrix (i.e. More...
bool	choleskyPartial (Matrix &ABC, size_t nFrontal, size_t topleft=0)
	Partial Cholesky computes a factor [R S such that [R' 0 [R S = [A B 0 L] S' I] 0 L] B' C]. More...
bool	guardedIsDebug (const std::string &s)
void	guardedSetDebug (const std::string &s, const bool v)
template<typename G >
	BOOST_CONCEPT_REQUIRES (((IsGroup< G >)),(bool)) check_group_invariants(const G &a
	Check invariants.
template<class Class >
Class	between_default (const Class &l1, const Class &l2)
	These core global functions can be specialized by new Lie types for better performance. More...
template<class Class >
Vector	logmap_default (const Class &l0, const Class &lp)
	Log map centered at l0, s.t. More...
template<class Class >
Class	expmap_default (const Class &t, const Vector &d)
	Exponential map centered at l0, s.t. More...
template<class T >
T	BCH (const T &X, const T &Y)
	Three term approximation of the Baker-Campbell-Hausdorff formula In non-commutative Lie groups, when composing exp(Z) = exp(X)exp(Y) it is not true that Z = X+Y. More...
template<class T >
Matrix	wedge (const Vector &x)
	Declaration of wedge (see Murray94book) used to convert from n exponential coordinates to n*n element of the Lie algebra.
template<class T >
T	expm (const Vector &x, int K=7)
	Exponential map given exponential coordinates class T needs a wedge<> function and a constructor from Matrix. More...
template<typename T >
T	interpolate (const T &X, const T &Y, double t)
	Linear interpolation between X and Y by coefficient t in [0, 1].
template<typename T >
	BOOST_CONCEPT_REQUIRES (((IsTestable< T >)),(bool)) check_manifold_invariants(const T &a
	Check invariants for Manifold type.
bool	assert_equal (const Matrix &A, const Matrix &B, double tol=1e-9)
	equals with an tolerance, prints out message if unequal
bool	assert_inequal (const Matrix &A, const Matrix &B, double tol=1e-9)
	inequals with an tolerance, prints out message if within tolerance
bool	assert_equal (const std::list< Matrix > &As, const std::list< Matrix > &Bs, double tol=1e-9)
	equals with an tolerance, prints out message if unequal
bool	linear_independent (const Matrix &A, const Matrix &B, double tol=1e-9)
	check whether the rows of two matrices are linear independent
bool	linear_dependent (const Matrix &A, const Matrix &B, double tol=1e-9)
	check whether the rows of two matrices are linear dependent
Vector	operator^ (const Matrix &A, const Vector &v)
	overload ^ for trans(A)*v We transpose the vectors for speed.
void	print (const Matrix &A, const std::string &s, std::ostream &stream)
	print without optional string, must specify cout yourself
void	print (const Matrix &A, const std::string &s="")
	print with optional string to cout
void	save (const Matrix &A, const std::string &s, const std::string &filename)
	save a matrix to file, which can be loaded by matlab
istream &	operator>> (std::istream &inputStream, Matrix &destinationMatrix)
	Read a matrix from an input stream, such as a file. More...
Matrix	diag (const std::vector< Matrix > &Hs)
	Create a matrix with submatrices along its diagonal.
Vector	columnNormSquare (const Matrix &A)
pair< Matrix, Matrix >	qr (const Matrix &A)
	Householder QR factorization, Golub & Van Loan p 224, explicit version. More...
list< boost::tuple< Vector, double, double > >	weighted_eliminate (Matrix &A, Vector &b, const Vector &sigmas)
	Imperative algorithm for in-place full elimination with weights and constraint handling. More...
void	householder_ (Matrix &A, size_t k, bool copy_vectors)
	Imperative version of Householder QR factorization, Golub & Van Loan p 224 version with Householder vectors below diagonal, as in GVL. More...
void	householder (Matrix &A, size_t k)
	Householder tranformation, zeros below diagonal. More...
Vector	backSubstituteLower (const Matrix &L, const Vector &b, bool unit=false)
	backSubstitute L*x=b More...
Vector	backSubstituteUpper (const Matrix &U, const Vector &b, bool unit=false)
	backSubstitute U*x=b More...
Vector	backSubstituteUpper (const Vector &b, const Matrix &U, bool unit=false)
	backSubstitute x'*U=b' More...
Matrix	stack (size_t nrMatrices,...)
	create a matrix by stacking other matrices Given a set of matrices: A1, A2, A3... More...
Matrix	stack (const std::vector< Matrix > &blocks)
Matrix	collect (const std::vector< const Matrix * > &matrices, size_t m=0, size_t n=0)
	create a matrix by concatenating Given a set of matrices: A1, A2, A3... More...
Matrix	collect (size_t nrMatrices,...)
void	vector_scale_inplace (const Vector &v, Matrix &A, bool inf_mask=false)
	scales a matrix row or column by the values in a vector Arguments (Matrix, Vector) scales the columns, (Vector, Matrix) scales the rows More...
Matrix	vector_scale (const Vector &v, const Matrix &A, bool inf_mask)
Matrix	vector_scale (const Matrix &A, const Vector &v, bool inf_mask)
Matrix	LLt (const Matrix &A)
Matrix	RtR (const Matrix &A)
Matrix	cholesky_inverse (const Matrix &A)
	Return the inverse of a S.P.D. More...
Matrix	inverse_square_root (const Matrix &A)
	Use Cholesky to calculate inverse square root of a matrix.
void	svd (const Matrix &A, Matrix &U, Vector &S, Matrix &V)
	SVD computes economy SVD A=U*S*V'. More...
boost::tuple< int, double, Vector >	DLT (const Matrix &A, double rank_tol=1e-9)
	Direct linear transform algorithm that calls svd to find a vector v that minimizes the algebraic error A*v. More...
Matrix	expm (const Matrix &A, size_t K=7)
	Numerical exponential map, naive approach, not industrial strength !!! More...
std::string	formatMatrixIndented (const std::string &label, const Matrix &matrix, bool makeVectorHorizontal)
void	inplace_QR (Matrix &A)
	QR factorization using Eigen's internal block QR algorithm. More...
	GTSAM_MAKE_MATRIX_DEFS (1)
	GTSAM_MAKE_MATRIX_DEFS (2)
	GTSAM_MAKE_MATRIX_DEFS (3)
	GTSAM_MAKE_MATRIX_DEFS (4)
	GTSAM_MAKE_MATRIX_DEFS (5)
	GTSAM_MAKE_MATRIX_DEFS (6)
	GTSAM_MAKE_MATRIX_DEFS (7)
	GTSAM_MAKE_MATRIX_DEFS (8)
	GTSAM_MAKE_MATRIX_DEFS (9)
template<class MATRIX >
bool	equal_with_abs_tol (const Eigen::DenseBase< MATRIX > &A, const Eigen::DenseBase< MATRIX > &B, double tol=1e-9)
	equals with a tolerance
bool	operator== (const Matrix &A, const Matrix &B)
	equality is just equal_with_abs_tol 1e-9
bool	operator!= (const Matrix &A, const Matrix &B)
	inequality
template<class MATRIX >
MATRIX	prod (const MATRIX &A, const MATRIX &B)
	products using old-style format to improve compatibility
template<class MATRIX >
Eigen::Block< const MATRIX >	sub (const MATRIX &A, size_t i1, size_t i2, size_t j1, size_t j2)
	extract submatrix, slice semantics, i.e. More...
template<typename Derived1 , typename Derived2 >
void	insertSub (Eigen::MatrixBase< Derived1 > &fullMatrix, const Eigen::MatrixBase< Derived2 > &subMatrix, size_t i, size_t j)
	insert a submatrix IN PLACE at a specified location in a larger matrix NOTE: there is no size checking More...
template<class MATRIX >
const MATRIX::ConstColXpr	column (const MATRIX &A, size_t j)
	Extracts a column view from a matrix that avoids a copy. More...
template<class MATRIX >
const MATRIX::ConstRowXpr	row (const MATRIX &A, size_t j)
	Extracts a row view from a matrix that avoids a copy. More...
template<class MATRIX >
void	zeroBelowDiagonal (MATRIX &A, size_t cols=0)
	Zeros all of the elements below the diagonal of a matrix, in place. More...
Matrix	trans (const Matrix &A)
	static transpose function, just calls Eigen transpose member function
template<int OutM, int OutN, int OutOptions, int InM, int InN, int InOptions>
Reshape< OutM, OutN, OutOptions, InM, InN, InOptions >::ReshapedType	reshape (const Eigen::Matrix< double, InM, InN, InOptions > &m)
Matrix3	skewSymmetric (double wx, double wy, double wz)
	skew symmetric matrix returns this: 0 -wz wy wz 0 -wx -wy wx 0 More...
template<class Derived >
Matrix3	skewSymmetric (const Eigen::MatrixBase< Derived > &w)
template<class X >
internal::FixedSizeMatrix< X >::type	numericalGradient (boost::function< double(const X &)> h, const X &x, double delta=1e-5)
	Numerically compute gradient of scalar function Class X is the input argument The class X needs to have dim, expmap, logmap.
template<class Y , class X >
internal::FixedSizeMatrix< Y, X >::type	numericalDerivative11 (boost::function< Y(const X &)> h, const X &x, double delta=1e-5)
	New-style numerical derivatives using manifold_traits. More...
template<class Y , class X >
internal::FixedSizeMatrix< Y, X >::type	numericalDerivative11 (Y(*h)(const X &), const X &x, double delta=1e-5)
	use a raw C++ function pointer
template<class Y , class X1 , class X2 >
internal::FixedSizeMatrix< Y, X1 >::type	numericalDerivative21 (const boost::function< Y(const X1 &, const X2 &)> &h, const X1 &x1, const X2 &x2, double delta=1e-5)
	Compute numerical derivative in argument 1 of binary function. More...
template<class Y , class X1 , class X2 >
internal::FixedSizeMatrix< Y, X1 >::type	numericalDerivative21 (Y(*h)(const X1 &, const X2 &), const X1 &x1, const X2 &x2, double delta=1e-5)
	use a raw C++ function pointer
template<class Y , class X1 , class X2 >
internal::FixedSizeMatrix< Y, X2 >::type	numericalDerivative22 (boost::function< Y(const X1 &, const X2 &)> h, const X1 &x1, const X2 &x2, double delta=1e-5)
	Compute numerical derivative in argument 2 of binary function. More...
template<class Y , class X1 , class X2 >
internal::FixedSizeMatrix< Y, X2 >::type	numericalDerivative22 (Y(*h)(const X1 &, const X2 &), const X1 &x1, const X2 &x2, double delta=1e-5)
	use a raw C++ function pointer
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X1 >::type	numericalDerivative31 (boost::function< Y(const X1 &, const X2 &, const X3 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
	Compute numerical derivative in argument 1 of ternary function. More...
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X1 >::type	numericalDerivative31 (Y(*h)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X2 >::type	numericalDerivative32 (boost::function< Y(const X1 &, const X2 &, const X3 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
	Compute numerical derivative in argument 2 of ternary function. More...
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X2 >::type	numericalDerivative32 (Y(*h)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X3 >::type	numericalDerivative33 (boost::function< Y(const X1 &, const X2 &, const X3 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
	Compute numerical derivative in argument 3 of ternary function. More...
template<class Y , class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< Y, X3 >::type	numericalDerivative33 (Y(*h)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class Y , class X1 , class X2 , class X3 , class X4 >
internal::FixedSizeMatrix< Y, X1 >::type	numericalDerivative41 (boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, const X4 &x4, double delta=1e-5)
	Compute numerical derivative in argument 1 of 4-argument function. More...
template<class Y , class X1 , class X2 , class X3 , class X4 >
internal::FixedSizeMatrix< Y, X2 >::type	numericalDerivative42 (boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, const X4 &x4, double delta=1e-5)
	Compute numerical derivative in argument 2 of 4-argument function. More...
template<class Y , class X1 , class X2 , class X3 , class X4 >
internal::FixedSizeMatrix< Y, X3 >::type	numericalDerivative43 (boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, const X4 &x4, double delta=1e-5)
	Compute numerical derivative in argument 3 of 4-argument function. More...
template<class Y , class X1 , class X2 , class X3 , class X4 >
internal::FixedSizeMatrix< Y, X4 >::type	numericalDerivative44 (boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)> h, const X1 &x1, const X2 &x2, const X3 &x3, const X4 &x4, double delta=1e-5)
	Compute numerical derivative in argument 4 of 4-argument function. More...
template<class X >
internal::FixedSizeMatrix< X, X >::type	numericalHessian (boost::function< double(const X &)> f, const X &x, double delta=1e-5)
	Compute numerical Hessian matrix. More...
template<class X >
internal::FixedSizeMatrix< X, X >::type	numericalHessian (double(*f)(const X &), const X &x, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X1, X2 >::type	numericalHessian212 (boost::function< double(const X1 &, const X2 &)> f, const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X1, X2 >::type	numericalHessian212 (double(*f)(const X1 &, const X2 &), const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X1, X1 >::type	numericalHessian211 (boost::function< double(const X1 &, const X2 &)> f, const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X1, X1 >::type	numericalHessian211 (double(*f)(const X1 &, const X2 &), const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X2, X2 >::type	numericalHessian222 (boost::function< double(const X1 &, const X2 &)> f, const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 >
internal::FixedSizeMatrix< X2, X2 >::type	numericalHessian222 (double(*f)(const X1 &, const X2 &), const X1 &x1, const X2 &x2, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X1 >::type	numericalHessian311 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
	Numerical Hessian for tenary functions.
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X1 >::type	numericalHessian311 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X2, X2 >::type	numericalHessian322 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X2, X2 >::type	numericalHessian322 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X3, X3 >::type	numericalHessian333 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X3, X3 >::type	numericalHessian333 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X2 >::type	numericalHessian312 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X3 >::type	numericalHessian313 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X2, X3 >::type	numericalHessian323 (boost::function< double(const X1 &, const X2 &, const X3 &)> f, const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X2 >::type	numericalHessian312 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X1, X3 >::type	numericalHessian313 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class X1 , class X2 , class X3 >
internal::FixedSizeMatrix< X2, X3 >::type	numericalHessian323 (double(*f)(const X1 &, const X2 &, const X3 &), const X1 &x1, const X2 &x2, const X3 &x3, double delta=1e-5)
template<class T >
std::string	serialize (const T &input)
template<class T >
void	deserialize (const std::string &serialized, T &output)
template<class T >
bool	serializeToFile (const T &input, const std::string &filename)
template<class T >
bool	deserializeFromFile (const std::string &filename, T &output)
template<class T >
std::string	serializeXML (const T &input, const std::string &name="data")
template<class T >
void	deserializeXML (const std::string &serialized, T &output, const std::string &name="data")
template<class T >
bool	serializeToXMLFile (const T &input, const std::string &filename, const std::string &name="data")
template<class T >
bool	deserializeFromXMLFile (const std::string &filename, T &output, const std::string &name="data")
template<class T >
std::string	serializeBinary (const T &input, const std::string &name="data")
template<class T >
void	deserializeBinary (const std::string &serialized, T &output, const std::string &name="data")
template<class T >
bool	serializeToBinaryFile (const T &input, const std::string &filename, const std::string &name="data")
template<class T >
bool	deserializeFromBinaryFile (const std::string &filename, T &output, const std::string &name="data")
void	print (float v, const std::string &s="")
void	print (double v, const std::string &s="")
template<class T >
bool	equal (const T &obj1, const T &obj2, double tol)
	Call equal on the object.
template<class T >
bool	equal (const T &obj1, const T &obj2)
	Call equal without tolerance (use default tolerance)
template<class V >
bool	assert_equal (const V &expected, const V &actual, double tol=1e-9)
	This template works for any type with equals.
bool	assert_equal (const Key &expected, const Key &actual, double tol=0.0)
	Equals testing for basic types.
template<class V >
bool	assert_equal (const boost::optional< V > &expected, const boost::optional< V > &actual, double tol=1e-9)
	Comparisons for boost.optional objects that checks whether objects exist before comparing their values. More...
template<class V >
bool	assert_equal (const V &expected, const boost::optional< V > &actual, double tol=1e-9)
template<class V >
bool	assert_equal (const V &expected, const boost::optional< const V & > &actual, double tol=1e-9)
template<class V >
bool	assert_equal (const std::vector< V > &expected, const std::vector< V > &actual, double tol=1e-9)
	Version of assert_equals to work with vectors. More...
template<class V1 , class V2 >
bool	assert_container_equal (const std::map< V1, V2 > &expected, const std::map< V1, V2 > &actual, double tol=1e-9)
	Function for comparing maps of testable->testable TODO: replace with more generalized version.
template<class V2 >
bool	assert_container_equal (const std::map< size_t, V2 > &expected, const std::map< size_t, V2 > &actual, double tol=1e-9)
	Function for comparing maps of size_t->testable.
template<class V1 , class V2 >
bool	assert_container_equal (const std::vector< std::pair< V1, V2 > > &expected, const std::vector< std::pair< V1, V2 > > &actual, double tol=1e-9)
	Function for comparing vector of pairs (testable, testable)
template<class V >
bool	assert_container_equal (const V &expected, const V &actual, double tol=1e-9)
	General function for comparing containers of testable objects.
template<class V2 >
bool	assert_container_equality (const std::map< size_t, V2 > &expected, const std::map< size_t, V2 > &actual)
	Function for comparing maps of size_t->testable Types are assumed to have operator ==.
template<class V >
bool	assert_container_equality (const V &expected, const V &actual)
	General function for comparing containers of objects with operator==.
bool	assert_equal (const std::string &expected, const std::string &actual)
	Compare strings for unit tests.
template<class V >
bool	assert_inequal (const V &expected, const V &actual, double tol=1e-9)
	Allow for testing inequality.
template<typename G >
void	testLieGroupDerivatives (TestResult &result_, const std::string &name_, const G &t1, const G &t2)
template<typename G >
void	testChartDerivatives (TestResult &result_, const std::string &name_, const G &t1, const G &t2)
void	tictoc_finishedIteration_ ()
void	tictoc_print_ ()
void	tictoc_print2_ ()
void	tictoc_reset_ ()
	BOOST_CONCEPT_ASSERT ((boost::RandomAccessRangeConcept< ListOfOneContainer< int > >))
template<typename T >
ListOfOneContainer< T >	ListOfOne (const T &element)
	Factory function for ListOfOneContainer to enable ListOfOne(e) syntax. More...
void	print (const Vector &v, const std::string &s, std::ostream &stream)
	print without optional string, must specify cout yourself
void	print (const Vector &v, const std::string &s="")
	print with optional string to cout
void	save (const Vector &A, const std::string &s, const std::string &filename)
	save a vector to file, which can be loaded by matlab
bool	operator== (const Vector &vec1, const Vector &vec2)
	operator==()
bool	greaterThanOrEqual (const Vector &v1, const Vector &v2)
	Greater than or equal to operation returns true if all elements in v1 are greater than corresponding elements in v2.
bool	equal_with_abs_tol (const Vector &vec1, const Vector &vec2, double tol=1e-9)
	VecA == VecB up to tolerance.
bool	equal_with_abs_tol (const SubVector &vec1, const SubVector &vec2, double tol)
bool	assert_equal (const Vector &vec1, const Vector &vec2, double tol=1e-9)
	Same, prints if error. More...
bool	assert_inequal (const Vector &vec1, const Vector &vec2, double tol=1e-9)
	Not the same, prints if error. More...
bool	assert_equal (const SubVector &vec1, const SubVector &vec2, double tol=1e-9)
	Same, prints if error. More...
bool	assert_equal (const ConstSubVector &expected, const ConstSubVector &actual, double tol)
bool	linear_dependent (const Vector &vec1, const Vector &vec2, double tol=1e-9)
	check whether two vectors are linearly dependent More...
Vector	ediv_ (const Vector &a, const Vector &b)
	elementwise division, but 0/0 = 0, not inf More...
double	houseInPlace (Vector &x)
	beta = house(x) computes the HouseHolder vector in place
pair< double, Vector >	house (const Vector &x)
	house(x,j) computes HouseHolder vector v and scaling factor beta from x, such that the corresponding Householder reflection zeroes out all but x. More...
double	weightedPseudoinverse (const Vector &a, const Vector &weights, Vector &pseudo)
pair< Vector, double >	weightedPseudoinverse (const Vector &v, const Vector &weights)
	Weighted Householder solution vector, a.k.a., the pseudoinverse of the column NOTE: if any sigmas are zero (indicating a constraint) the pseudoinverse will be a selection vector, and the variance will be zero. More...
Vector	concatVectors (const std::list< Vector > &vs)
	concatenate Vectors
Vector	concatVectors (size_t nrVectors,...)
	concatenate Vectors
	GTSAM_MAKE_VECTOR_DEFS (4)
	GTSAM_MAKE_VECTOR_DEFS (5)
	GTSAM_MAKE_VECTOR_DEFS (6)
	GTSAM_MAKE_VECTOR_DEFS (7)
	GTSAM_MAKE_VECTOR_DEFS (8)
	GTSAM_MAKE_VECTOR_DEFS (9)
	GTSAM_MAKE_VECTOR_DEFS (10)
	GTSAM_MAKE_VECTOR_DEFS (11)
	GTSAM_MAKE_VECTOR_DEFS (12)
bool	equal (const Vector &vec1, const Vector &vec2, double tol)
	Override of equal in Lie.h.
bool	equal (const Vector &vec1, const Vector &vec2)
	Override of equal in Lie.h.
template<class V1 , class V2 >
double	dot (const V1 &a, const V2 &b)
	Dot product.
template<class V1 , class V2 >
double	inner_prod (const V1 &a, const V2 &b)
	compatibility version for ublas' inner_prod()
void	scal (double alpha, Vector &x)
	BLAS Level 1 scal: x <- alpha*x. More...
template<class V1 , class V2 >
void	axpy (double alpha, const V1 &x, V2 &y)
	BLAS Level 1 axpy: y <- alpha*x + y. More...
void	axpy (double alpha, const Vector &x, SubVector y)
template<typename L >
std::vector< Assignment< L > >	cartesianProduct (const std::vector< std::pair< L, size_t > > &keys)
	Get Cartesian product consisting all possible configurations. More...
template<typename Y , typename L >
DecisionTree< L, Y >	apply (const DecisionTree< L, Y > &f, const typename DecisionTree< L, Y >::Unary &op)
	free versions of apply
template<typename Y , typename L >
DecisionTree< L, Y >	apply (const DecisionTree< L, Y > &f, const DecisionTree< L, Y > &g, const typename DecisionTree< L, Y >::Binary &op)
std::pair< DiscreteConditional::shared_ptr, DecisionTreeFactor::shared_ptr >	EliminateDiscrete (const DiscreteFactorGraph &factors, const Ordering &keys)
	Main elimination function for DiscreteFactorGraph.
DiscreteKeys	operator& (const DiscreteKey &key1, const DiscreteKey &key2)
	Create a list from two keys.
ostream &	operator<< (ostream &os, const Signature::Row &row)
ostream &	operator<< (ostream &os, const Signature::Table &table)
ostream &	operator<< (ostream &os, const Signature &s)
Signature	operator| (const DiscreteKey &key, const DiscreteKey &parent)
	Helper function to create Signature objects example: Signature s = D | E;.
Signature	operator% (const DiscreteKey &key, const std::string &parent)
	Helper function to create Signature objects example: Signature s(D % "99/1"); Uses string parser, which requires BOOST 1.42 or higher.
Signature	operator% (const DiscreteKey &key, const Signature::Table &parent)
	Helper function to create Signature objects, using table construction directly example: Signature s(D % table);.
ostream &	operator<< (ostream &os, const Cal3_S2 &cal)
ostream &	operator<< (ostream &os, const EssentialMatrix &E)
istream &	operator>> (istream &is, EssentialMatrix &E)
double	norm2 (const Point2 &p, OptionalJacobian< 1, 2 > H=boost::none)
	Distance of the point from the origin, with Jacobian.
double	distance2 (const Point2 &p1, const Point2 &q, OptionalJacobian< 1, 2 > H1=boost::none, OptionalJacobian< 1, 2 > H2=boost::none)
	distance between two points
ostream &	operator<< (ostream &os, const Point2 &p)
boost::optional< Point2 >	circleCircleIntersection (double R_d, double r_d, double tol)
list< Point2 >	circleCircleIntersection (Point2 c1, Point2 c2, boost::optional< Point2 > fh)
list< Point2 >	circleCircleIntersection (Point2 c1, double r1, Point2 c2, double r2, double tol=1e-9)
	Intersect 2 circles. More...
ostream &	operator<< (ostream &os, const gtsam::Point2Pair &p)
Point2	operator* (double s, const Point2 &p)
	multiply with scalar
ostream &	operator<< (ostream &os, const Point3 &p)
double	distance3 (const Point3 &p1, const Point3 &q, OptionalJacobian< 1, 3 > H1=boost::none, OptionalJacobian< 1, 3 > H2=boost::none)
	distance between two points
double	norm3 (const Point3 &p, OptionalJacobian< 1, 3 > H=boost::none)
	Distance of the point from the origin, with Jacobian.
Point3	normalize (const Point3 &p, OptionalJacobian< 3, 3 > H=boost::none)
	normalize, with optional Jacobian
Point3	cross (const Point3 &p, const Point3 &q, OptionalJacobian< 3, 3 > H_p=boost::none, OptionalJacobian< 3, 3 > H_q=boost::none)
	cross product More...
double	dot (const Point3 &p, const Point3 &q, OptionalJacobian< 1, 3 > H_p=boost::none, OptionalJacobian< 1, 3 > H_q=boost::none)
	dot product
ostream &	operator<< (ostream &os, const gtsam::Point3Pair &p)
	GTSAM_CONCEPT_POSE_INST (Pose2)
	instantiate concept checks
boost::optional< Pose2 >	align (const vector< Point2Pair > &pairs)
template<>
Matrix	wedge< Pose2 > (const Vector &xi)
	specialization for pose2 wedge function (generic template in Lie.h)
	GTSAM_CONCEPT_POSE_INST (Pose3)
	instantiate concept checks
boost::optional< Pose3 >	align (const std::vector< Point3Pair > &baPointPairs)
	Calculate pose between a vector of 3D point correspondences (b_point, a_point) where a_point = Pose3::transform_from(b_point) = t + R*b_point. More...
std::ostream &	operator<< (std::ostream &os, const Pose3 &pose)
template<>
Matrix	wedge< Pose3 > (const Vector &xi)
	wedge for Pose3: More...
pair< Matrix3, Vector3 >	RQ (const Matrix3 &A)
	[RQ] receives a 3 by 3 matrix and returns an upper triangular matrix R and 3 rotation angles corresponding to the rotation matrix Q=Qz'*Qy'*Qx' such that A = R*Q = R*Qz'*Qy'*Qx'. More...
ostream &	operator<< (ostream &os, const Rot3 &R)
SimpleCamera	simpleCamera (const Matrix34 &P)
	Recover camera from 3*4 camera matrix.
ostream &	operator<< (ostream &os, const StereoPoint2 &p)
Vector4	triangulateHomogeneousDLT (const std::vector< Matrix34 > &projection_matrices, const std::vector< Point2 > &measurements, double rank_tol=1e-9)
	DLT triangulation: See Hartley and Zisserman, 2nd Ed., page 312. More...
Point3	triangulateDLT (const std::vector< Matrix34 > &projection_matrices, const std::vector< Point2 > &measurements, double rank_tol=1e-9)
	DLT triangulation: See Hartley and Zisserman, 2nd Ed., page 312. More...
Point3	optimize (const NonlinearFactorGraph &graph, const Values &values, Key landmarkKey)
	Optimize for triangulation. More...
template<class CALIBRATION >
std::pair< NonlinearFactorGraph, Values >	triangulationGraph (const std::vector< Pose3 > &poses, boost::shared_ptr< CALIBRATION > sharedCal, const std::vector< Point2 > &measurements, Key landmarkKey, const Point3 &initialEstimate)
	Create a factor graph with projection factors from poses and one calibration. More...
template<class CAMERA >
std::pair< NonlinearFactorGraph, Values >	triangulationGraph (const std::vector< CAMERA > &cameras, const std::vector< typename CAMERA::Measurement > &measurements, Key landmarkKey, const Point3 &initialEstimate)
	Create a factor graph with projection factors from pinhole cameras (each camera has a pose and calibration) More...
template<class CALIBRATION >
std::pair< NonlinearFactorGraph, Values >	triangulationGraph (const std::vector< PinholeCamera< CALIBRATION > > &cameras, const std::vector< Point2 > &measurements, Key landmarkKey, const Point3 &initialEstimate)
	PinholeCamera specific version // TODO: (chris) why does this exist?
template<class CALIBRATION >
Point3	triangulateNonlinear (const std::vector< Pose3 > &poses, boost::shared_ptr< CALIBRATION > sharedCal, const std::vector< Point2 > &measurements, const Point3 &initialEstimate)
	Given an initial estimate , refine a point using measurements in several cameras. More...
template<class CAMERA >
Point3	triangulateNonlinear (const std::vector< CAMERA > &cameras, const std::vector< typename CAMERA::Measurement > &measurements, const Point3 &initialEstimate)
	Given an initial estimate , refine a point using measurements in several cameras. More...
template<class CALIBRATION >
Point3	triangulateNonlinear (const std::vector< PinholeCamera< CALIBRATION > > &cameras, const std::vector< Point2 > &measurements, const Point3 &initialEstimate)
	PinholeCamera specific version // TODO: (chris) why does this exist?
template<class CALIBRATION >
Point3	triangulatePoint3 (const std::vector< Pose3 > &poses, boost::shared_ptr< CALIBRATION > sharedCal, const std::vector< Point2 > &measurements, double rank_tol=1e-9, bool optimize=false)
	Function to triangulate 3D landmark point from an arbitrary number of poses (at least 2) using the DLT. More...
template<class CAMERA >
Point3	triangulatePoint3 (const std::vector< CAMERA > &cameras, const std::vector< Point2 > &measurements, double rank_tol=1e-9, bool optimize=false)
	Function to triangulate 3D landmark point from an arbitrary number of poses (at least 2) using the DLT. More...
template<class CALIBRATION >
Point3	triangulatePoint3 (const std::vector< PinholeCamera< CALIBRATION > > &cameras, const std::vector< Point2 > &measurements, double rank_tol=1e-9, bool optimize=false)
	Pinhole-specific version.
template<class CAMERA >
TriangulationResult	triangulateSafe (const std::vector< CAMERA > &cameras, const std::vector< Point2 > &measured, const TriangulationParameters &params)
	triangulateSafe: extensive checking of the outcome
std::ostream &	operator<< (std::ostream &os, const Unit3 &pair)
template<class CLIQUE >
bool	check_sharedCliques (const std::pair< Key, typename BayesTree< CLIQUE >::sharedClique > &v1, const std::pair< Key, typename BayesTree< CLIQUE >::sharedClique > &v2)
template<class KEY >
std::list< KEY >	predecessorMap2Keys (const PredecessorMap< KEY > &p_map)
	Generate a list of keys from a spanning tree represented by its predecessor map.
template<class G , class F , class KEY >
SDGraph< KEY >	toBoostGraph (const G &graph)
	Convert the factor graph to an SDGraph G = Graph type F = Factor type Key = Key type.
template<class G , class V , class KEY >
boost::tuple< G, V, std::map< KEY, V > >	predecessorMap2Graph (const PredecessorMap< KEY > &p_map)
	Build takes a predecessor map, and builds a directed graph corresponding to the tree. More...
template<class G , class Factor , class POSE , class KEY >
boost::shared_ptr< Values >	composePoses (const G &graph, const PredecessorMap< KEY > &tree, const POSE &rootPose)
	Compose the poses by following the chain specified by the spanning tree.
template<class G , class KEY , class FACTOR2 >
PredecessorMap< KEY >	findMinimumSpanningTree (const G &g)
	find the minimum spanning tree using boost graph library
template<class G , class KEY , class FACTOR2 >
void	split (const G &g, const PredecessorMap< KEY > &tree, G &Ab1, G &Ab2)
	Split the graph into two parts: one corresponds to the given spanning tree, and the other corresponds to the rest of the factors.
string	_defaultKeyFormatter (Key key)
void	PrintKey (Key key, const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter)
	Utility function to print one key with optional prefix.
string	_multirobotKeyFormatter (Key key)
void	PrintKeyList (const KeyList &keys, const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter)
	Utility function to print sets of keys with optional prefix.
void	PrintKeyVector (const KeyVector &keys, const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter)
	Utility function to print sets of keys with optional prefix.
void	PrintKeySet (const KeySet &keys, const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter)
	Utility function to print sets of keys with optional prefix.
Key	mrsymbol (unsigned char c, unsigned char label, size_t j)
	Create a symbol key from a character, label and index, i.e. More...
unsigned char	mrsymbolChr (Key key)
	Return the character portion of a symbol key. More...
unsigned char	mrsymbolLabel (Key key)
	Return the label portion of a symbol key. More...
size_t	mrsymbolIndex (Key key)
	Return the index portion of a symbol key. More...
Key	symbol (unsigned char c, std::uint64_t j)
	Create a symbol key from a character and index, i.e. More...
unsigned char	symbolChr (Key key)
	Return the character portion of a symbol key. More...
std::uint64_t	symbolIndex (Key key)
	Return the index portion of a symbol key. More...
template<class S , class V >
V	preconditionedConjugateGradient (const S &system, const V &initial, const ConjugateGradientParameters &parameters)
double	dot (const Errors &a, const Errors &b)
	dot product
template<>
void	axpy< Errors, Errors > (double alpha, const Errors &x, Errors &y)
	BLAS level 2 style.
void	print (const Errors &a, const std::string &s="Error")
	print with optional string
template<>
GTSAM_EXPORT void	axpy< Errors, Errors > (double alpha, const Errors &x, Errors &y)
	BLAS level 2 style.
bool	hasConstraints (const GaussianFactorGraph &factors)
	Evaluates whether linear factors have any constrained noise models. More...
std::pair< boost::shared_ptr< GaussianConditional >, boost::shared_ptr< HessianFactor > >	EliminateCholesky (const GaussianFactorGraph &factors, const Ordering &keys)
std::pair< boost::shared_ptr< GaussianConditional >, boost::shared_ptr< GaussianFactor > >	EliminatePreferCholesky (const GaussianFactorGraph &factors, const Ordering &keys)
template<class S , class V , class E >
V	conjugateGradients (const S &Ab, V x, const ConjugateGradientParameters &parameters, bool steepest=false)
	Method of conjugate gradients (CG) template "System" class S needs gradient(S,v), e=S*v, v=S^e "Vector" class V needs dot(v,v), -v, v+v, s*v "Vector" class E needs dot(v,v) More...
Vector	steepestDescent (const System &Ab, const Vector &x, const ConjugateGradientParameters &parameters)
Vector	conjugateGradientDescent (const System &Ab, const Vector &x, const ConjugateGradientParameters &parameters)
	Method of conjugate gradients (CG), System version.
Vector	steepestDescent (const Matrix &A, const Vector &b, const Vector &x, const ConjugateGradientParameters &parameters)
	convenience calls using matrices, will create System class internally: More...
Vector	conjugateGradientDescent (const Matrix &A, const Vector &b, const Vector &x, const ConjugateGradientParameters &parameters)
	Method of conjugate gradients (CG), Matrix version.
VectorValues	steepestDescent (const GaussianFactorGraph &fg, const VectorValues &x, const ConjugateGradientParameters &parameters)
	Method of steepest gradients, Gaussian Factor Graph version.
VectorValues	conjugateGradientDescent (const GaussianFactorGraph &fg, const VectorValues &x, const ConjugateGradientParameters &parameters)
	Method of conjugate gradients (CG), Gaussian Factor Graph version.
GTSAM_EXPORT Vector	steepestDescent (const System &Ab, const Vector &x, const IterativeOptimizationParameters &parameters)
	Method of steepest gradients, System version.
ostream &	operator<< (ostream &os, const IterativeOptimizationParameters &p)
std::pair< GaussianConditional::shared_ptr, JacobianFactor::shared_ptr >	EliminateQR (const GaussianFactorGraph &factors, const Ordering &keys)
	Multiply all factors and eliminate the given keys from the resulting factor using a QR variant that handles constraints (zero sigmas). More...
ostream &	operator<< (ostream &os, const PreconditionerParameters &p)
boost::shared_ptr< Preconditioner >	createPreconditioner (const boost::shared_ptr< PreconditionerParameters > parameters)
vector< size_t >	uniqueSampler (const vector< double > &weight, const size_t n)
std::ostream &	operator<< (std::ostream &os, const SubgraphEdge &edge)
std::ostream &	operator<< (std::ostream &os, const Subgraph &subgraph)
ostream &	operator<< (ostream &os, const SubgraphBuilderParameters &p)
Vector	getSubvector (const Vector &src, const KeyInfo &keyInfo, const FastVector< Key > &keys)
void	setSubvector (const Vector &src, const KeyInfo &keyInfo, const FastVector< Key > &keys, Vector &dst)
boost::shared_ptr< GaussianFactorGraph >	buildFactorSubgraph (const GaussianFactorGraph &gfg, const Subgraph &subgraph, const bool clone)
template<typename Container >
std::vector< size_t >	sort_idx (const Container &src)
VectorValues	operator* (const double a, const VectorValues &v)
std::ostream &	operator<< (std::ostream &os, const ImuFactor &f)
std::ostream &	operator<< (std::ostream &os, const ImuFactor2 &f)
ostream &	operator<< (ostream &os, const NavState &state)
ostream &	operator<< (ostream &os, const PreintegrationBase &pim)
template<typename T >
Expression< T >	operator* (const Expression< T > &expression1, const Expression< T > &expression2)
	Construct a product expression, assumes T::compose(T) -> T. More...
template<typename T >
std::vector< Expression< T > >	createUnknowns (size_t n, char c, size_t start)
	Construct an array of leaves. More...
template<typename T , typename A >
Expression< T >	linearExpression (const boost::function< T(A)> &f, const Expression< A > &expression, const Eigen::Matrix< double, traits< T >::dimension, traits< A >::dimension > &dTdA)
	Create an expression out of a linear function f:T->A with (constant) Jacobian dTdA TODO(frank): create a more efficient version like ScalarMultiplyExpression. More...
template<typename T >
ScalarMultiplyExpression< T >	operator* (double s, const Expression< T > &e)
	Construct an expression that executes the scalar multiplication with an input expression The type T must be a vector space Example: Expression<Point2> a(0), b = 12 * a;.
template<typename T >
BinarySumExpression< T >	operator+ (const Expression< T > &e1, const Expression< T > &e2)
	Construct an expression that sums two input expressions of the same type T The type T must be a vector space Example: Expression<Point2> a(0), b(1), c = a + b;.
template<typename T >
BinarySumExpression< T >	operator- (const Expression< T > &e1, const Expression< T > &e2)
	Construct an expression that subtracts one expression from another.
template<typename T >
Expression< T >	between (const Expression< T > &t1, const Expression< T > &t2)
template<typename T >
Expression< T >	compose (const Expression< T > &t1, const Expression< T > &t2)
JacobianFactor	linearizeNumerically (const NoiseModelFactor &factor, const Values &values, double delta=1e-5)
	Linearize a nonlinear factor using numerical differentiation The benefit of this method is that it does not need to know what types are involved to evaluate the factor. More...
void	CheckRelinearizationRecursiveDouble (KeySet &relinKeys, double threshold, const VectorValues &delta, const ISAM2Clique::shared_ptr &clique)
void	CheckRelinearizationRecursiveMap (KeySet &relinKeys, const FastMap< char, Vector > &thresholds, const VectorValues &delta, const ISAM2Clique::shared_ptr &clique)
template<class CLIQUE >
size_t	optimizeWildfire (const boost::shared_ptr< CLIQUE > &root, double threshold, const KeySet &replaced, VectorValues &delta)
	Optimize the BayesTree, starting from the root. More...
template<class CLIQUE >
size_t	optimizeWildfireNonRecursive (const boost::shared_ptr< CLIQUE > &root, double threshold, const KeySet &keys, VectorValues &delta)
template<class CLIQUE >
void	nnz_internal (const boost::shared_ptr< CLIQUE > &clique, int &result)
template<class CLIQUE >
int	calculate_nnz (const boost::shared_ptr< CLIQUE > &clique)
	calculate the number of non-zero entries for the tree starting at clique (use root for complete matrix)
template<class S , class V , class W >
double	lineSearch (const S &system, const V currentValues, const W &gradient)
	Implement the golden-section line search algorithm.
template<class S , class V >
boost::tuple< V, int >	nonlinearConjugateGradient (const S &system, const V &initial, const NonlinearOptimizerParams &params, const bool singleIteration, const bool gradientDescent=false)
	Implement the nonlinear conjugate gradient method using the Polak-Ribiere formula suggested in http://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method. More...
bool	checkConvergence (double relativeErrorTreshold, double absoluteErrorTreshold, double errorThreshold, double currentError, double newError, NonlinearOptimizerParams::Verbosity verbosity=NonlinearOptimizerParams::SILENT)
	Check whether the relative error decrease is less than relativeErrorTreshold, the absolute error decrease is less than absoluteErrorTreshold, or the error itself is less than errorThreshold.
GTSAM_EXPORT bool	checkConvergence (const NonlinearOptimizerParams &params, double currentError, double newError)
string	findExampleDataFile (const std::string &name)
	Find the full path to an example dataset distributed with gtsam. More...
string	createRewrittenFileName (const std::string &name)
	Creates a temporary file name that needs to be ignored in .gitingnore for checking read-write oprations.
GraphAndValues	load2D (pair< string, SharedNoiseModel > dataset, int maxID, bool addNoise, bool smart, NoiseFormat noiseFormat, KernelFunctionType kernelFunctionType)
	Load TORO 2D Graph. More...
GraphAndValues	load2D (const std::string &filename, SharedNoiseModel model=SharedNoiseModel(), Key maxID=0, bool addNoise=false, bool smart=true, NoiseFormat noiseFormat=NoiseFormatAUTO,KernelFunctionType kernelFunctionType=KernelFunctionTypeNONE)
	Load TORO/G2O style graph files. More...
GraphAndValues	load2D_robust (const string &filename, noiseModel::Base::shared_ptr &model, int maxID)
void	save2D (const NonlinearFactorGraph &graph, const Values &config, const noiseModel::Diagonal::shared_ptr model, const std::string &filename)
	save 2d graph
GraphAndValues	readG2o (const std::string &g2oFile, const bool is3D=false, KernelFunctionType kernelFunctionType=KernelFunctionTypeNONE)
	This function parses a g2o file and stores the measurements into a NonlinearFactorGraph and the initial guess in a Values structure. More...
void	writeG2o (const NonlinearFactorGraph &graph, const Values &estimate, const std::string &filename)
	This function writes a g2o file from NonlinearFactorGraph and a Values structure. More...
GraphAndValues	load3D (const std::string &filename)
	Load TORO 3D Graph.
Rot3	openGLFixedRotation ()
Pose3	openGL2gtsam (const Rot3 &R, double tx, double ty, double tz)
	This function converts an openGL camera pose to an GTSAM camera pose. More...
Pose3	gtsam2openGL (const Rot3 &R, double tx, double ty, double tz)
	This function converts a GTSAM camera pose to an openGL camera pose. More...
Pose3	gtsam2openGL (const Pose3 &PoseGTSAM)
	This function converts a GTSAM camera pose to an openGL camera pose. More...
bool	readBundler (const std::string &filename, SfM_data &data)
	This function parses a bundler output file and stores the data into a SfM_data structure. More...
bool	readBAL (const std::string &filename, SfM_data &data)
	This function parses a "Bundle Adjustment in the Large" (BAL) file and stores the data into a SfM_data structure. More...
bool	writeBAL (const std::string &filename, SfM_data &data)
	This function writes a "Bundle Adjustment in the Large" (BAL) file from a SfM_data structure. More...
bool	writeBALfromValues (const std::string &filename, const SfM_data &data, Values &values)
	This function writes a "Bundle Adjustment in the Large" (BAL) file from a SfM_data structure and a value structure (measurements are the same as the SfM input data, while camera poses and values are read from Values) More...
Values	initialCamerasEstimate (const SfM_data &db)
	This function creates initial values for cameras from db. More...
Values	initialCamerasAndPointsEstimate (const SfM_data &db)
	This function creates initial values for cameras and points from db. More...
GTSAM_EXPORT GraphAndValues	load2D (std::pair< std::string, SharedNoiseModel > dataset, int maxID=0, bool addNoise=false, bool smart=true, NoiseFormat noiseFormat=NoiseFormatAUTO, KernelFunctionType kernelFunctionType=KernelFunctionTypeNONE)
	Load TORO 2D Graph. More...
Point2_	transform_to (const Pose2_ &x, const Point2_ &p)
Point3_	transform_to (const Pose3_ &x, const Point3_ &p)
Point3_	transform_from (const Pose3_ &x, const Point3_ &p)
Point3_	rotate (const Rot3_ &x, const Point3_ &p)
Point3_	unrotate (const Rot3_ &x, const Point3_ &p)
Point2_	project (const Point3_ &p_cam)
	Expression version of PinholeBase::Project.
Point2_	project (const Unit3_ &p_cam)
template<class CAMERA , class POINT >
Point2_	project2 (const Expression< CAMERA > &camera_, const Expression< POINT > &p_)
template<class CALIBRATION , class POINT >
Point2_	project3 (const Pose3_ &x, const Expression< POINT > &p, const Expression< CALIBRATION > &K)
template<class CALIBRATION >
Point2_	uncalibrate (const Expression< CALIBRATION > &K, const Point2_ &xy_hat)
template<class T , class P >
P	transform_point (const T &trans, const P &global, boost::optional< Matrix & > Dtrans, boost::optional< Matrix & > Dglobal)
	Transform function that must be specialized specific domains. More...
std::pair< boost::shared_ptr< SymbolicConditional >, boost::shared_ptr< SymbolicFactor > >	EliminateSymbolic (const SymbolicFactorGraph &factors, const Ordering &keys)
	Dense elimination function for symbolic factors. More...
utility functions
VectorValues	buildVectorValues (const Vector &v, const Ordering &ordering, const std::map< Key, size_t > &dimensions)
	Create VectorValues from a Vector.
VectorValues	buildVectorValues (const Vector &v, const KeyInfo &keyInfo)
	Create VectorValues from a Vector and a KeyInfo class.

Variables
GTSAM_EXPORT FastMap< std::string, ValueWithDefault< bool, false > >	debugFlags
const G &	b
const G double	tol
const double	logSqrt2PI = log(std::sqrt(2.0 * M_PI))
	constant needed below

Detailed Description

Global functions in a separate testing namespace.

triangulationFactor.h

Matrix is a typedef in the gtsam namespace TODO: make a version to work with matlab wrapping we use the default < double,col_major,unbounded_array<double> >

These should not be used outside of tests, as they are just remappings of the original functions. We use these to avoid needing to do too much boost::bind magic or writing a bunch of separate proxy functions.

Don't expect all classes to work for all of these functions.

Date

March 2, 2014

Author

Frank Dellaert

Typedef Documentation

typedef noiseModel::Base::shared_ptr gtsam::SharedNoiseModel

Note, deliberately not in noiseModel namespace.

Deprecated. Only for compatibility with previous version.

Enumeration Type Documentation

enum gtsam::NoiseFormat

Indicates how noise parameters are stored in file.

Enumerator
NoiseFormatG2O	Information matrix I11, I12, I13, I22, I23, I33.
NoiseFormatTORO	Information matrix, but inf_ff inf_fs inf_ss inf_rr inf_fr inf_sr.
NoiseFormatGRAPH	default: toro-style order, but covariance matrix !
NoiseFormatCOV	Covariance matrix C11, C12, C13, C22, C23, C33.
NoiseFormatAUTO	Try to guess covariance matrix layout.

Function Documentation

GTSAM_EXPORT boost::optional< Pose3 > gtsam::align

(

const std::vector< Point3Pair > &

baPointPairs

)

Calculate pose between a vector of 3D point correspondences (b_point, a_point) where a_point = Pose3::transform_from(b_point) = t + R*b_point.

Deprecated:

: use Pose3::Align with point pairs ordered the opposite way

template<class V >

bool gtsam::assert_equal	(	const boost::optional< V > &	expected,
		const boost::optional< V > &	actual,
		double	tol = 1e-9
	)

Comparisons for boost.optional objects that checks whether objects exist before comparing their values.

First version allows for both to be boost::none, but the second, with expected given rather than optional

Concept requirement: V is testable

template<class V >

bool gtsam::assert_equal	(	const std::vector< V > &	expected,
		const std::vector< V > &	actual,
		double	tol = 1e-9
	)

Version of assert_equals to work with vectors.

Deprecated:

: use container equals instead

GTSAM_EXPORT bool gtsam::assert_equal	(	const Vector &	vec1,
		const Vector &	vec2,
		double	tol = 1e-9
	)

Same, prints if error.

Parameters

vec1	Vector
vec2	Vector
tol	1e-9

Returns

bool

GTSAM_EXPORT bool gtsam::assert_equal	(	const SubVector &	vec1,
		const SubVector &	vec2,
		double	tol = 1e-9
	)

Same, prints if error.

Parameters

vec1	Vector
vec2	Vector
tol	1e-9

Returns

bool

GTSAM_EXPORT bool gtsam::assert_inequal	(	const Vector &	vec1,
		const Vector &	vec2,
		double	tol = 1e-9
	)

Not the same, prints if error.

Parameters

vec1	Vector
vec2	Vector
tol	1e-9

Returns

bool

template<class V1 , class V2 >

void gtsam::axpy ( double  alpha, const V1 &  x, V2 &  y  )

inline

BLAS Level 1 axpy: y <- alpha*x + y.

Deprecated:

: use operators instead

GTSAM_EXPORT Vector gtsam::backSubstituteLower	(	const Matrix &	L,
		const Vector &	b,
		bool	unit = false
	)

backSubstitute L*x=b

Parameters

L	an lower triangular matrix
b	an RHS vector
unit,set	true if unit triangular

Returns

the solution x of L*x=b

GTSAM_EXPORT Vector gtsam::backSubstituteUpper	(	const Matrix &	U,
		const Vector &	b,
		bool	unit = false
	)

backSubstitute U*x=b

Parameters

U	an upper triangular matrix
b	an RHS vector
unit,set	true if unit triangular

Returns

the solution x of U*x=b

GTSAM_EXPORT Vector gtsam::backSubstituteUpper	(	const Vector &	b,
		const Matrix &	U,
		bool	unit = false
	)

backSubstitute x'*U=b'

Parameters

U	an upper triangular matrix
b	an RHS vector
unit,set	true if unit triangular

Returns

the solution x of x'*U=b'

template<class T >

T gtsam::BCH	(	const T &	X,
		const T &	Y
	)

Three term approximation of the Baker-Campbell-Hausdorff formula In non-commutative Lie groups, when composing exp(Z) = exp(X)exp(Y) it is not true that Z = X+Y.

Instead, Z can be calculated using the BCH formula: Z = X + Y + [X,Y]/2 + [X-Y,[X,Y]]/12 - [Y,[X,[X,Y]]]/24 http://en.wikipedia.org/wiki/Baker-Campbell-Hausdorff_formulaAGC: bracket() only appears in Rot3 tests, should this be used elsewhere?

template<class Class >

Class gtsam::between_default ( const Class &  l1, const Class &  l2  )

inline

These core global functions can be specialized by new Lie types for better performance.

Compute l0 s.t. l2=l1*l0

template<typename L >

std::vector<Assignment<L> > gtsam::cartesianProduct

(

const std::vector< std::pair< L, size_t > > &

keys

)

Get Cartesian product consisting all possible configurations.

Parameters

vector

list of keys (label,cardinality) pairs.

Returns

vector list of all possible value assignments

This function returns a vector of Assignment values for all possible (Cartesian product) configurations of set of Keys which are nothing but (Label,cardinality) pairs. This function should NOT be called for more than a small number of variables and cardinalities. E.g. For 6 variables with each having cardinalities 4, we get 4096 possible configurations!!

GTSAM_EXPORT Matrix gtsam::cholesky_inverse

(

const Matrix &

A

)

Return the inverse of a S.P.D.

matrix. Inversion is done via Cholesky decomposition.

GTSAM_EXPORT std::pair< size_t, bool > gtsam::choleskyCareful	(	Matrix &	ATA,
		int	order = -1
	)

"Careful" Cholesky computes the positive square-root of a positive symmetric semi-definite matrix (i.e.

that may be rank-deficient). Unlike standard Cholesky, the square-root factor may have all-zero rows for free variables.

Additionally, this function returns the index of the row after the last non-zero row in the computed factor, so that it may be truncated to an upper-trapazoidal matrix.

The second element of the return value is true if the matrix was factored successfully, or false if it was non-positive-semidefinite (i.e. indefinite or negative-(semi-)definite.

Note that this returned index is the rank of the matrix if and only if all of the zero-rows of the factor occur after any non-zero rows. This is (always?) the case during elimination of a fully-constrained least-squares problem.

The optional order argument specifies the size of the square upper-left submatrix to operate on, ignoring the rest of the matrix.

GTSAM_EXPORT bool gtsam::choleskyPartial	(	Matrix &	ABC,
		size_t	nFrontal,
		size_t	topleft = 0
	)

Partial Cholesky computes a factor [R S such that [R' 0 [R S = [A B 0 L] S' I] 0 L] B' C].

The input to this function is the matrix ABC = [A B], and the parameter [B' C] nFrontal determines the split between A, B, and C, with A being of size nFrontal x nFrontal.

if non-zero, factorization proceeds in bottom-right corner starting at topleft

Returns

true if the decomposition is successful, false if A was not positive-definite.

std::list< Point2 > gtsam::circleCircleIntersection	(	Point2	c1,
		double	r1,
		Point2	c2,
		double	r2,
		double	tol = 1e-9
	)

Intersect 2 circles.

Parameters

c1	center of first circle
r1	radius of first circle
c2	center of second circle
r2	radius of second circle
tol	absolute tolerance below which we consider touching circles

Returns

list of solutions (0,1, or 2). Identical circles will return empty list, as well.

GTSAM_EXPORT Matrix gtsam::collect	(	const std::vector< const Matrix * > &	matrices,
		size_t	m = 0,
		size_t	n = 0
	)

create a matrix by concatenating Given a set of matrices: A1, A2, A3...

If all matrices have the same size, specifying single matrix dimensions will avoid the lookup of dimensions

Parameters

matrices	is a vector of matrices in the order to be collected
m	is the number of rows of a single matrix
n	is the number of columns of a single matrix

Returns

combined matrix [A1 A2 A3]

template<class MATRIX >

const MATRIX::ConstColXpr gtsam::column	(	const MATRIX &	A,
		size_t	j
	)

Extracts a column view from a matrix that avoids a copy.

Parameters

A	matrix to extract column from
j	index of the column

Returns

a const view of the matrix

template<class S , class V , class E >

V gtsam::conjugateGradients	(	const S &	Ab,
		V	x,
		const ConjugateGradientParameters &	parameters,
		bool	steepest = false
	)

Method of conjugate gradients (CG) template "System" class S needs gradient(S,v), e=S*v, v=S^e "Vector" class V needs dot(v,v), -v, v+v, s*v "Vector" class E needs dot(v,v)

Parameters

Ab,the	"system" that needs to be solved, examples below
x	is the initial estimate
steepest	flag, if true does steepest descent, not CG

template<typename T >

std::vector< Expression< T > > gtsam::createUnknowns	(	size_t	n,
		char	c,
		size_t	start
	)

Construct an array of leaves.

Construct an array of unknown expressions with successive symbol keys Example: createUnknowns<Pose2>(3,'x') creates unknown expressions for x0,x1,x2.

Point3 gtsam::cross	(	const Point3 &	p,
		const Point3 &	q,
		OptionalJacobian< 3, 3 >	H_p = boost::none,
		OptionalJacobian< 3, 3 >	H_q = boost::none
	)

cross product

Returns

this x q

GTSAM_EXPORT boost::tuple< int, double, Vector > gtsam::DLT	(	const Matrix &	A,
		double	rank_tol = 1e-9
	)

Direct linear transform algorithm that calls svd to find a vector v that minimizes the algebraic error A*v.

Parameters

A	of size m*n, where m>=n (pad with zero rows if not!) Returns rank of A, minimum error (singular value), and corresponding eigenvector (column of V, with A=U*S*V')

GTSAM_EXPORT Vector gtsam::ediv_	(	const Vector &	a,
		const Vector &	b
	)

elementwise division, but 0/0 = 0, not inf

Parameters

a	first vector
b	second vector

Returns

vector [a(i)/b(i)]

GTSAM_EXPORT std::pair< boost::shared_ptr< GaussianConditional >, boost::shared_ptr< JacobianFactor > > gtsam::EliminateQR	(	const GaussianFactorGraph &	factors,
		const Ordering &	keys
	)

Multiply all factors and eliminate the given keys from the resulting factor using a QR variant that handles constraints (zero sigmas).

Computation happens in noiseModel::Gaussian::QR Returns a conditional on those keys, and a new factor on the separator.

GTSAM_EXPORT std::pair< boost::shared_ptr< SymbolicConditional >, boost::shared_ptr< SymbolicFactor > > gtsam::EliminateSymbolic	(	const SymbolicFactorGraph &	factors,
		const Ordering &	keys
	)

Dense elimination function for symbolic factors.

This is usually provided as an argument to one of the factor graph elimination functions (see EliminateableFactorGraph). The factor graph elimination functions do sparse variable elimination, and use this function to eliminate single variables or variable cliques.

template<class T >

T gtsam::expm	(	const Vector &	x,
		int	K = 7
	)

Exponential map given exponential coordinates class T needs a wedge<> function and a constructor from Matrix.

Parameters

x	exponential coordinates, vector of size n @ return a T

GTSAM_EXPORT Matrix gtsam::expm	(	const Matrix &	A,
		size_t	K = 7
	)

Numerical exponential map, naive approach, not industrial strength !!!

Parameters

A	matrix to exponentiate
K	number of iterations

template<class Class >

Class gtsam::expmap_default ( const Class &  t, const Vector &  d  )

inline

Exponential map centered at l0, s.t.

exp(t,d) = t*exp(d)

GTSAM_EXPORT std::string gtsam::findExampleDataFile

(

const std::string &

name

)

Find the full path to an example dataset distributed with gtsam.

The name may be specified with or without a file extension - if no extension is given, this function first looks for the .graph extension, then .txt. We first check the gtsam source tree for the file, followed by the installed example dataset location. Both the source tree and installed locations are obtained from CMake during compilation.

Returns

The full path and filename to the requested dataset.

Exceptions

std::invalid_argument

if no matching file could be found using the search process described above.

GTSAM_EXPORT Pose3 gtsam::gtsam2openGL	(	const Rot3 &	R,
		double	tx,
		double	ty,
		double	tz
	)

This function converts a GTSAM camera pose to an openGL camera pose.

Parameters

R	rotation in GTSAM
tx	x component of the translation in GTSAM
ty	y component of the translation in GTSAM
tz	z component of the translation in GTSAM

Returns

Pose3 in openGL format

GTSAM_EXPORT Pose3 gtsam::gtsam2openGL

(

const Pose3 &

PoseGTSAM

)

This function converts a GTSAM camera pose to an openGL camera pose.

Parameters

PoseGTSAM

pose in GTSAM format

Returns

Pose3 in openGL format

GTSAM_EXPORT bool gtsam::hasConstraints

(

const GaussianFactorGraph &

factors

)

Evaluates whether linear factors have any constrained noise models.

Returns

true if any factor is constrained.

GTSAM_EXPORT std::pair< double, Vector > gtsam::house

(

const Vector &

x

)

house(x,j) computes HouseHolder vector v and scaling factor beta from x, such that the corresponding Householder reflection zeroes out all but x.

(j), j is base 0. Golub & Van Loan p 210.

GTSAM_EXPORT void gtsam::householder	(	Matrix &	A,
		size_t	k
	)

Householder tranformation, zeros below diagonal.

Parameters

k	number of columns to zero out below diagonal
A	matrix

Returns

nothing: in place !!!

GTSAM_EXPORT void gtsam::householder_	(	Matrix &	A,
		size_t	k,
		bool	copy_vectors = true
	)

Imperative version of Householder QR factorization, Golub & Van Loan p 224 version with Householder vectors below diagonal, as in GVL.

Householder transformation, Householder vectors below diagonal.

Parameters

k	number of columns to zero out below diagonal
A	matrix
copy_vectors	- true to copy Householder vectors below diagonal

Returns

nothing: in place !!!

GTSAM_EXPORT Values gtsam::initialCamerasAndPointsEstimate

(

const SfM_data &

db

)

This function creates initial values for cameras and points from db.

Parameters

SfM_data

Returns

Values

GTSAM_EXPORT Values gtsam::initialCamerasEstimate

(

const SfM_data &

db

)

This function creates initial values for cameras from db.

Parameters

SfM_data

Returns

Values

void gtsam::inplace_QR

(

Matrix &

A

)

QR factorization using Eigen's internal block QR algorithm.

Parameters

A	is the input matrix, and is the output
clear_below_diagonal	enables zeroing out below diagonal

template<typename Derived1 , typename Derived2 >

void gtsam::insertSub	(	Eigen::MatrixBase< Derived1 > &	fullMatrix,
		const Eigen::MatrixBase< Derived2 > &	subMatrix,
		size_t	i,
		size_t	j
	)

insert a submatrix IN PLACE at a specified location in a larger matrix NOTE: there is no size checking

Parameters

fullMatrix	matrix to be updated
subMatrix	matrix to be inserted
i	is the row of the upper left corner insert location
j	is the column of the upper left corner insert location

GTSAM_EXPORT bool gtsam::linear_dependent	(	const Vector &	vec1,
		const Vector &	vec2,
		double	tol = 1e-9
	)

check whether two vectors are linearly dependent

Parameters

vec1	Vector
vec2	Vector
tol	1e-9

Returns

bool

template<typename T , typename A >

Expression<T> gtsam::linearExpression	(	const boost::function< T(A)> &	f,
		const Expression< A > &	expression,
		const Eigen::Matrix< double, traits< T >::dimension, traits< A >::dimension > &	dTdA
	)

Create an expression out of a linear function f:T->A with (constant) Jacobian dTdA TODO(frank): create a more efficient version like ScalarMultiplyExpression.

This version still does a malloc every linearize.

JacobianFactor gtsam::linearizeNumerically	(	const NoiseModelFactor &	factor,
		const Values &	values,
		double	delta = 1e-5
	)

Linearize a nonlinear factor using numerical differentiation The benefit of this method is that it does not need to know what types are involved to evaluate the factor.

If all the machinery of gtsam is working correctly, we should get the correct numerical derivatives out the other side. NOTE(frank): factors that have non vector-space measurements use between or LocalCoordinates to evaluate the error, and their derivatives will only be correct for near-zero errors. This is fixable but expensive, and does not matter in practice as most factors will sit near zero errors anyway. However, it means that below will only be exact for the correct measurement.

template<typename T >

ListOfOneContainer<T> gtsam::ListOfOne

(

const T &

element

)

Factory function for ListOfOneContainer to enable ListOfOne(e) syntax.

GTSAM_EXPORT GraphAndValues gtsam::load2D	(	std::pair< std::string, SharedNoiseModel >	dataset,
		int	maxID = 0,
		bool	addNoise = false,
		bool	smart = true,
		NoiseFormat	noiseFormat = NoiseFormatAUTO,
		KernelFunctionType	kernelFunctionType = KernelFunctionTypeNONE
	)

Load TORO 2D Graph.

Parameters

dataset/model	pair as constructed by [dataset]
maxID	if non-zero cut out vertices >= maxID
addNoise	add noise to the edges
smart	try to reduce complexity of covariance to cheapest model

GraphAndValues gtsam::load2D	(	std::pair< std::string, SharedNoiseModel >	dataset,
		int	maxID = 0,
		bool	addNoise = false,
		bool	smart = true,
		NoiseFormat	noiseFormat = NoiseFormatAUTO,
		KernelFunctionType	kernelFunctionType = KernelFunctionTypeNONE
	)

Load TORO 2D Graph.

Parameters

dataset/model	pair as constructed by [dataset]
maxID	if non-zero cut out vertices >= maxID
addNoise	add noise to the edges
smart	try to reduce complexity of covariance to cheapest model

GTSAM_EXPORT GraphAndValues gtsam::load2D	(	const std::string &	filename,
		SharedNoiseModel	model = SharedNoiseModel(),
		Key	maxID = 0,
		bool	addNoise = false,
		bool	smart = true,
		NoiseFormat	noiseFormat = NoiseFormatAUTO,
		KernelFunctionType	kernelFunctionType = KernelFunctionTypeNONE
	)

Load TORO/G2O style graph files.

Parameters

filename
model	optional noise model to use instead of one specified by file
maxID	if non-zero cut out vertices >= maxID
addNoise	add noise to the edges
smart	try to reduce complexity of covariance to cheapest model
noiseFormat	how noise parameters are stored
kernelFunctionType	whether to wrap the noise model in a robust kernel

Returns

graph and initial values

GTSAM_EXPORT GraphAndValues gtsam::load2D_robust	(	const std::string &	filename,
		noiseModel::Base::shared_ptr &	model,
		int	maxID = 0
	)

Deprecated:

load2D now allows for arbitrary models and wrapping a robust kernel

template<class Class >

Vector gtsam::logmap_default ( const Class &  l0, const Class &  lp  )

inline

Log map centered at l0, s.t.

exp(l0,log(l0,lp)) = lp

Key gtsam::mrsymbol ( unsigned char  c, unsigned char  label, size_t  j  )

inline

Create a symbol key from a character, label and index, i.e.

xA5.

unsigned char gtsam::mrsymbolChr ( Key  key )

inline

Return the character portion of a symbol key.

size_t gtsam::mrsymbolIndex ( Key  key )

inline

Return the index portion of a symbol key.

unsigned char gtsam::mrsymbolLabel ( Key  key )

inline

Return the label portion of a symbol key.

template<class S , class V >

boost::tuple<V, int> gtsam::nonlinearConjugateGradient	(	const S &	system,
		const V &	initial,
		const NonlinearOptimizerParams &	params,
		const bool	singleIteration,
		const bool	gradientDescent = false
	)

Implement the nonlinear conjugate gradient method using the Polak-Ribiere formula suggested in http://en.wikipedia.org/wiki/Nonlinear_conjugate_gradient_method.

The S (system) class requires three member functions: error(state), gradient(state) and advance(state, step-size, direction). The V class denotes the state or the solution.

The last parameter is a switch between gradient-descent and conjugate gradient

template<class Y , class X >

internal::FixedSizeMatrix<Y,X>::type gtsam::numericalDerivative11	(	boost::function< Y(const X &)>	h,
		const X &	x,
		double	delta = 1e-5
	)

New-style numerical derivatives using manifold_traits.

Computes numerical derivative in argument 1 of unary function

Parameters

h	unary function yielding m-vector
x	n-dimensional value at which to evaluate h
delta	increment for numerical derivative Class Y is the output argument Class X is the input argument

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 >

internal::FixedSizeMatrix<Y,X1>::type gtsam::numericalDerivative21	(	const boost::function< Y(const X1 &, const X2 &)> &	h,
		const X1 &	x1,
		const X2 &	x2,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 1 of binary function.

Parameters

h	binary function yielding m-vector
x1	n-dimensional first argument value
x2	second argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 >

internal::FixedSizeMatrix<Y,X2>::type gtsam::numericalDerivative22	(	boost::function< Y(const X1 &, const X2 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 2 of binary function.

Parameters

h	binary function yielding m-vector
x1	first argument value
x2	n-dimensional second argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 , class X3 >

internal::FixedSizeMatrix<Y,X1>::type gtsam::numericalDerivative31	(	boost::function< Y(const X1 &, const X2 &, const X3 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 1 of ternary function.

Parameters

h	ternary function yielding m-vector
x1	n-dimensional first argument value
x2	second argument value
x3	third argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing All classes Y,X1,X2,X3 need dim, expmap, logmap

template<class Y , class X1 , class X2 , class X3 >

internal::FixedSizeMatrix<Y,X2>::type gtsam::numericalDerivative32	(	boost::function< Y(const X1 &, const X2 &, const X3 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 2 of ternary function.

Parameters

h	ternary function yielding m-vector
x1	n-dimensional first argument value
x2	second argument value
x3	third argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing All classes Y,X1,X2,X3 need dim, expmap, logmap

template<class Y , class X1 , class X2 , class X3 >

internal::FixedSizeMatrix<Y,X3>::type gtsam::numericalDerivative33	(	boost::function< Y(const X1 &, const X2 &, const X3 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 3 of ternary function.

Parameters

h	ternary function yielding m-vector
x1	n-dimensional first argument value
x2	second argument value
x3	third argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing All classes Y,X1,X2,X3 need dim, expmap, logmap

template<class Y , class X1 , class X2 , class X3 , class X4 >

internal::FixedSizeMatrix<Y,X1>::type gtsam::numericalDerivative41	(	boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		const X4 &	x4,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 1 of 4-argument function.

Parameters

h	ternary function yielding m-vector
x1	n-dimensional first argument value
x2	second argument value
x3	third argument value
x4	fourth argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 , class X3 , class X4 >

internal::FixedSizeMatrix<Y,X2>::type gtsam::numericalDerivative42	(	boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		const X4 &	x4,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 2 of 4-argument function.

Parameters

h	ternary function yielding m-vector
x1	first argument value
x2	n-dimensional second argument value
x3	third argument value
x4	fourth argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 , class X3 , class X4 >

internal::FixedSizeMatrix<Y,X3>::type gtsam::numericalDerivative43	(	boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		const X4 &	x4,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 3 of 4-argument function.

Parameters

h	ternary function yielding m-vector
x1	first argument value
x2	second argument value
x3	n-dimensional third argument value
x4	fourth argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class Y , class X1 , class X2 , class X3 , class X4 >

internal::FixedSizeMatrix<Y,X4>::type gtsam::numericalDerivative44	(	boost::function< Y(const X1 &, const X2 &, const X3 &, const X4 &)>	h,
		const X1 &	x1,
		const X2 &	x2,
		const X3 &	x3,
		const X4 &	x4,
		double	delta = 1e-5
	)

Compute numerical derivative in argument 4 of 4-argument function.

Parameters

h	ternary function yielding m-vector
x1	first argument value
x2	second argument value
x3	third argument value
x4	n-dimensional fourth argument value
delta	increment for numerical derivative

Returns

m*n Jacobian computed via central differencing

template<class X >

internal::FixedSizeMatrix<X,X>::type gtsam::numericalHessian ( boost::function< double(const X &)>  f, const X &  x, double  delta = 1e-5  )

inline

Compute numerical Hessian matrix.

Requires a single-argument Lie->scalar function. This is implemented simply as the derivative of the gradient.

Parameters

f	A function taking a Lie object as input and returning a scalar
x	The center point for computing the Hessian
delta	The numerical derivative step size

Returns

n*n Hessian matrix computed via central differencing

GTSAM_EXPORT Pose3 gtsam::openGL2gtsam	(	const Rot3 &	R,
		double	tx,
		double	ty,
		double	tz
	)

This function converts an openGL camera pose to an GTSAM camera pose.

Parameters

R	rotation in openGL
tx	x component of the translation in openGL
ty	y component of the translation in openGL
tz	z component of the translation in openGL

Returns

Pose3 in GTSAM format

template<typename T >

Expression< T > gtsam::operator*	(	const Expression< T > &	expression1,
		const Expression< T > &	expression2
	)

Construct a product expression, assumes T::compose(T) -> T.

Construct a product expression, assumes T::compose(T) -> T Example: Expression<Point2> a(0), b(1), c = a*b;.

VectorValues gtsam::operator*	(	const double	a,
		const VectorValues &	v
	)

GTSAM_EXPORT std::istream & gtsam::operator>>	(	std::istream &	inputStream,
		Matrix &	destinationMatrix
	)

Read a matrix from an input stream, such as a file.

Entries can be either tab-, space-, or comma-separated, similar to the format read by the MATLAB dlmread command.

GTSAM_EXPORT Point3 gtsam::optimize	(	const NonlinearFactorGraph &	graph,
		const Values &	values,
		Key	landmarkKey
	)

Optimize for triangulation.

Parameters

graph	nonlinear factors for projection
values	initial values
landmarkKey	to refer to landmark

Returns

refined Point3

template<class CLIQUE >

size_t gtsam::optimizeWildfire	(	const boost::shared_ptr< CLIQUE > &	root,
		double	threshold,
		const KeySet &	replaced,
		VectorValues &	delta
	)

Optimize the BayesTree, starting from the root.

Parameters

replaced	Needs to contain all variables that are contained in the top of the Bayes tree that has been redone.
delta	The current solution, an offset from the linearization point.
threshold	The maximum change against the PREVIOUS delta for non-replaced variables that can be ignored, ie. the old delta entry is kept and recursive backsubstitution might eventually stop if none of the changed variables are contained in the subtree.

Returns

The number of variables that were solved for

template<class G , class V , class KEY >

boost::tuple< G, V, std::map< KEY, V > > gtsam::predecessorMap2Graph

(

const PredecessorMap< KEY > &

p_map

)

Build takes a predecessor map, and builds a directed graph corresponding to the tree.

G = Graph type V = Vertex type

GTSAM_EXPORT std::pair< Matrix, Matrix > gtsam::qr

(

const Matrix &

A

)

Householder QR factorization, Golub & Van Loan p 224, explicit version.

QR factorization, inefficient, best use imperative householder below m*n matrix -> m*m Q, m*n R.

Parameters

A

a matrix

Returns

<Q,R> rotation matrix Q, upper triangular R

GTSAM_EXPORT bool gtsam::readBAL	(	const std::string &	filename,
		SfM_data &	data
	)

This function parses a "Bundle Adjustment in the Large" (BAL) file and stores the data into a SfM_data structure.

Parameters

filename	The name of the BAL file
data	SfM structure where the data is stored

Returns

true if the parsing was successful, false otherwise

GTSAM_EXPORT bool gtsam::readBundler	(	const std::string &	filename,
		SfM_data &	data
	)

This function parses a bundler output file and stores the data into a SfM_data structure.

Parameters

filename	The name of the bundler file
data	SfM structure where the data is stored

Returns

true if the parsing was successful, false otherwise

GTSAM_EXPORT GraphAndValues gtsam::readG2o	(	const std::string &	g2oFile,
		const bool	is3D = false,
		KernelFunctionType	kernelFunctionType = KernelFunctionTypeNONE
	)

This function parses a g2o file and stores the measurements into a NonlinearFactorGraph and the initial guess in a Values structure.

Parameters

filename	The name of the g2o file\
is3D	indicates if the file describes a 2D or 3D problem
kernelFunctionType	whether to wrap the noise model in a robust kernel

Returns

graph and initial values

template<class MATRIX >

const MATRIX::ConstRowXpr gtsam::row	(	const MATRIX &	A,
		size_t	j
	)

Extracts a row view from a matrix that avoids a copy.

Parameters

A	matrix to extract row from
j	index of the row

Returns

a const view of the matrix

GTSAM_EXPORT std::pair< Matrix3, Vector3 > gtsam::RQ

(

const Matrix3 &

A

)

[RQ] receives a 3 by 3 matrix and returns an upper triangular matrix R and 3 rotation angles corresponding to the rotation matrix Q=Qz'*Qy'*Qx' such that A = R*Q = R*Qz'*Qy'*Qx'.

When A is a rotation matrix, R will be the identity and Q is a yaw-pitch-roll decomposition of A. The implementation uses Givens rotations and is based on Hartley-Zisserman.

Parameters

A	3 by 3 matrix A=RQ

Returns

an upper triangular matrix R

a vector [thetax, thetay, thetaz] in radians.

void gtsam::scal ( double  alpha, Vector &  x  )

inline

BLAS Level 1 scal: x <- alpha*x.

Deprecated:

: use operators instead

Matrix3 gtsam::skewSymmetric ( double  wx, double  wy, double  wz  )

inline

skew symmetric matrix returns this: 0 -wz wy wz 0 -wx -wy wx 0

Parameters

wx	3 dimensional vector
wy
wz

Returns

a 3*3 skew symmetric matrix

GTSAM_EXPORT Matrix gtsam::stack	(	size_t	nrMatrices,
			...
	)

create a matrix by stacking other matrices Given a set of matrices: A1, A2, A3...

Parameters

...	pointers to matrices to be stacked

Returns

combined matrix [A1; A2; A3]

GTSAM_EXPORT Vector gtsam::steepestDescent	(	const Matrix &	A,
		const Vector &	b,
		const Vector &	x,
		const ConjugateGradientParameters &	parameters
	)

convenience calls using matrices, will create System class internally:

Method of steepest gradients, Matrix version

template<class MATRIX >

Eigen::Block<const MATRIX> gtsam::sub	(	const MATRIX &	A,
		size_t	i1,
		size_t	i2,
		size_t	j1,
		size_t	j2
	)

extract submatrix, slice semantics, i.e.

range = [i1,i2[ excluding i2

Parameters

A	matrix
i1	first row index
i2	last row index + 1
j1	first col index
j2	last col index + 1

Returns

submatrix A(i1:i2-1,j1:j2-1)

GTSAM_EXPORT void gtsam::svd	(	const Matrix &	A,
		Matrix &	U,
		Vector &	S,
		Matrix &	V
	)

SVD computes economy SVD A=U*S*V'.

Parameters

A	an m*n matrix
U	output argument: rotation matrix
S	output argument: sorted vector of singular values
V	output argument: rotation matrix if m > n then U*S*V' = (m*n)*(n*n)*(n*n) if m < n then U*S*V' = (m*m)*(m*m)*(m*n) Careful! The dimensions above reflect V', not V, which is n*m if m<n. U is a basis in R^m, V is a basis in R^n You can just pass empty matrices U,V, and vector S, they will be re-allocated.

Key gtsam::symbol ( unsigned char  c, std::uint64_t  j  )

inline

Create a symbol key from a character and index, i.e.

x5.

unsigned char gtsam::symbolChr ( Key  key )

inline

Return the character portion of a symbol key.

std::uint64_t gtsam::symbolIndex ( Key  key )

inline

Return the index portion of a symbol key.

template<class T , class P >

P gtsam::transform_point	(	const T &	trans,
		const P &	global,
		boost::optional< Matrix & >	Dtrans,
		boost::optional< Matrix & >	Dglobal
	)

Transform function that must be specialized specific domains.

Template Parameters

T	is a Transform type
P	is a point type

GTSAM_EXPORT Point3 gtsam::triangulateDLT	(	const std::vector< Matrix34 > &	projection_matrices,
		const std::vector< Point2 > &	measurements,
		double	rank_tol = 1e-9
	)

DLT triangulation: See Hartley and Zisserman, 2nd Ed., page 312.

Parameters

projection_matrices	Projection matrices (K*P^-1)
measurements	2D measurements
rank_tol	SVD rank tolerance

Returns

Triangulated Point3

GTSAM_EXPORT Vector4 gtsam::triangulateHomogeneousDLT	(	const std::vector< Matrix34 > &	projection_matrices,
		const std::vector< Point2 > &	measurements,
		double	rank_tol = 1e-9
	)

DLT triangulation: See Hartley and Zisserman, 2nd Ed., page 312.

Parameters

projection_matrices	Projection matrices (K*P^-1)
measurements	2D measurements
rank_tol	SVD rank tolerance

Returns

Triangulated point, in homogeneous coordinates

template<class CALIBRATION >

Point3 gtsam::triangulateNonlinear	(	const std::vector< Pose3 > &	poses,
		boost::shared_ptr< CALIBRATION >	sharedCal,
		const std::vector< Point2 > &	measurements,
		const Point3 &	initialEstimate
	)

Given an initial estimate , refine a point using measurements in several cameras.

Parameters

poses	Camera poses
sharedCal	shared pointer to single calibration object
measurements	2D measurements
initialEstimate

Returns

refined Point3

template<class CAMERA >

Point3 gtsam::triangulateNonlinear	(	const std::vector< CAMERA > &	cameras,
		const std::vector< typename CAMERA::Measurement > &	measurements,
		const Point3 &	initialEstimate
	)

Given an initial estimate , refine a point using measurements in several cameras.

Parameters

cameras	pinhole cameras (monocular or stereo)
measurements	2D measurements
initialEstimate

Returns

refined Point3

template<class CALIBRATION >

Point3 gtsam::triangulatePoint3	(	const std::vector< Pose3 > &	poses,
		boost::shared_ptr< CALIBRATION >	sharedCal,
		const std::vector< Point2 > &	measurements,
		double	rank_tol = 1e-9,
		bool	optimize = false
	)

Function to triangulate 3D landmark point from an arbitrary number of poses (at least 2) using the DLT.

The function checks that the resulting point lies in front of all cameras, but has no other checks to verify the quality of the triangulation.

Parameters

poses	A vector of camera poses
sharedCal	shared pointer to single calibration object
measurements	A vector of camera measurements
rank_tol	rank tolerance, default 1e-9
optimize	Flag to turn on nonlinear refinement of triangulation

Returns

Returns a Point3

template<class CAMERA >

Point3 gtsam::triangulatePoint3	(	const std::vector< CAMERA > &	cameras,
		const std::vector< Point2 > &	measurements,
		double	rank_tol = 1e-9,
		bool	optimize = false
	)

Function to triangulate 3D landmark point from an arbitrary number of poses (at least 2) using the DLT.

This function is similar to the one above, except that each camera has its own calibration. The function checks that the resulting point lies in front of all cameras, but has no other checks to verify the quality of the triangulation.

Parameters

cameras	pinhole cameras
measurements	A vector of camera measurements
rank_tol	rank tolerance, default 1e-9
optimize	Flag to turn on nonlinear refinement of triangulation

Returns

Returns a Point3

template<class CALIBRATION >

std::pair<NonlinearFactorGraph, Values> gtsam::triangulationGraph	(	const std::vector< Pose3 > &	poses,
		boost::shared_ptr< CALIBRATION >	sharedCal,
		const std::vector< Point2 > &	measurements,
		Key	landmarkKey,
		const Point3 &	initialEstimate
	)

Create a factor graph with projection factors from poses and one calibration.

Parameters

poses	Camera poses
sharedCal	shared pointer to single calibration object (monocular only!)
measurements	2D measurements
landmarkKey	to refer to landmark
initialEstimate

Returns

graph and initial values

template<class CAMERA >

std::pair<NonlinearFactorGraph, Values> gtsam::triangulationGraph	(	const std::vector< CAMERA > &	cameras,
		const std::vector< typename CAMERA::Measurement > &	measurements,
		Key	landmarkKey,
		const Point3 &	initialEstimate
	)

Create a factor graph with projection factors from pinhole cameras (each camera has a pose and calibration)

Parameters

cameras	pinhole cameras (monocular or stereo)
measurements	2D measurements
landmarkKey	to refer to landmark
initialEstimate

Returns

graph and initial values

GTSAM_EXPORT void gtsam::vector_scale_inplace	(	const Vector &	v,
		Matrix &	A,
		bool	inf_mask = false
	)

scales a matrix row or column by the values in a vector Arguments (Matrix, Vector) scales the columns, (Vector, Matrix) scales the rows

Parameters

inf_mask

when true, will not scale with a NaN or inf value.

template<>

Matrix gtsam::wedge< Pose3 > ( const Vector &  xi )

inline

wedge for Pose3:

Parameters

xi	6-dim twist (omega,v) where omega = 3D angular velocity v = 3D velocity

Returns

xihat, 4*4 element of Lie algebra that can be exponentiated

GTSAM_EXPORT std::list< boost::tuple< Vector, double, double > > gtsam::weighted_eliminate	(	Matrix &	A,
		Vector &	b,
		const Vector &	sigmas
	)

Imperative algorithm for in-place full elimination with weights and constraint handling.

Parameters

A	is a matrix to eliminate
b	is the rhs
sigmas	is a vector of the measurement standard deviation

Returns

list of r vectors, d and sigma

GTSAM_EXPORT std::pair< Vector, double > gtsam::weightedPseudoinverse	(	const Vector &	v,
		const Vector &	weights
	)

Weighted Householder solution vector, a.k.a., the pseudoinverse of the column NOTE: if any sigmas are zero (indicating a constraint) the pseudoinverse will be a selection vector, and the variance will be zero.

Parameters

v	is the first column of the matrix to solve
weights	is a vector of weights/precisions where w=1/(s*s)

Returns

a pair of the pseudoinverse of v and the associated precision/weight

GTSAM_EXPORT bool gtsam::writeBAL	(	const std::string &	filename,
		SfM_data &	data
	)

This function writes a "Bundle Adjustment in the Large" (BAL) file from a SfM_data structure.

Parameters

filename	The name of the BAL file to write
data	SfM structure where the data is stored

Returns

true if the parsing was successful, false otherwise

GTSAM_EXPORT bool gtsam::writeBALfromValues	(	const std::string &	filename,
		const SfM_data &	data,
		Values &	values
	)

This function writes a "Bundle Adjustment in the Large" (BAL) file from a SfM_data structure and a value structure (measurements are the same as the SfM input data, while camera poses and values are read from Values)

Parameters

filename	The name of the BAL file to write
data	SfM structure where the data is stored
values	structure where the graph values are stored (values can be either Pose3 or PinholeCamera<Cal3Bundler> for the cameras, and should be Point3 for the 3D points). Note that the current version assumes that the keys are "x1" for pose 1 (or "c1" for camera 1) and "l1" for landmark 1

Returns

true if the parsing was successful, false otherwise

GTSAM_EXPORT void gtsam::writeG2o	(	const NonlinearFactorGraph &	graph,
		const Values &	estimate,
		const std::string &	filename
	)

This function writes a g2o file from NonlinearFactorGraph and a Values structure.

Parameters

filename	The name of the g2o file to write
graph	NonlinearFactor graph storing the measurements
estimate	Values

template<class MATRIX >

void gtsam::zeroBelowDiagonal	(	MATRIX &	A,
		size_t	cols = 0
	)

Zeros all of the elements below the diagonal of a matrix, in place.

Parameters

A	is a matrix, to be modified in place
cols	is the number of columns to zero, use zero for all columns