Classes
class	Anchor2d_Node
class	Anchor3d_Node
class	Cholesky
class	CholeskyImpl
class	CholeskyImplCSparse
class	ChowLiuTree
class	ChowLiuTreeInfo
class	ChowLiuTreeNode
class	Covariance
class	CovarianceCache
class	Covariances
struct	DeleteOnReturn
class	Depthmono_Factor
class	DepthmonoCamera
class	DepthmonoMeasurement
class	Element
class	Factor
class	FactorT
class	Function
class	GLC_Factor
class	GLC_Reparam
class	GLC_RootShift
class	Graph
class	Information
class	Jacobian
class	MI
class	Monocular_Factor
class	MonocularCamera
class	MonocularMeasurement
class	Node
class	NodeT
class	Noise
class	OptimizationInterface
class	Optimizer
class	OrderedSparseMatrix
class	Point2d
class	Point2d_Factor
class	Point3d
class	Point3dh
class	Point3dT_Node
class	Pose2d
class	Pose2d_Factor
class	Pose2d_Point2d_Factor
class	Pose2d_Pose2d_Factor
class	Pose3d
class	Pose3d_Factor
class	Pose3d_Point3d_Factor
class	Pose3d_Pose3d_Factor
class	Properties
class	Rot3d
class	Slam
class	SparseMatrix
class	SparseSystem
class	SparseVector
class	SparseVectorIter
class	SqrtInformation
class	Stereo_Factor
class	StereoCamera
class	StereoMeasurement
class	Term
class	Timing
class	UpdateStats
Typedefs
typedef double(*	cost_func_t )(double)
typedef std::vector< std::pair < int, int > >	entry_list_t
typedef std::vector < std::vector< int > >	index_lists_t
typedef NodeT< Point2d >	Point2d_Node
typedef Point3dT_Node< Point3d >	Point3d_Node
typedef Point3dT_Node< Point3dh >	Point3dh_Node
typedef NodeT< Pose2d >	Pose2d_Node
typedef NodeT< Pose3d >	Pose3d_Node
typedef SparseVector *	SparseVector_p
typedef std::list< Term >	Terms
typedef std::tr1::unordered_map< int, double >	umap
typedef Eigen::Matrix< double, 6, 1 >	Vector6d
Enumerations
enum	Method { GAUSS_NEWTON, LEVENBERG_MARQUARDT, DOG_LEG }
enum	Selector { LINPOINT, ESTIMATE }
Functions
double	cost_blake_zisserman (double d, double e)
double	cost_cauchy (double d, double b=1.)
double	cost_corrupted_gaussian (double d, double w, double a)
double	cost_huber (double d, double b)
double	cost_l1 (double d, double b=0.5)
double	cost_pseudo_huber (double d, double b)
double	cost_squared (double d)
std::list< Eigen::MatrixXd >	cov_marginal (const SparseMatrix &R, CovarianceCache &cache, const index_lists_t &index_lists, bool debug=false, int step=-1)
std::list< double >	cov_marginal (const SparseMatrix &R, CovarianceCache &cache, const entry_list_t &entry_list)
double	deg_to_rad (double d)
Eigen::MatrixXd	exmap_jacobian (const std::vector< Node * > &nodes)
Eigen::MatrixXd	eye (int num)
const SparseVector &	get_row (const SparseMatrix &R, CovarianceCache &cache, int i)
void	givens (const double a, const double b, double &c, double &s)
MatrixXd	glc_cholcov (MatrixXd A, double eps=numeric_limits< float >::epsilon())
std::vector< isam::Node * >	glc_elim_clique_nodes (Node *node)
std::vector< Factor * >	glc_elim_factors (Node *node)
Factor *	glc_factor (const MatrixXd &L, const vector< Node * > &clique_nodes, GLC_Reparam *rp)
MatrixXd	glc_get_weighted_jacobian (isam::Factor *f)
vector< Factor * >	glc_intra_clique_factors (vector< Node * > clique_nodes, Node *node)
vector< Factor * >	glc_lift_factors (const MatrixXd &L, const vector< Node * > &clique_nodes, bool sparse, GLC_Reparam *rp)
std::vector< Factor * >	glc_remove_node (Slam &slam, Node node, bool sparse=false, GLC_Reparam rp=NULL)
MatrixXd	glc_target_info (Node node, vector< Node > &clique_nodes, vector< Factor * > &ic_factors)
Eigen::MatrixXd	matrix_of_sparseMatrix (const SparseMatrix &s)
MatrixXd	matslice (const MatrixXd A, vector< int > ii, vector< int > jj)
bool	mi_sort (MI &first, MI &second)
Eigen::VectorXd	mul_SparseMatrixTrans_Vector (const SparseMatrix &lhs, const Eigen::VectorXd &rhs)
VectorXd	mul_SparseMatrixTrans_Vector (const SparseMatrix &lhs, const VectorXd &rhs)
std::string	noise_to_string (const Noise &noise)
Eigen::MatrixXd	numericalDiff (Function &func)
const Eigen::VectorXd	operator* (const SparseMatrix &lhs, const Eigen::VectorXd &rhs)
const VectorXd	operator* (const SparseMatrix &lhs, const VectorXd &rhs)
template<typename T >
T	pinv (const T &a, double eps=std::numeric_limits< typename T::Scalar >::epsilon())
template<typename T >
T	posdef_pinv (const T &a, double eps=std::numeric_limits< typename T::Scalar >::epsilon())
void	prepare (const SparseMatrix &R, CovarianceCache &cache)
double	rad_to_deg (double r)
double	recover (const SparseMatrix &R, CovarianceCache &cache, int n, int i, int l)
SparseMatrix	sparseMatrix_of_matrix (const Eigen::MatrixXd &m)
SparseMatrix	sparseMatrix_of_matrix (const MatrixXd &m)
double	standardRad (double t)
double	sum_j (const SparseMatrix &R, CovarianceCache &cache, int n, int l, int i)
double	tic ()
double	tic (std::string id)
double	tic (string id)
double	tictoc (std::string id)
double	tictoc (string id)
void	tictoc_print ()
double	toc (double t0)
double	toc (std::string id)
double	toc (string id)
Variables
const double	epsilon = 0.0001
const double	HALFPI = PI/2.0
const int	INITIAL_ENTRIES = 50
const int	MIN_NUM_COLS = 10
const int	MIN_NUM_ROWS = 10
const double	NUMERICAL_ZERO = 1e-12
const double	PI = 3.14159265358979323846
Timing	timing
const double	TWOPI = 2.0*PI

Typedef Documentation

typedef double(* isam::cost_func_t)(double)

Definition at line 34 of file Jacobian.h.

typedef std::vector< std::pair<int, int> > isam::entry_list_t

Definition at line 74 of file covariance.h.

typedef std::vector< std::vector<int> > isam::index_lists_t

Definition at line 73 of file covariance.h.

typedef NodeT<Point2d> isam::Point2d_Node

Definition at line 48 of file slam2d.h.

typedef Point3dT_Node<Point3d> isam::Point3d_Node

Definition at line 55 of file slam3d.h.

typedef Point3dT_Node<Point3dh> isam::Point3dh_Node

Definition at line 56 of file slam3d.h.

typedef NodeT< Pose2d > isam::Pose2d_Node

Definition at line 40 of file Anchor.h.

typedef NodeT< Pose3d > isam::Pose3d_Node

Definition at line 41 of file Anchor.h.

typedef SparseVector* isam::SparseVector_p

Definition at line 37 of file SparseMatrix.h.

typedef std::list<Term> isam::Terms

Definition at line 70 of file Jacobian.h.

typedef std::tr1::unordered_map<int, double> isam::umap

Definition at line 49 of file covariance.h.

typedef Eigen::Matrix< double, 6, 1> isam::Vector6d

Definition at line 67 of file Pose3d.h.

Enumeration Type Documentation

enum isam::Method

Enumerator:

GAUSS_NEWTON
LEVENBERG_MARQUARDT
DOG_LEG

Definition at line 32 of file Properties.h.

enum isam::Selector

Enumerator:

LINPOINT
ESTIMATE

Definition at line 41 of file Node.h.

Function Documentation

double isam::cost_blake_zisserman	(	double	d,
		double	e
	)		`[inline]`

Robust Blake-Zisserman cost function.

Parameters:

d	Unmodified cost/distance.
e	Approximate crossover between inliers and outliers: d^2 = -log(e).

Definition at line 60 of file robust.h.

double isam::cost_cauchy	(	double	d,
		double	b = `1.`
	)		`[inline]`

Robust Cauchy cost function.

Parameters:

d	Unmodified cost/distance.
b	Determines for which range of d the function closely approximates the squared error function.

Definition at line 82 of file robust.h.

double isam::cost_corrupted_gaussian	(	double	d,
		double	w,
		double	a
	)		`[inline]`

Robust cost function using corrupted Gaussian.

Parameters:

d	Unmodified cost/distance.
w	Ratio of standard deviations of the outliers to the inliers.
a	Expected fraction of inliers.

Definition at line 70 of file robust.h.

double isam::cost_huber	(	double	d,
		double	b
	)		`[inline]`

Robust Huber cost function.

Parameters:

d	Unmodified cost/distance.
b	Approximately the outlier threshold.

Definition at line 101 of file robust.h.

double isam::cost_l1	(	double	d,
		double	b = `0.5`
	)		`[inline]`

Robust L1 cost function, the sum of absolute errors.

Parameters:

d	Unmodified cost/distance.
b	?

Definition at line 92 of file robust.h.

double isam::cost_pseudo_huber	(	double	d,
		double	b
	)		`[inline]`

Robust Pseudo-Huber cost function.

Parameters:

d	Unmodified cost/distance.
b	?

Definition at line 115 of file robust.h.

double isam::cost_squared ( double d ) [inline]

Standard squared cost function.

Parameters:

d	Unmodified cost/distance.

Definition at line 51 of file robust.h.

list< MatrixXd > isam::cov_marginal	(	const SparseMatrix &	R,
		CovarianceCache &	cache,
		const index_lists_t &	index_lists,
		bool	debug = `false`,
		int	step = `-1`
	)

Takes a list of variable indices, and returns the marginal covariance matrix.

Parameters:

R	Sparse factor matrix.
cache	Covariance cache object.
index_lists	List of lists of indices; a block will be recovered for each list.
debug	Optional parameter to print timing information.
step	Optional parameter to print statistics (default=-1, no stats printed).

Returns:: List of dense marginal covariance matrices.

Definition at line 119 of file covariance.cpp.

list< double > isam::cov_marginal	(	const SparseMatrix &	R,
		CovarianceCache &	cache,
		const entry_list_t &	entry_list
	)

Takes a list of pairs of integers and returns the corresonding entries of the covariance matrix.

Parameters:

R	Sparse factor matrix.
cache	Covariance cache object.
entry_lists	List of pairs of integers refering to covariance matrix entries.
debug	Optional parameter to print timing information.
step	Optional parameter to print statistics (default=-1, no stats printed).

Returns:: List of doubles corresponding to the requested covariance matrix entries.

Definition at line 170 of file covariance.cpp.

double isam::deg_to_rad ( double d ) [inline]

Definition at line 110 of file util.h.

Eigen::MatrixXd isam::exmap_jacobian ( const std::vector< Node * > & nodes )

Eigen::MatrixXd isam::eye ( int num )

Return identity matrix.

Definition at line 110 of file util.cpp.

const SparseVector& isam::get_row	(	const SparseMatrix &	R,
		CovarianceCache &	cache,
		int	i
	)

Definition at line 64 of file covariance.cpp.

void isam::givens	(	const double	a,
		const double	b,
		double &	c,
		double &	s
	)

Calculate Givens rotation so that a specific entry becomes 0.

Parameters:

a	Diagonal entry from above.
b	Entry to be zeroed out.
c	Resulting cosine part.
s	Resulting sine part.

Definition at line 114 of file util.cpp.

MatrixXd isam::glc_cholcov	(	MatrixXd	A,
		double	eps = `numeric_limits<float>::epsilon()`
	)

Definition at line 98 of file glc.cpp.

std::vector<isam::Node*> isam::glc_elim_clique_nodes ( Node * node )

Definition at line 174 of file glc.cpp.

vector< Factor * > isam::glc_elim_factors ( Node * node )

Find the factors that will be eliminated and replaced when node is removed IMPORTANT: must be called before node is removed using glc_remove_node()

Parameters:

node	Pointer to node to be removed.

Returns:: vector of factors that will be eliminated.

Definition at line 468 of file glc.cpp.

Factor* isam::glc_factor	(	const MatrixXd &	L,
		const vector< Node * > &	clique_nodes,
		GLC_Reparam *	rp
	)

Definition at line 330 of file glc.cpp.

MatrixXd isam::glc_get_weighted_jacobian ( isam::Factor * f )

Definition at line 149 of file glc.cpp.

vector<Factor*> isam::glc_intra_clique_factors	(	vector< Node * >	clique_nodes,
		Node *	node
	)

Definition at line 205 of file glc.cpp.

vector<Factor*> isam::glc_lift_factors	(	const MatrixXd &	L,
		const vector< Node * > &	clique_nodes,
		bool	sparse,
		GLC_Reparam *	rp
	)

Definition at line 416 of file glc.cpp.

vector< Factor * > isam::glc_remove_node	(	Slam &	slam,
		Node *	node,
		bool	sparse = `false`,
		GLC_Reparam *	rp = `NULL`
	)

Removes a node (variable) and all factors within in its elimination clique replaces these factors with either a dense GLC factor or a set of sparse GLC factors Note that the node itself and the removed factors are not deallocated.

We usually use GLC with USE_QUATERNIONS turned off in Rot3d.h. This avoids having to account for the state transformation between euler and angle axis representations. This is currently done numerically (see exmap_jacobian()) and produces slightly higher KLD in the GLC results.

Parameters:

node	Pointer to node to be removed.
sparse	Bool flag if new factors should be sparse approximate or dense
rp	functor to reparamertize variables before linearization

Returns:: vector of new factors.

Definition at line 482 of file glc.cpp.

MatrixXd isam::glc_target_info	(	Node *	node,
		vector< Node * > &	clique_nodes,
		vector< Factor * > &	ic_factors
	)

Definition at line 246 of file glc.cpp.

MatrixXd isam::matrix_of_sparseMatrix ( const SparseMatrix & s )

Definition at line 424 of file SparseMatrix.cpp.

MatrixXd isam::matslice	(	const MatrixXd	A,
		vector< int >	ii,
		vector< int >	jj
	)

Definition at line 37 of file ChowLiuTree.cpp.

bool isam::mi_sort	(	MI &	first,
		MI &	second
	)

Definition at line 135 of file ChowLiuTree.cpp.

Eigen::VectorXd isam::mul_SparseMatrixTrans_Vector	(	const SparseMatrix &	lhs,
		const Eigen::VectorXd &	rhs
	)

VectorXd isam::mul_SparseMatrixTrans_Vector	(	const SparseMatrix &	lhs,
		const VectorXd &	rhs
	)

Definition at line 400 of file SparseMatrix.cpp.

std::string isam::noise_to_string ( const Noise & noise ) [inline]

Convert upper triangular square root information matrix to string.

Parameters:

sqrtinf Upper triangular square matrix.

Definition at line 168 of file Factor.h.

MatrixXd isam::numericalDiff ( Function & func )

Takes a general vector valued function and returns the Jacobian at the linearization point given by x0.

Parameters:

func	Function object with evaluation function that takes and returns vectors.

Returns:: Matrix containing the Jacobian of func, with dim(y) rows and dim(x) columns, where y=func(x).

Definition at line 41 of file numericalDiff.cpp.

const Eigen::VectorXd isam::operator*	(	const SparseMatrix &	lhs,
		const Eigen::VectorXd &	rhs
	)

const VectorXd isam::operator*	(	const SparseMatrix &	lhs,
		const VectorXd &	rhs
	)

Definition at line 386 of file SparseMatrix.cpp.

template<typename T >

T isam::pinv	(	const T &	a,
		double	eps = `std::numeric_limits<typename T::Scalar>::epsilon()`
	)

Calculate the pseudo inverse of an arbitrary matrix using the SVD a Eigen matrix to invert eps Numerical epsilon to determine zero singular values (defaults to std::numeric_limits::eps)

Returns:: Eigen matrix of same type as a

Definition at line 125 of file util.h.

template<typename T >

T isam::posdef_pinv	(	const T &	a,
		double	eps = `std::numeric_limits<typename T::Scalar>::epsilon()`
	)

Calculate the pseudo inverse of an positive semidefinite matrix using the eigenvalue decomposition a Eigen matrix to invert eps Numerical epsilon to determine zero singular values (defaults to std::numeric_limits::eps)

Returns:: Eigen matrix of same type as a

Definition at line 157 of file util.h.

void isam::prepare	(	const SparseMatrix &	R,
		CovarianceCache &	cache
	)

Definition at line 40 of file covariance.cpp.

double isam::rad_to_deg ( double r ) [inline]

Definition at line 114 of file util.h.

double isam::recover	(	const SparseMatrix &	R,
		CovarianceCache &	cache,
		int	n,
		int	i,
		int	l
	)

Definition at line 94 of file covariance.cpp.

SparseMatrix isam::sparseMatrix_of_matrix ( const Eigen::MatrixXd & m )

SparseMatrix isam::sparseMatrix_of_matrix ( const MatrixXd & m )

Definition at line 414 of file SparseMatrix.cpp.

double isam::standardRad ( double t ) [inline]

Normalize angle to be within the interval [-pi,pi].

Definition at line 101 of file util.h.

double isam::sum_j	(	const SparseMatrix &	R,
		CovarianceCache &	cache,
		int	n,
		int	l,
		int	i
	)		`[inline]`

Definition at line 76 of file covariance.cpp.

double isam::tic ( )

Return current system time in seconds.

Definition at line 79 of file util.cpp.

double isam::tic ( std::string id )

Remember and return system time in seconds.

Parameters:

id	Name of time slot.

double isam::tic ( string id )

Definition at line 85 of file util.cpp.

double isam::tictoc ( std::string id )

Return the accumulated time.

Parameters:

id	Name of time slot.

double isam::tictoc ( string id )

Definition at line 106 of file util.cpp.

void isam::tictoc_print ( )

Print a list of accumulated times and additional statistics for each name used in tic/toc.

Definition at line 102 of file util.cpp.

double isam::toc ( double t0 )

Return difference between current system time and t in seconds.

Parameters:

t0	Start time as returned by tic();

Definition at line 91 of file util.cpp.

double isam::toc ( std::string id )

Return difference between current system time and remembered time in seconds, and add to accumulated time.

Parameters:

id	Name of time slot.

double isam::toc ( string id )

Definition at line 96 of file util.cpp.

Variable Documentation

const double isam::epsilon = 0.0001

Definition at line 115 of file GLCReparam.cpp.

const double isam::HALFPI = PI/2.0

Definition at line 43 of file util.h.

const int isam::INITIAL_ENTRIES = 50

Definition at line 46 of file SparseVector.cpp.

const int isam::MIN_NUM_COLS = 10

Definition at line 44 of file SparseMatrix.cpp.

const int isam::MIN_NUM_ROWS = 10

Definition at line 45 of file SparseMatrix.cpp.

const double isam::NUMERICAL_ZERO = 1e-12

Definition at line 46 of file util.h.

const double isam::PI = 3.14159265358979323846

Definition at line 40 of file util.h.

Timing isam::timing

Definition at line 77 of file util.cpp.

const double isam::TWOPI = 2.0*PI

Definition at line 42 of file util.h.

Classes

Typedefs

Enumerations

Functions

Variables

Typedef Documentation

Enumeration Type Documentation

Function Documentation

Variable Documentation