Inheritance diagram for gtsam::BatchFixedLagSmoother:

Public Member Functions
	BatchFixedLagSmoother (double smootherLag=0.0, const LevenbergMarquardtParams &parameters=LevenbergMarquardtParams(), bool enforceConsistency=true)
	default constructor

virtual	~BatchFixedLagSmoother ()
	destructor

virtual void	print (const std::string &s="BatchFixedLagSmoother:\n", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const
	Print the factor for debugging and testing (implementing Testable)

virtual bool	equals (const FixedLagSmoother &rhs, double tol=1e-9) const
	Check if two IncrementalFixedLagSmoother Objects are equal.

Result	update (const NonlinearFactorGraph &newFactors=NonlinearFactorGraph(), const Values &newTheta=Values(), const KeyTimestampMap &timestamps=KeyTimestampMap(), const FastVector< size_t > &factorsToRemove=FastVector< size_t >())
	Add new factors, updating the solution and relinearizing as needed. More...

Values	calculateEstimate () const
	Compute an estimate from the incomplete linear delta computed during the last update. More...

template<class VALUE >
VALUE	calculateEstimate (Key key) const
	Compute an estimate for a single variable using its incomplete linear delta computed during the last update. More...

const LevenbergMarquardtParams &	params () const
	read the current set of optimizer parameters

LevenbergMarquardtParams &	params ()
	update the current set of optimizer parameters

const NonlinearFactorGraph &	getFactors () const
	Access the current set of factors.

const Values &	getLinearizationPoint () const
	Access the current linearization point.

const Ordering &	getOrdering () const
	Access the current ordering.

const VectorValues &	getDelta () const
	Access the current set of deltas to the linearization point.

Matrix	marginalCovariance (Key key) const
	Calculate marginal covariance on given variable.

Static Public Member Functions
static GaussianFactorGraph	CalculateMarginalFactors (const GaussianFactorGraph &graph, const KeyVector &keys, const GaussianFactorGraph::Eliminate &eliminateFunction=EliminatePreferCholesky)
	Marginalize specific keys from a linear graph. More...

static NonlinearFactorGraph	CalculateMarginalFactors (const NonlinearFactorGraph &graph, const Values &theta, const KeyVector &keys, const GaussianFactorGraph::Eliminate &eliminateFunction=EliminatePreferCholesky)
	Marginalize specific keys from a nonlinear graph, wrap in LinearContainers.

Public Types
typedef boost::shared_ptr< BatchFixedLagSmoother >	shared_ptr
	Typedef for a shared pointer to an Incremental Fixed-Lag Smoother.

Protected Types
typedef std::map< Key, std::set< Key > >	FactorIndex
	A typedef defining an Key-Factor mapping.

Protected Member Functions
void	insertFactors (const NonlinearFactorGraph &newFactors)
	Augment the list of factors with a set of new factors.

void	removeFactors (const std::set< size_t > &deleteFactors)
	Remove factors from the list of factors by slot index.

void	eraseKeys (const KeyVector &keys)
	Erase any keys associated with timestamps before the provided time.

void	reorder (const KeyVector &marginalizeKeys=KeyVector())
	Use colamd to update into an efficient ordering.

Result	optimize ()
	Optimize the current graph using a modified version of L-M.

void	marginalize (const KeyVector &marginalizableKeys)
	Marginalize out selected variables.

Protected Attributes
LevenbergMarquardtParams	parameters_
	The L-M optimization parameters.

bool	enforceConsistency_
	A flag indicating if the optimizer should enforce probabilistic consistency by maintaining the linearization point of all variables involved in linearized/marginal factors at the edge of the smoothing window. More...

NonlinearFactorGraph	factors_
	The nonlinear factors.

Values	theta_
	The current linearization point.

Values	linearKeys_
	The set of keys involved in current linear factors. More...

Ordering	ordering_
	The current ordering.

VectorValues	delta_
	The current set of linear deltas.

std::queue< size_t >	availableSlots_
	The set of available factor graph slots. More...

FactorIndex	factorIndex_
	A cross-reference structure to allow efficient factor lookups by key.

Member Function Documentation

◆ calculateEstimate() [1/2]

Values gtsam::BatchFixedLagSmoother::calculateEstimate ( ) const

inlinevirtual

Compute an estimate from the incomplete linear delta computed during the last update.

This delta is incomplete because it was not updated below wildfire_threshold. If only a single variable is needed, it is faster to call calculateEstimate(const KEY&).

Implements gtsam::FixedLagSmoother.

◆ calculateEstimate() [2/2]

template<class VALUE >

VALUE gtsam::BatchFixedLagSmoother::calculateEstimate ( Key key ) const

inline

Compute an estimate for a single variable using its incomplete linear delta computed during the last update.

This is faster than calling the no-argument version of calculateEstimate, which operates on all variables.

Parameters

key

Returns

◆ CalculateMarginalFactors()

GaussianFactorGraph gtsam::BatchFixedLagSmoother::CalculateMarginalFactors	(	const GaussianFactorGraph &	graph,
		const KeyVector &	keys,
		const GaussianFactorGraph::Eliminate &	eliminateFunction = `EliminatePreferCholesky`
	)

static

Marginalize specific keys from a linear graph.

Does not check whether keys actually exist in graph. In that case will fail somewhere deep within elimination

◆ update()

FixedLagSmoother::Result gtsam::BatchFixedLagSmoother::update	(	const NonlinearFactorGraph &	newFactors = `NonlinearFactorGraph()`,
		const Values &	newTheta = `Values()`,
		const KeyTimestampMap &	timestamps = `KeyTimestampMap()`,
		const FastVector< size_t > &	factorsToRemove = `FastVector<size_t>()`
	)

virtual

Add new factors, updating the solution and relinearizing as needed.

Implements gtsam::FixedLagSmoother.

Member Data Documentation

◆ availableSlots_

std::queue<size_t> gtsam::BatchFixedLagSmoother::availableSlots_

protected

The set of available factor graph slots.

These occur because we are constantly deleting factors, leaving holes.

◆ enforceConsistency_

bool gtsam::BatchFixedLagSmoother::enforceConsistency_

protected

A flag indicating if the optimizer should enforce probabilistic consistency by maintaining the linearization point of all variables involved in linearized/marginal factors at the edge of the smoothing window.

This idea is from ??? TODO: Look up paper reference

◆ linearKeys_

Values gtsam::BatchFixedLagSmoother::linearKeys_

protected

The set of keys involved in current linear factors.

These keys should not be relinearized.

The documentation for this class was generated from the following files:

/Users/dellaert/git/gtsam/gtsam_unstable/nonlinear/BatchFixedLagSmoother.h
/Users/dellaert/git/gtsam/gtsam_unstable/gtsam_unstable.h
/Users/dellaert/git/gtsam/gtsam_unstable/nonlinear/BatchFixedLagSmoother.cpp

Public Member Functions

Static Public Member Functions

Public Types

Protected Types

Protected Member Functions

Protected Attributes

Member Function Documentation

◆ calculateEstimate() [1/2]

◆ calculateEstimate() [2/2]

◆ CalculateMarginalFactors()

◆ update()

Member Data Documentation

◆ availableSlots_

◆ enforceConsistency_

◆ linearKeys_