#include <SmartProjectionRigFactor.h>

Inheritance diagram for gtsam::SmartProjectionRigFactor< CAMERA >:

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Collaboration diagram for gtsam::SmartProjectionRigFactor< CAMERA >:

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Public Types
typedef CameraSet< CAMERA >	Cameras

typedef std::shared_ptr< This >	shared_ptr
	shorthand for a smart pointer to a factor

typedef Eigen::Matrix< double, ZDim, Dim >	MatrixZD

typedef std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > >	FBlocks

typedef KeyVector::iterator	iterator
	Iterator over keys.

typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys.

Public Member Functions
	SmartProjectionRigFactor ()
	Default constructor, only for serialization.

	SmartProjectionRigFactor (const SharedNoiseModel &sharedNoiseModel, const std::shared_ptr< Cameras > &cameraRig, const SmartProjectionParams &params=SmartProjectionParams())

void	add (const MEASUREMENT &measured, const Key &poseKey, const size_t &cameraId=0)

void	add (const MEASUREMENTS &measurements, const KeyVector &poseKeys, const FastVector< size_t > &cameraIds=FastVector< size_t >())

const KeyVector &	nonUniqueKeys () const

const std::shared_ptr< Cameras > &	cameraRig () const
	return the calibration object

const FastVector< size_t > &	cameraIds () const
	return the calibration object

void	print (const std::string &s="", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const override

bool	equals (const NonlinearFactor &p, double tol=1e-9) const override
	equals

Base::Cameras	cameras (const Values &values) const override

double	error (const Values &values) const override

void	computeJacobiansWithTriangulatedPoint (typename Base::FBlocks &Fs, Matrix &E, Vector &b, const Cameras &cameras) const

std::shared_ptr< RegularHessianFactor< DimPose > >	createHessianFactor (const Values &values, const double &lambda=0.0, bool diagonalDamping=false) const
	linearize and return a Hessianfactor that is an approximation of error(p)

std::shared_ptr< GaussianFactor >	linearizeDamped (const Values &values, const double &lambda=0.0) const

std::shared_ptr< GaussianFactor >	linearize (const Values &values) const override
	linearize

bool	decideIfTriangulate (const Cameras &cameras) const
	Check if the new linearization point is the same as the one used for previous triangulation. More...

TriangulationResult	triangulateSafe (const Cameras &cameras) const
	Call gtsam::triangulateSafe iff we need to re-triangulate. More...

bool	triangulateForLinearize (const Cameras &cameras) const
	Possibly re-triangulate before calculating Jacobians. More...

std::shared_ptr< RegularHessianFactor< Base::Dim > >	createHessianFactor (const Cameras &cameras, const double lambda=0.0, bool diagonalDamping=false) const
	Create a Hessianfactor that is an approximation of error(p).

std::shared_ptr< RegularHessianFactor< Dim > >	createHessianFactor (const Cameras &cameras, const Point3 &point, const double lambda=0.0, bool diagonalDamping=false) const
	Linearize to a Hessianfactor.

std::shared_ptr< RegularImplicitSchurFactor< CAMERA > >	createRegularImplicitSchurFactor (const Cameras &cameras, double lambda) const

std::shared_ptr< RegularImplicitSchurFactor< CAMERA > >	createRegularImplicitSchurFactor (const Cameras &cameras, const Point3 &point, double lambda=0.0, bool diagonalDamping=false) const
	Return Jacobians as RegularImplicitSchurFactor with raw access.

std::shared_ptr< JacobianFactorQ< Base::Dim, 2 > >	createJacobianQFactor (const Cameras &cameras, double lambda) const
	Create JacobianFactorQ factor.

std::shared_ptr< JacobianFactorQ< Base::Dim, 2 > >	createJacobianQFactor (const Values &values, double lambda) const
	Create JacobianFactorQ factor, takes values.

std::shared_ptr< JacobianFactorQ< Dim, ZDim > >	createJacobianQFactor (const Cameras &cameras, const Point3 &point, double lambda=0.0, bool diagonalDamping=false) const
	Return Jacobians as JacobianFactorQ.

std::shared_ptr< JacobianFactor >	createJacobianSVDFactor (const Cameras &cameras, double lambda) const
	Different (faster) way to compute a JacobianFactorSVD factor.

std::shared_ptr< JacobianFactor >	createJacobianSVDFactor (const Cameras &cameras, const Point3 &point, double lambda=0.0) const

virtual std::shared_ptr< RegularHessianFactor< Base::Dim > >	linearizeToHessian (const Values &values, double lambda=0.0) const
	Linearize to a Hessianfactor.

virtual std::shared_ptr< RegularImplicitSchurFactor< CAMERA > >	linearizeToImplicit (const Values &values, double lambda=0.0) const
	Linearize to an Implicit Schur factor.

virtual std::shared_ptr< JacobianFactorQ< Base::Dim, 2 > >	linearizeToJacobian (const Values &values, double lambda=0.0) const
	Linearize to a JacobianfactorQ.

std::shared_ptr< GaussianFactor >	linearizeDamped (const Cameras &cameras, const double lambda=0.0) const

std::shared_ptr< GaussianFactor >	linearizeDamped (const Values &values, const double lambda=0.0) const

bool	triangulateAndComputeE (Matrix &E, const Cameras &cameras) const

bool	triangulateAndComputeE (Matrix &E, const Values &values) const

bool	triangulateAndComputeJacobians (typename Base::FBlocks &Fs, Matrix &E, Vector &b, const Values &values) const
	Version that takes values, and creates the point.

bool	triangulateAndComputeJacobiansSVD (typename Base::FBlocks &Fs, Matrix &Enull, Vector &b, const Values &values) const
	takes values

Vector	reprojectionErrorAfterTriangulation (const Values &values) const
	Calculate vector of re-projection errors, before applying noise model.

double	totalReprojectionError (const Cameras &cameras, std::optional< Point3 > externalPoint={}) const

template<class POINT >
double	totalReprojectionError (const Cameras &cameras, const POINT &point) const

TriangulationResult	point () const

TriangulationResult	point (const Values &values) const

bool	isValid () const
	Is result valid?

bool	isDegenerate () const

bool	isPointBehindCamera () const

bool	isOutlier () const

bool	isFarPoint () const

void	add (const Z &measured, const Key &key)

void	add (const ZVector &measurements, const KeyVector &cameraKeys)
	Add a bunch of measurements, together with the camera keys.

template<class SFM_TRACK >
void	add (const SFM_TRACK &trackToAdd)

size_t	dim () const override
	Return the dimension (number of rows!) of the factor.

const ZVector &	measured () const
	Return the 2D measurements (ZDim, in general).

template<class POINT >
Vector	unwhitenedError (const Cameras &cameras, const POINT &point, typename Cameras::FBlocks Fs=nullptr, Matrix E=nullptr) const

template<class POINT , class ... OptArgs, typename = std::enable_if_t<sizeof...(OptArgs)!=0>>
Vector	unwhitenedError (const Cameras &cameras, const POINT &point, OptArgs &&... optArgs) const

virtual void	correctForMissingMeasurements (const Cameras &cameras, Vector &ue, typename Cameras::FBlocks Fs=nullptr, Matrix E=nullptr) const

template<class ... OptArgs>
void	correctForMissingMeasurements (const Cameras &cameras, Vector &ue, OptArgs &&... optArgs) const

template<class POINT >
Vector	whitenedError (const Cameras &cameras, const POINT &point) const

template<class POINT >
void	computeJacobians (FBlocks &Fs, Matrix &E, Vector &b, const Cameras &cameras, const POINT &point) const

template<class POINT >
void	computeJacobiansSVD (FBlocks &Fs, Matrix &Enull, Vector &b, const Cameras &cameras, const POINT &point) const

void	updateAugmentedHessian (const Cameras &cameras, const Point3 &point, const double lambda, bool diagonalDamping, SymmetricBlockMatrix &augmentedHessian, const KeyVector allKeys) const

void	whitenJacobians (FBlocks &F, Matrix &E, Vector &b) const
	Whiten the Jacobians computed by computeJacobians using noiseModel_.

Pose3	body_P_sensor () const

Testable
bool	equals (const This &other, double tol=1e-9) const
	check equality

virtual void	printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print only keys

Standard Interface
double	error (const HybridValues &c) const override

virtual bool	active (const Values &) const

virtual shared_ptr	clone () const

virtual shared_ptr	rekey (const std::map< Key, Key > &rekey_mapping) const

virtual shared_ptr	rekey (const KeyVector &new_keys) const

virtual bool	sendable () const

Standard Interface
bool	empty () const
	Whether the factor is empty (involves zero variables).

Key	front () const
	First key.

Key	back () const
	Last key.

const_iterator	find (Key key) const
	find

const KeyVector &	keys () const
	Access the factor's involved variable keys.

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

Advanced Interface
KeyVector &	keys ()

iterator	begin ()

iterator	end ()

Static Public Member Functions
static Matrix	PointCov (const Matrix &E)
	Computes Point Covariance P from the "point Jacobian" E.

static void	FillDiagonalF (const FBlocks &Fs, Matrix &F)
	Create BIG block-diagonal matrix F from Fblocks.

Public Attributes
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef CAMERA	Camera

Static Public Attributes
static const int	Dim = traits<CAMERA>::dimension
	Camera dimension.

Static Protected Member Functions
Standard Constructors
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

template<typename ITERATOR >
static Factor	FromIterators (ITERATOR first, ITERATOR last)

Protected Attributes
KeyVector	nonUniqueKeys_
	vector of keys (one for each observation) with potentially repeated keys

std::shared_ptr< typename Base::Cameras >	cameraRig_
	cameras in the rig (fixed poses wrt body and intrinsics, for each camera)

FastVector< size_t >	cameraIds_

SharedIsotropic	noiseModel_

ZVector	measured_

std::optional< Pose3 >	body_P_sensor_
	Pose of the camera in the body frame.

FBlocks	Fs

KeyVector	keys_
	The keys involved in this factor.

Parameters
SmartProjectionParams	params_

Caching triangulation
TriangulationResult	result_
	result from triangulateSafe

std::vector< Pose3, Eigen::aligned_allocator< Pose3 > >	cameraPosesTriangulation_
	current triangulation poses

Detailed Description

template<class CAMERA>
class gtsam::SmartProjectionRigFactor< CAMERA >

If you are using the factor, please cite: L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating conditionally independent sets in factor graphs: a unifying perspective based on smart factors, Int. Conf. on Robotics and Automation (ICRA), 2014. This factor assumes that camera calibration is fixed (but each measurement can be taken by a different camera in the rig, hence can have a different extrinsic and intrinsic calibration). The factor only constrains poses (variable dimension is 6 for each pose). This factor requires that values contains the involved poses (Pose3). If all measurements share the same calibration (i.e., are from the same camera), use SmartProjectionPoseFactor instead! If the calibration should be optimized, as well, use SmartProjectionFactor instead!

Constructor & Destructor Documentation

◆ SmartProjectionRigFactor()

template<class CAMERA >

gtsam::SmartProjectionRigFactor< CAMERA >::SmartProjectionRigFactor	(	const SharedNoiseModel &	sharedNoiseModel,
		const std::shared_ptr< Cameras > &	cameraRig,
		const SmartProjectionParams &	params = `SmartProjectionParams()`
	)

inline

Constructor

Parameters

sharedNoiseModel	isotropic noise model for the 2D feature measurements
cameraRig	set of cameras (fixed poses wrt body and intrinsics) in the camera rig
params	parameters for the smart projection factors

Member Function Documentation

◆ active()

virtual bool gtsam::NonlinearFactor::active ( const Values & ) const

inlinevirtualinherited

Checks whether a factor should be used based on a set of values. This is primarily used to implement inequality constraints that require a variable active set. For all others, the default implementation returning true solves this problem.

In an inequality/bounding constraint, this active() returns true when the constraint is NOT fulfilled.

Returns: true if the constraint is active

Reimplemented in gtsam::BoundingConstraint2< VALUE1, VALUE2 >, gtsam::AntiFactor, and gtsam::BoundingConstraint1< VALUE >.

◆ add() [1/4]

template<class CAMERA >

void gtsam::SmartProjectionRigFactor< CAMERA >::add	(	const MEASUREMENT &	measured,
		const Key &	poseKey,
		const size_t &	cameraId = `0`
	)

inline

add a new measurement, corresponding to an observation from pose "poseKey" and taken from the camera in the rig identified by "cameraId"

Parameters

measured	2-dimensional location of the projection of a single landmark in a single view (the measurement)
poseKey	key corresponding to the body pose of the camera taking the measurement
cameraId	ID of the camera in the rig taking the measurement (default 0)

◆ add() [2/4]

template<class CAMERA>

void gtsam::SmartFactorBase< CAMERA >::add	(	const Z &	measured,
		const Key &	key
	)

inlineinherited

Add a new measurement and pose/camera key.

Parameters

measured	is the 2m dimensional projection of a single landmark
key	is the index corresponding to the camera observing the landmark

◆ add() [3/4]

template<class CAMERA >

void gtsam::SmartProjectionRigFactor< CAMERA >::add	(	const MEASUREMENTS &	measurements,
		const KeyVector &	poseKeys,
		const FastVector< size_t > &	cameraIds = `FastVector<size_t>()`
	)

inline

Variant of the previous "add" function in which we include multiple measurements

Parameters

measurements	vector of the 2m dimensional location of the projection of a single landmark in the m views (the measurements)
poseKeys	keys corresponding to the body poses of the cameras taking the measurements
cameraIds	IDs of the cameras in the rig taking each measurement (same order as the measurements)

◆ add() [4/4]

template<class CAMERA>

template<class SFM_TRACK >

void gtsam::SmartFactorBase< CAMERA >::add ( const SFM_TRACK & trackToAdd )

inlineinherited

Add an entire SfM_track (collection of cameras observing a single point). The noise is assumed to be the same for all measurements.

◆ begin() [1/2]

const_iterator gtsam::Factor::begin ( ) const

inlineinherited

Iterator at beginning of involved variable keys

◆ begin() [2/2]

iterator gtsam::Factor::begin ( )

inlineinherited

Iterator at beginning of involved variable keys

◆ cameras()

template<class CAMERA >

Base::Cameras gtsam::SmartProjectionRigFactor< CAMERA >::cameras ( const Values & values ) const

inlineoverridevirtual

Collect all cameras involved in this factor

Parameters

values Values structure which must contain body poses corresponding to keys involved in this factor

Returns: vector of cameras

Reimplemented from gtsam::SmartFactorBase< CAMERA >.

◆ clone()

virtual shared_ptr gtsam::NonlinearFactor::clone ( ) const

inlinevirtualinherited

Creates a shared_ptr clone of the factor - needs to be specialized to allow for subclasses

By default, throws exception if subclass does not implement the function.

Reimplemented in gtsam::EssentialMatrixFactor4< CALIBRATION >, gtsam::NonlinearEquality2< T >, gtsam::CombinedImuFactor, gtsam::ImuFactor2, gtsam::EssentialMatrixFactor3, gtsam::NonlinearEquality1< VALUE >, gtsam::GeneralSFMFactor2< CALIBRATION >, gtsam::ImuFactor, gtsam::LinearizedHessianFactor, gtsam::PendulumFactorPk1, gtsam::FunctorizedFactor2< R, T1, T2 >, gtsam::MagFactor3, gtsam::Pose3AttitudeFactor, gtsam::SmartRangeFactor, gtsam::NonlinearEquality< VALUE >, gtsam::AHRSFactor, gtsam::EssentialMatrixFactor2, gtsam::ExpressionFactor< T >, gtsam::ExpressionFactor< BearingRange< A1, A2 > >, gtsam::ExpressionFactor< double >, gtsam::MagFactor2, gtsam::PendulumFactorPk, gtsam::GPSFactor2, gtsam::ProjectionFactorRollingShutter, gtsam::RangeFactorWithTransform< A1, A2, T >, gtsam::LinearContainerFactor, gtsam::LinearizedJacobianFactor, gtsam::Rot3AttitudeFactor, gtsam::MagFactor1, gtsam::GenericProjectionFactor< POSE, LANDMARK, CALIBRATION >, gtsam::DiscreteEulerPoincareHelicopter, gtsam::MultiProjectionFactor< POSE, LANDMARK, CALIBRATION >, gtsam::GeneralSFMFactor< CAMERA, LANDMARK >, gtsam::ProjectionFactorPPP< POSE, LANDMARK, CALIBRATION >, gtsam::TransformBtwRobotsUnaryFactorEM< VALUE >, gtsam::GenericStereoFactor< POSE, LANDMARK >, gtsam::TriangulationFactor< CAMERA >, gtsam::RotateDirectionsFactor, gtsam::PendulumFactor2, gtsam::ReferenceFrameFactor< POINT, TRANSFORM >, gtsam::FunctorizedFactor< R, T >, gtsam::PartialPriorFactor< VALUE >, gtsam::FunctorizedFactor< Vector, ParameterMatrix< M > >, gtsam::FunctorizedFactor< double, BASIS::Parameters >, gtsam::FunctorizedFactor< double, Vector >, gtsam::FunctorizedFactor< T, ParameterMatrix< traits< T >::dimension > >, gtsam::FunctorizedFactor< double, ParameterMatrix< P > >, gtsam::PartialPriorFactor< PoseRTV >, gtsam::MagPoseFactor< POSE >, gtsam::ProjectionFactorPPPC< POSE, LANDMARK, CALIBRATION >, gtsam::TransformBtwRobotsUnaryFactor< VALUE >, gtsam::BetweenFactor< VALUE >, gtsam::EssentialMatrixFactor, gtsam::VelocityConstraint, gtsam::PriorFactor< VALUE >, gtsam::GPSFactor, gtsam::PoseToPointFactor< POSE, POINT >, gtsam::BarometricFactor, gtsam::EssentialMatrixConstraint, gtsam::DummyFactor, gtsam::PoseBetweenFactor< POSE >, gtsam::BearingRangeFactor< A1, A2, B, R >, gtsam::PosePriorFactor< POSE >, gtsam::MagFactor, gtsam::PoseTranslationPrior< POSE >, gtsam::PoseRotationPrior< POSE >, gtsam::FullIMUFactor< POSE >, gtsam::AntiFactor, gtsam::RangeFactor< A1, A2, T >, gtsam::IMUFactor< POSE >, gtsam::RelativeElevationFactor, gtsam::PendulumFactor1, gtsam::Reconstruction, gtsam::RotateFactor, and gtsam::VelocityConstraint3.

◆ computeJacobians()

template<class CAMERA>

template<class POINT >

void gtsam::SmartFactorBase< CAMERA >::computeJacobians	(	FBlocks &	Fs,
		Matrix &	E,
		Vector &	b,
		const Cameras &	cameras,
		const POINT &	point
	)		const

inlineinherited

Compute F, E, and b (called below in both vanilla and SVD versions), where F is a vector of derivatives wrpt the cameras, and E the stacked derivatives with respect to the point. The value of cameras/point are passed as parameters.

◆ computeJacobiansSVD()

template<class CAMERA>

template<class POINT >

void gtsam::SmartFactorBase< CAMERA >::computeJacobiansSVD	(	FBlocks &	Fs,
		Matrix &	Enull,
		Vector &	b,
		const Cameras &	cameras,
		const POINT &	point
	)		const

inlineinherited

SVD version that produces smaller Jacobian matrices by doing an SVD decomposition on E, and returning the left nulkl-space of E. See JacobianFactorSVD for more documentation.

◆ computeJacobiansWithTriangulatedPoint()

template<class CAMERA >

void gtsam::SmartProjectionRigFactor< CAMERA >::computeJacobiansWithTriangulatedPoint	(	typename Base::FBlocks &	Fs,
		Matrix &	E,
		Vector &	b,
		const Cameras &	cameras
	)		const

inline

Compute jacobian F, E and error vector at a given linearization point

Parameters

values Values structure which must contain camera poses corresponding to keys involved in this factor

Returns: Return arguments are the camera jacobians Fs (including the jacobian with respect to both body poses we interpolate from), the point Jacobian E, and the error vector b. Note that the jacobians are computed for a given point.

◆ correctForMissingMeasurements() [1/2]

template<class CAMERA>

virtual void gtsam::SmartFactorBase< CAMERA >::correctForMissingMeasurements	(	const Cameras &	cameras,
		Vector &	ue,
		typename Cameras::FBlocks *	Fs = `nullptr`,
		Matrix *	E = `nullptr`
	)		const

inlinevirtualinherited

This corrects the Jacobians for the case in which some 2D measurement is missing (nan). In practice, this does not do anything in the monocular case, but it is implemented in the stereo version.

Reimplemented in gtsam::SmartStereoProjectionFactor.

◆ correctForMissingMeasurements() [2/2]

template<class CAMERA>

template<class ... OptArgs>

void gtsam::SmartFactorBase< CAMERA >::correctForMissingMeasurements	(	const Cameras &	cameras,
		Vector &	ue,
		OptArgs &&...	optArgs
	)		const

inlineinherited

An overload of correctForMissingMeasurements. This allows end users to provide optional arguments that are l-value references to the matrices and vectors that will be used to store the results instead of pointers.

◆ createJacobianSVDFactor()

template<class CAMERA>

std::shared_ptr<JacobianFactor> gtsam::SmartFactorBase< CAMERA >::createJacobianSVDFactor	(	const Cameras &	cameras,
		const Point3 &	point,
		double	lambda = `0.0`
	)		const

inlineinherited

Return Jacobians as JacobianFactorSVD. TODO(dellaert): lambda is currently ignored

◆ decideIfTriangulate()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::decideIfTriangulate ( const Cameras & cameras ) const

inlineinherited

Check if the new linearization point is the same as the one used for previous triangulation.

Parameters

cameras

Returns: true if we need to re-triangulate.

◆ end() [1/2]

const_iterator gtsam::Factor::end ( ) const

inlineinherited

Iterator at end of involved variable keys

◆ end() [2/2]

iterator gtsam::Factor::end ( )

inlineinherited

Iterator at end of involved variable keys

◆ error() [1/2]

double gtsam::NonlinearFactor::error ( const HybridValues & c ) const

overridevirtualinherited

All factor types need to implement an error function. In factor graphs, this is the negative log-likelihood.

Reimplemented from gtsam::Factor.

◆ error() [2/2]

template<class CAMERA >

double gtsam::SmartProjectionRigFactor< CAMERA >::error ( const Values & values ) const

inlineoverridevirtual

error calculates the error of the factor.

Reimplemented from gtsam::SmartProjectionFactor< CAMERA >.

◆ FromIterators()

template<typename ITERATOR >

static Factor gtsam::Factor::FromIterators	(	ITERATOR	first,
		ITERATOR	last
	)

inlinestaticprotectedinherited

Construct factor from iterator keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ FromKeys()

template<typename CONTAINER >

static Factor gtsam::Factor::FromKeys ( const CONTAINER & keys )

inlinestaticprotectedinherited

Construct factor from container of keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ isDegenerate()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::isDegenerate ( ) const

inlineinherited

return the degenerate state

◆ isFarPoint()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::isFarPoint ( ) const

inlineinherited

return the farPoint state

◆ isOutlier()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::isOutlier ( ) const

inlineinherited

return the outlier state

◆ isPointBehindCamera()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::isPointBehindCamera ( ) const

inlineinherited

return the cheirality status flag

◆ keys()

KeyVector& gtsam::Factor::keys ( )

inlineinherited

Returns: keys involved in this factor

◆ linearizeDamped() [1/3]

template<class CAMERA>

std::shared_ptr<GaussianFactor> gtsam::SmartProjectionFactor< CAMERA >::linearizeDamped	(	const Cameras &	cameras,
		const double	lambda = `0.0`
	)		const

inlineinherited

Linearize to Gaussian Factor

Parameters

values Values structure which must contain camera poses for this factor

Returns: a Gaussian factor

◆ linearizeDamped() [2/3]

template<class CAMERA>

std::shared_ptr<GaussianFactor> gtsam::SmartProjectionFactor< CAMERA >::linearizeDamped	(	const Values &	values,
		const double	lambda = `0.0`
	)		const

inlineinherited

Linearize to Gaussian Factor

Parameters

values Values structure which must contain camera poses for this factor

Returns: a Gaussian factor

◆ linearizeDamped() [3/3]

template<class CAMERA >

std::shared_ptr<GaussianFactor> gtsam::SmartProjectionRigFactor< CAMERA >::linearizeDamped	(	const Values &	values,
		const double &	lambda = `0.0`
	)		const

inline

Linearize to Gaussian Factor (possibly adding a damping factor Lambda for LM)

Parameters

values Values structure which must contain camera poses and extrinsic pose for this factor

Returns: a Gaussian factor

◆ nonUniqueKeys()

template<class CAMERA >

const KeyVector& gtsam::SmartProjectionRigFactor< CAMERA >::nonUniqueKeys ( ) const

inline

return (for each observation) the (possibly non unique) keys involved in the measurements

◆ point() [1/2]

template<class CAMERA>

TriangulationResult gtsam::SmartProjectionFactor< CAMERA >::point ( ) const

inlineinherited

return the landmark

◆ point() [2/2]

template<class CAMERA>

TriangulationResult gtsam::SmartProjectionFactor< CAMERA >::point ( const Values & values ) const

inlineinherited

COMPUTE the landmark

◆ print()

template<class CAMERA >

void gtsam::SmartProjectionRigFactor< CAMERA >::print	(	const std::string &	s = `""`,
		const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`
	)		const

inlineoverridevirtual

print

Parameters

s	optional string naming the factor
keyFormatter	optional formatter useful for printing Symbols

Reimplemented from gtsam::SmartProjectionFactor< CAMERA >.

◆ rekey() [1/2]

virtual shared_ptr gtsam::NonlinearFactor::rekey ( const std::map< Key, Key > & rekey_mapping ) const

virtualinherited

Creates a shared_ptr clone of the factor with different keys using a map from old->new keys

Reimplemented in gtsam::LinearContainerFactor.

◆ rekey() [2/2]

virtual shared_ptr gtsam::NonlinearFactor::rekey ( const KeyVector & new_keys ) const

virtualinherited

Clones a factor and fully replaces its keys

Parameters

new_keys is the full replacement set of keys

Reimplemented in gtsam::LinearContainerFactor.

◆ sendable()

virtual bool gtsam::NonlinearFactor::sendable ( ) const

inlinevirtualinherited

Should the factor be evaluated in the same thread as the caller This is to enable factors that has shared states (like the Python GIL lock)

Reimplemented in gtsam::CustomFactor.

◆ size()

size_t gtsam::Factor::size ( ) const

inlineinherited

Returns: the number of variables involved in this factor

◆ totalReprojectionError() [1/2]

template<class CAMERA>

template<class POINT >

double gtsam::SmartFactorBase< CAMERA >::totalReprojectionError	(	const Cameras &	cameras,
		const POINT &	point
	)		const

inlineinherited

Calculate the error of the factor. This is the log-likelihood, e.g. \( 0.5(h(x)-z)^2/\sigma^2 \) in case of Gaussian. In this class, we take the raw prediction error \( h(x)-z \), ask the noise model to transform it to \( (h(x)-z)^2/\sigma^2 \), and then multiply by 0.5. Will be used in "error(Values)" function required by NonlinearFactor base class

◆ totalReprojectionError() [2/2]

template<class CAMERA>

double gtsam::SmartProjectionFactor< CAMERA >::totalReprojectionError	(	const Cameras &	cameras,
		std::optional< Point3 >	externalPoint = `{}`
	)		const

inlineinherited

Calculate the error of the factor. This is the log-likelihood, e.g. \( 0.5(h(x)-z)^2/\sigma^2 \) in case of Gaussian. In this class, we take the raw prediction error \( h(x)-z \), ask the noise model to transform it to \( (h(x)-z)^2/\sigma^2 \), and then multiply by 0.5.

◆ triangulateAndComputeE() [1/2]

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::triangulateAndComputeE	(	Matrix &	E,
		const Cameras &	cameras
	)		const

inlineinherited

Triangulate and compute derivative of error with respect to point

Returns: whether triangulation worked

◆ triangulateAndComputeE() [2/2]

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::triangulateAndComputeE	(	Matrix &	E,
		const Values &	values
	)		const

inlineinherited

Triangulate and compute derivative of error with respect to point

Returns: whether triangulation worked

◆ triangulateForLinearize()

template<class CAMERA>

bool gtsam::SmartProjectionFactor< CAMERA >::triangulateForLinearize ( const Cameras & cameras ) const

inlineinherited

Possibly re-triangulate before calculating Jacobians.

Parameters

cameras

Returns: true if we could safely triangulate

◆ triangulateSafe()

template<class CAMERA>

TriangulationResult gtsam::SmartProjectionFactor< CAMERA >::triangulateSafe ( const Cameras & cameras ) const

inlineinherited

Call gtsam::triangulateSafe iff we need to re-triangulate.

Parameters

cameras

Returns: TriangulationResult

◆ unwhitenedError() [1/2]

template<class CAMERA>

template<class POINT >

Vector gtsam::SmartFactorBase< CAMERA >::unwhitenedError	(	const Cameras &	cameras,
		const POINT &	point,
		typename Cameras::FBlocks *	Fs = `nullptr`,
		Matrix *	E = `nullptr`
	)		const

inlineinherited

Compute reprojection errors [h(x)-z] = [cameras.project(p)-z] and derivatives. This is the error before the noise model is applied. The templated version described above must finally get resolved to this function.

◆ unwhitenedError() [2/2]

template<class CAMERA>

template<class POINT , class ... OptArgs, typename = std::enable_if_t<sizeof...(OptArgs)!=0>>

Vector gtsam::SmartFactorBase< CAMERA >::unwhitenedError	(	const Cameras &	cameras,
		const POINT &	point,
		OptArgs &&...	optArgs
	)		const

inlineinherited

An overload of unwhitenedError. This allows end users to provide optional arguments that are l-value references to the matrices and vectors that will be used to store the results instead of pointers.

◆ updateAugmentedHessian()

template<class CAMERA>

void gtsam::SmartFactorBase< CAMERA >::updateAugmentedHessian	(	const Cameras &	cameras,
		const Point3 &	point,
		const double	lambda,
		bool	diagonalDamping,
		SymmetricBlockMatrix &	augmentedHessian,
		const KeyVector	allKeys
	)		const

inlineinherited

Add the contribution of the smart factor to a pre-allocated Hessian, using sparse linear algebra. More efficient than the creation of the Hessian without preallocation of the SymmetricBlockMatrix

◆ whitenedError()

template<class CAMERA>

template<class POINT >

Vector gtsam::SmartFactorBase< CAMERA >::whitenedError	(	const Cameras &	cameras,
		const POINT &	point
	)		const

inlineinherited

Calculate vector of re-projection errors [h(x)-z] = [cameras.project(p) - z], with the noise model applied.

Member Data Documentation

◆ cameraIds_

template<class CAMERA >

FastVector<size_t> gtsam::SmartProjectionRigFactor< CAMERA >::cameraIds_

protected

vector of camera Ids (one for each observation, in the same order), identifying which camera took the measurement

◆ measured_

template<class CAMERA>

ZVector gtsam::SmartFactorBase< CAMERA >::measured_

protectedinherited

Measurements for each of the m views. We keep a copy of the measurements for I/O and computing the error. The order is kept the same as the keys that we use to create the factor.

◆ noiseModel_

template<class CAMERA>

SharedIsotropic gtsam::SmartFactorBase< CAMERA >::noiseModel_

protectedinherited

As of Feb 22, 2015, the noise model is the same for all measurements and is isotropic. This allows for moving most calculations of Schur complement etc. to be easily moved to CameraSet, and also agrees pragmatically with what is normally done.

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

/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/slam/SmartProjectionRigFactor.h

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Static Public Attributes

Static Protected Member Functions

Protected Attributes

Detailed Description

template<class CAMERA> class gtsam::SmartProjectionRigFactor< CAMERA >

Constructor & Destructor Documentation

◆ SmartProjectionRigFactor()

Member Function Documentation

◆ active()

◆ add() [1/4]

◆ add() [2/4]

◆ add() [3/4]

◆ add() [4/4]

◆ begin() [1/2]

◆ begin() [2/2]

◆ cameras()

◆ clone()

◆ computeJacobians()

◆ computeJacobiansSVD()

◆ computeJacobiansWithTriangulatedPoint()

◆ correctForMissingMeasurements() [1/2]

◆ correctForMissingMeasurements() [2/2]

◆ createJacobianSVDFactor()

◆ decideIfTriangulate()

◆ end() [1/2]

◆ end() [2/2]

◆ error() [1/2]

◆ error() [2/2]

◆ FromIterators()

◆ FromKeys()

◆ isDegenerate()

◆ isFarPoint()

◆ isOutlier()

◆ isPointBehindCamera()

◆ keys()

◆ linearizeDamped() [1/3]

◆ linearizeDamped() [2/3]

◆ linearizeDamped() [3/3]

◆ nonUniqueKeys()

◆ point() [1/2]

◆ point() [2/2]

◆ print()

◆ rekey() [1/2]

◆ rekey() [2/2]

◆ sendable()

◆ size()

◆ totalReprojectionError() [1/2]

◆ totalReprojectionError() [2/2]

◆ triangulateAndComputeE() [1/2]

◆ triangulateAndComputeE() [2/2]

◆ triangulateForLinearize()

◆ triangulateSafe()

◆ unwhitenedError() [1/2]

◆ unwhitenedError() [2/2]

◆ updateAugmentedHessian()

◆ whitenedError()

Member Data Documentation

◆ cameraIds_

◆ measured_

◆ noiseModel_

template<class CAMERA>
class gtsam::SmartProjectionRigFactor< CAMERA >