NoiseModel.h File Reference

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Classes
class	gtsam::noiseModel::Base
	noiseModel::Base is the abstract base class for all noise models. More...
class	gtsam::noiseModel::Gaussian
	Gaussian implements the mathematical model |R*x|^2 = |y|^2 with R'*R=inv(Sigma) where y = whiten(x) = R*x x = unwhiten(x) = inv(R)*y as indeed |y|^2 = y'*y = x'*R'*R*x Various derived classes are available that are more efficient. More...
class	gtsam::noiseModel::Diagonal
	A diagonal noise model implements a diagonal covariance matrix, with the elements of the diagonal specified in a Vector. More...
class	gtsam::noiseModel::Constrained
	A Constrained constrained model is a specialization of Diagonal which allows some or all of the sigmas to be zero, forcing the error to be zero there. More...
class	gtsam::noiseModel::Isotropic
	An isotropic noise model corresponds to a scaled diagonal covariance To construct, use one of the static methods. More...
class	gtsam::noiseModel::Unit
	Unit: i.i.d. More...
class	gtsam::noiseModel::mEstimator::Base
class	gtsam::noiseModel::mEstimator::Null
	Null class is not robust so is a Gaussian ? More...
class	gtsam::noiseModel::mEstimator::Fair
	Fair implements the "Fair" robust error model (Zhang97ivc) More...
class	gtsam::noiseModel::mEstimator::Huber
	Huber implements the "Huber" robust error model (Zhang97ivc) More...
class	gtsam::noiseModel::mEstimator::Cauchy
	Cauchy implements the "Cauchy" robust error model (Lee2013IROS). More...
class	gtsam::noiseModel::mEstimator::Tukey
	Tukey implements the "Tukey" robust error model (Zhang97ivc) More...
class	gtsam::noiseModel::mEstimator::Welsh
	Welsh implements the "Welsh" robust error model (Zhang97ivc) More...
class	gtsam::noiseModel::mEstimator::GemanMcClure
	GemanMcClure implements the "Geman-McClure" robust error model (Zhang97ivc). More...
class	gtsam::noiseModel::mEstimator::DCS
	DCS implements the Dynamic Covariance Scaling robust error model from the paper Robust Map Optimization (Agarwal13icra). More...
class	gtsam::noiseModel::mEstimator::L2WithDeadZone
	L2WithDeadZone implements a standard L2 penalty, but with a dead zone of width 2*k, centered at the origin. More...
class	gtsam::noiseModel::Robust
	Base class for robust error models The robust M-estimators above simply tell us how to re-weight the residual, and are isotropic kernels, in that they do not allow for correlated noise. More...
struct	gtsam::traits< noiseModel::Gaussian >
	traits More...
struct	gtsam::traits< noiseModel::Diagonal >
struct	gtsam::traits< noiseModel::Constrained >
struct	gtsam::traits< noiseModel::Isotropic >
struct	gtsam::traits< noiseModel::Unit >

Namespaces
	gtsam
	Global functions in a separate testing namespace.
	gtsam::noiseModel
	All noise models live in the noiseModel namespace.
	gtsam::noiseModel::mEstimator
	The mEstimator name space contains all robust error functions.

Typedefs
typedef noiseModel::Base::shared_ptr	gtsam::SharedNoiseModel
	Note, deliberately not in noiseModel namespace. More...
typedef noiseModel::Gaussian::shared_ptr	gtsam::SharedGaussian
typedef noiseModel::Diagonal::shared_ptr	gtsam::SharedDiagonal
typedef noiseModel::Constrained::shared_ptr	gtsam::SharedConstrained
typedef noiseModel::Isotropic::shared_ptr	gtsam::SharedIsotropic

Functions
boost::optional< Vector >	gtsam::noiseModel::checkIfDiagonal (const Matrix M)

Detailed Description

Date

Jan 13, 2010

Author

Richard Roberts

Frank Dellaert