gtsam  4.0.0
gtsam
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