gtsam::noiseModel::mEstimator Namespace Reference

Classes
class	AsymmetricCauchy
class	AsymmetricTukey
class	Base
class	Cauchy
class	Custom
class	DCS
class	Fair
class	GemanMcClure
class	Huber
class	L2WithDeadZone
class	Null
class	Tukey
class	Welsch

Typedefs
using	CustomLossFunction = std::function< double(double)>
using	CustomWeightFunction = std::function< double(double)>

Detailed Description

The mEstimator name space contains all robust error functions. It mirrors the exposition at https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf which talks about minimizing \sum \rho(r_i), where \rho is a loss function of choice.

To illustrate, let's consider the least-squares (L2), L1, and Huber estimators as examples:

Name Symbol Least-Squares L1-norm Huber Loss \rho(x) 0.5*x^2 |x| 0.5*x^2 if |x|<k, 0.5*k^2 + k|x-k| otherwise Derivative \phi(x) x sgn(x) x if |x|<k, k sgn(x) otherwise Weight w(x)=\phi(x)/x 1 1/|x| 1 if |x|<k, k/|x| otherwise

With these definitions, D(\rho(x), p) = \phi(x) D(x,p) = w(x) x D(x,p) = w(x) D(L2(x), p), and hence we can solve the equivalent weighted least squares problem \sum w(r_i) \rho(r_i)

Each M-estimator in the mEstimator name space simply implements the above functions.

Typedef Documentation

CustomLossFunction

using gtsam::noiseModel::mEstimator::CustomLossFunction = typedef std::function<double(double)>

Definition at line 548 of file LossFunctions.h.

CustomWeightFunction

using gtsam::noiseModel::mEstimator::CustomWeightFunction = typedef std::function<double(double)>

Definition at line 549 of file LossFunctions.h.