372 lines
13 KiB
C++
372 lines
13 KiB
C++
|
|
/* ----------------------------------------------------------------------------
|
|
|
|
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
|
* Atlanta, Georgia 30332-0415
|
|
* All Rights Reserved
|
|
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
|
|
|
* See LICENSE for the license information
|
|
|
|
* -------------------------------------------------------------------------- */
|
|
|
|
/**
|
|
* @file NoiseModel.h
|
|
* @date Jan 13, 2010
|
|
* @author Richard Roberts
|
|
* @author Frank Dellaert
|
|
*/
|
|
|
|
#pragma once
|
|
|
|
#include <gtsam/base/Matrix.h>
|
|
#include <gtsam/base/Testable.h>
|
|
#include <gtsam/dllexport.h>
|
|
|
|
#include <boost/serialization/extended_type_info.hpp>
|
|
#include <boost/serialization/nvp.hpp>
|
|
#include <boost/serialization/optional.hpp>
|
|
#include <boost/serialization/shared_ptr.hpp>
|
|
#include <boost/serialization/singleton.hpp>
|
|
|
|
namespace gtsam {
|
|
namespace noiseModel {
|
|
// clang-format off
|
|
/**
|
|
* 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.
|
|
*/
|
|
// clang-format on
|
|
namespace mEstimator {
|
|
|
|
//---------------------------------------------------------------------------------------
|
|
|
|
class GTSAM_EXPORT Base {
|
|
public:
|
|
enum ReweightScheme { Scalar, Block };
|
|
typedef boost::shared_ptr<Base> shared_ptr;
|
|
|
|
protected:
|
|
/** the rows can be weighted independently according to the error
|
|
* or uniformly with the norm of the right hand side */
|
|
ReweightScheme reweight_;
|
|
|
|
public:
|
|
Base(const ReweightScheme reweight = Block) : reweight_(reweight) {}
|
|
virtual ~Base() {}
|
|
|
|
/*
|
|
* This method is responsible for returning the total penalty for a given
|
|
* amount of error. For example, this method is responsible for implementing
|
|
* the quadratic function for an L2 penalty, the absolute value function for
|
|
* an L1 penalty, etc.
|
|
*
|
|
* TODO(mikebosse): When the loss function has as input the norm of the
|
|
* error vector, then it prevents implementations of asymmeric loss
|
|
* functions. It would be better for this function to accept the vector and
|
|
* internally call the norm if necessary.
|
|
*/
|
|
virtual double loss(double distance) const { return 0; };
|
|
|
|
/*
|
|
* This method is responsible for returning the weight function for a given
|
|
* amount of error. The weight function is related to the analytic derivative
|
|
* of the loss function. See
|
|
* https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf
|
|
* for details. This method is required when optimizing cost functions with
|
|
* robust penalties using iteratively re-weighted least squares.
|
|
*/
|
|
virtual double weight(double distance) const = 0;
|
|
|
|
virtual void print(const std::string &s) const = 0;
|
|
virtual bool equals(const Base &expected, double tol = 1e-8) const = 0;
|
|
|
|
double sqrtWeight(double distance) const { return std::sqrt(weight(distance)); }
|
|
|
|
/** produce a weight vector according to an error vector and the implemented
|
|
* robust function */
|
|
Vector weight(const Vector &error) const;
|
|
|
|
/** square root version of the weight function */
|
|
Vector sqrtWeight(const Vector &error) const {
|
|
return weight(error).cwiseSqrt();
|
|
}
|
|
|
|
/** reweight block matrices and a vector according to their weight
|
|
* implementation */
|
|
void reweight(Vector &error) const;
|
|
void reweight(std::vector<Matrix> &A, Vector &error) const;
|
|
void reweight(Matrix &A, Vector &error) const;
|
|
void reweight(Matrix &A1, Matrix &A2, Vector &error) const;
|
|
void reweight(Matrix &A1, Matrix &A2, Matrix &A3, Vector &error) const;
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_NVP(reweight_);
|
|
}
|
|
};
|
|
|
|
/// Null class should behave as Gaussian
|
|
class GTSAM_EXPORT Null : public Base {
|
|
public:
|
|
typedef boost::shared_ptr<Null> shared_ptr;
|
|
|
|
Null(const ReweightScheme reweight = Block) : Base(reweight) {}
|
|
~Null() {}
|
|
double weight(double /*error*/) const override { return 1.0; }
|
|
double loss(double distance) const override { return 0.5 * distance * distance; }
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base & /*expected*/, double /*tol*/) const override { return true; }
|
|
static shared_ptr Create();
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
}
|
|
};
|
|
|
|
/// Fair implements the "Fair" robust error model (Zhang97ivc)
|
|
class GTSAM_EXPORT Fair : public Base {
|
|
protected:
|
|
double c_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<Fair> shared_ptr;
|
|
|
|
Fair(double c = 1.3998, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double c, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(c_);
|
|
}
|
|
};
|
|
|
|
/// Huber implements the "Huber" robust error model (Zhang97ivc)
|
|
class GTSAM_EXPORT Huber : public Base {
|
|
protected:
|
|
double k_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<Huber> shared_ptr;
|
|
|
|
Huber(double k = 1.345, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(k_);
|
|
}
|
|
};
|
|
|
|
/// Cauchy implements the "Cauchy" robust error model (Lee2013IROS). Contributed
|
|
/// by:
|
|
/// Dipl.-Inform. Jan Oberlaender (M.Sc.), FZI Research Center for
|
|
/// Information Technology, Karlsruhe, Germany.
|
|
/// oberlaender@fzi.de
|
|
/// Thanks Jan!
|
|
class GTSAM_EXPORT Cauchy : public Base {
|
|
protected:
|
|
double k_, ksquared_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<Cauchy> shared_ptr;
|
|
|
|
Cauchy(double k = 0.1, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(k_);
|
|
}
|
|
};
|
|
|
|
/// Tukey implements the "Tukey" robust error model (Zhang97ivc)
|
|
class GTSAM_EXPORT Tukey : public Base {
|
|
protected:
|
|
double c_, csquared_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<Tukey> shared_ptr;
|
|
|
|
Tukey(double c = 4.6851, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(c_);
|
|
}
|
|
};
|
|
|
|
/// Welsch implements the "Welsch" robust error model (Zhang97ivc)
|
|
class GTSAM_EXPORT Welsch : public Base {
|
|
protected:
|
|
double c_, csquared_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<Welsch> shared_ptr;
|
|
|
|
Welsch(double c = 2.9846, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(c_);
|
|
}
|
|
};
|
|
|
|
/// GemanMcClure implements the "Geman-McClure" robust error model
|
|
/// (Zhang97ivc).
|
|
///
|
|
/// Note that Geman-McClure weight function uses the parameter c == 1.0,
|
|
/// but here it's allowed to use different values, so we actually have
|
|
/// the generalized Geman-McClure from (Agarwal15phd).
|
|
class GTSAM_EXPORT GemanMcClure : public Base {
|
|
public:
|
|
typedef boost::shared_ptr<GemanMcClure> shared_ptr;
|
|
|
|
GemanMcClure(double c = 1.0, const ReweightScheme reweight = Block);
|
|
~GemanMcClure() {}
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
protected:
|
|
double c_;
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(c_);
|
|
}
|
|
};
|
|
|
|
/// DCS implements the Dynamic Covariance Scaling robust error model
|
|
/// from the paper Robust Map Optimization (Agarwal13icra).
|
|
///
|
|
/// Under the special condition of the parameter c == 1.0 and not
|
|
/// forcing the output weight s <= 1.0, DCS is similar to Geman-McClure.
|
|
class GTSAM_EXPORT DCS : public Base {
|
|
public:
|
|
typedef boost::shared_ptr<DCS> shared_ptr;
|
|
|
|
DCS(double c = 1.0, const ReweightScheme reweight = Block);
|
|
~DCS() {}
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
protected:
|
|
double c_;
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(c_);
|
|
}
|
|
};
|
|
|
|
/// L2WithDeadZone implements a standard L2 penalty, but with a dead zone of
|
|
/// width 2*k, centered at the origin. The resulting penalty within the dead
|
|
/// zone is always zero, and grows quadratically outside the dead zone. In this
|
|
/// sense, the L2WithDeadZone penalty is "robust to inliers", rather than being
|
|
/// robust to outliers. This penalty can be used to create barrier functions in
|
|
/// a general way.
|
|
class GTSAM_EXPORT L2WithDeadZone : public Base {
|
|
protected:
|
|
double k_;
|
|
|
|
public:
|
|
typedef boost::shared_ptr<L2WithDeadZone> shared_ptr;
|
|
|
|
L2WithDeadZone(double k = 1.0, const ReweightScheme reweight = Block);
|
|
double weight(double distance) const override;
|
|
double loss(double distance) const override;
|
|
void print(const std::string &s) const override;
|
|
bool equals(const Base &expected, double tol = 1e-8) const override;
|
|
static shared_ptr Create(double k, const ReweightScheme reweight = Block);
|
|
|
|
private:
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template <class ARCHIVE>
|
|
void serialize(ARCHIVE &ar, const unsigned int /*version*/) {
|
|
ar &BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
|
|
ar &BOOST_SERIALIZATION_NVP(k_);
|
|
}
|
|
};
|
|
|
|
} // namespace mEstimator
|
|
} // namespace noiseModel
|
|
} // namespace gtsam
|