141 lines
4.5 KiB
C++
141 lines
4.5 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file GaussMarkov1stOrderFactor.h
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* @author Vadim Indelman, Stephen Williams, Luca Carlone
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* @date Jan 17, 2012
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**/
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#pragma once
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/linear/GaussianFactor.h>
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#include <gtsam/linear/NoiseModel.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/Lie.h>
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#include <ostream>
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namespace gtsam {
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/*
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* - The 1st order GaussMarkov factor relates two keys of the same type. This relation is given via
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* key_2 = exp(-1/tau*delta_t) * key1 + w_d
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* where tau is the time constant and delta_t is the time difference between the two keys.
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* w_d is the equivalent discrete noise, whose covariance is calculated from the continuous noise model and delta_t.
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* - w_d is approximated as a Gaussian noise.
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* - In the multi-dimensional case, tau is a vector, and the above equation is applied on each element
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* in the state (represented by keys), using the appropriate time constant in the vector tau.
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*/
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/*
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* A class for a measurement predicted by "GaussMarkov1stOrderFactor(config[key1],config[key2])"
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* KEY1::Value is the Lie Group type
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* T is the measurement type, by default the same
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*/
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template<class VALUE>
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class GaussMarkov1stOrderFactor: public NoiseModelFactor2<VALUE, VALUE> {
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private:
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typedef GaussMarkov1stOrderFactor<VALUE> This;
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typedef NoiseModelFactor2<VALUE, VALUE> Base;
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double dt_;
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Vector tau_;
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public:
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// shorthand for a smart pointer to a factor
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typedef typename boost::shared_ptr<GaussMarkov1stOrderFactor> shared_ptr;
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/** default constructor - only use for serialization */
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GaussMarkov1stOrderFactor() {}
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/** Constructor */
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GaussMarkov1stOrderFactor(const Key& key1, const Key& key2, double delta_t, Vector tau,
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const SharedGaussian& model) :
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Base(calcDiscreteNoiseModel(model, delta_t), key1, key2), dt_(delta_t), tau_(tau) {
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}
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virtual ~GaussMarkov1stOrderFactor() {}
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/** implement functions needed for Testable */
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/** print */
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void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override {
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std::cout << s << "GaussMarkov1stOrderFactor("
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<< keyFormatter(this->key1()) << ","
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<< keyFormatter(this->key2()) << ")\n";
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this->noiseModel_->print(" noise model");
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}
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/** equals */
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bool equals(const NonlinearFactor& expected, double tol=1e-9) const override {
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const This *e = dynamic_cast<const This*> (&expected);
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return e != nullptr && Base::equals(*e, tol);
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}
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/** implement functions needed to derive from Factor */
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/** vector of errors */
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Vector evaluateError(const VALUE& p1, const VALUE& p2,
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boost::optional<Matrix&> H1 = boost::none,
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boost::optional<Matrix&> H2 = boost::none) const override {
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Vector v1( traits<VALUE>::Logmap(p1) );
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Vector v2( traits<VALUE>::Logmap(p2) );
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Vector alpha(tau_.size());
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Vector alpha_v1(tau_.size());
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for(int i=0; i<tau_.size(); i++){
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alpha(i) = exp(- 1/tau_(i)*dt_ );
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alpha_v1(i) = alpha(i) * v1(i);
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}
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Vector hx(v2 - alpha_v1);
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if(H1) *H1 = -1 * alpha.asDiagonal();
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if(H2) *H2 = Matrix::Identity(v2.size(),v2.size());
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return hx;
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}
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private:
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/** Serialization function */
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friend class boost::serialization::access;
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template<class ARCHIVE>
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void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
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ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
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ar & BOOST_SERIALIZATION_NVP(dt_);
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ar & BOOST_SERIALIZATION_NVP(tau_);
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}
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SharedGaussian calcDiscreteNoiseModel(const SharedGaussian& model, double delta_t){
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/* Q_d (approx)= Q * delta_t */
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/* In practice, square root of the information matrix is represented, so that:
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* R_d (approx)= R / sqrt(delta_t)
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* */
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noiseModel::Gaussian::shared_ptr gaussian_model = boost::dynamic_pointer_cast<noiseModel::Gaussian>(model);
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SharedGaussian model_d(noiseModel::Gaussian::SqrtInformation(gaussian_model->R()/sqrt(delta_t)));
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return model_d;
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}
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}; // \class GaussMarkov1stOrderFactor
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/// traits
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template<class VALUE> struct traits<GaussMarkov1stOrderFactor<VALUE> > :
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public Testable<GaussMarkov1stOrderFactor<VALUE> > {
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};
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} /// namespace gtsam
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