277 lines
10 KiB
C++
277 lines
10 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 BetweenFactorEM.h
|
|
* @author Vadim Indelman
|
|
**/
|
|
#pragma once
|
|
|
|
#include <ostream>
|
|
|
|
#include <gtsam/base/Testable.h>
|
|
#include <gtsam/base/Lie.h>
|
|
#include <gtsam/nonlinear/NonlinearFactor.h>
|
|
#include <gtsam/linear/GaussianFactor.h>
|
|
|
|
namespace gtsam {
|
|
|
|
/**
|
|
* A class for a measurement predicted by "between(config[key1],config[key2])"
|
|
* @tparam VALUE the Value type
|
|
* @addtogroup SLAM
|
|
*/
|
|
template<class VALUE>
|
|
class BetweenFactorEM: public NonlinearFactor {
|
|
|
|
public:
|
|
|
|
typedef VALUE T;
|
|
|
|
private:
|
|
|
|
typedef BetweenFactorEM<VALUE> This;
|
|
typedef gtsam::NonlinearFactor Base;
|
|
|
|
gtsam::Key key1_;
|
|
gtsam::Key key2_;
|
|
|
|
VALUE measured_; /** The measurement */
|
|
|
|
SharedGaussian model_inlier_;
|
|
SharedGaussian model_outlier_;
|
|
|
|
double prior_inlier_;
|
|
double prior_outlier_;
|
|
|
|
/** concept check by type */
|
|
GTSAM_CONCEPT_LIE_TYPE(T)
|
|
GTSAM_CONCEPT_TESTABLE_TYPE(T)
|
|
|
|
public:
|
|
|
|
// shorthand for a smart pointer to a factor
|
|
typedef typename boost::shared_ptr<BetweenFactorEM> shared_ptr;
|
|
|
|
/** default constructor - only use for serialization */
|
|
BetweenFactorEM() {}
|
|
|
|
/** Constructor */
|
|
BetweenFactorEM(Key key1, Key key2, const VALUE& measured,
|
|
const SharedGaussian& model_inlier, const SharedGaussian& model_outlier,
|
|
const double prior_inlier, const double prior_outlier) :
|
|
key1_(key1), key2_(key2), measured_(measured),
|
|
model_inlier_(model_inlier), model_outlier_(model_outlier),
|
|
prior_inlier_(prior_inlier), prior_outlier_(prior_outlier){
|
|
}
|
|
|
|
virtual ~BetweenFactorEM() {}
|
|
|
|
|
|
/** implement functions needed for Testable */
|
|
|
|
/** print */
|
|
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
|
|
Base::print(s, keyFormatter);
|
|
}
|
|
|
|
/** equals */
|
|
virtual bool equals(const NonlinearFactor& f, double tol=1e-9) const {
|
|
const This *t = dynamic_cast<const This*> (&f);
|
|
|
|
if(t && Base::equals(f))
|
|
return key1_ == t->key1_ && key2_ == t->key2_ &&
|
|
// model_inlier_->equals(t->model_inlier_ ) && // TODO: fix here
|
|
// model_outlier_->equals(t->model_outlier_ ) &&
|
|
prior_outlier_ == t->prior_outlier_ && prior_inlier_ == t->prior_inlier_ && measured_.equals(t->measured_);
|
|
else
|
|
return false;
|
|
}
|
|
|
|
/** implement functions needed to derive from Factor */
|
|
|
|
/* ************************************************************************* */
|
|
virtual double error(const gtsam::Values& x) const {
|
|
return whitenedError(x).squaredNorm();
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
/**
|
|
* Linearize a non-linearFactorN to get a gtsam::GaussianFactor,
|
|
* \f$ Ax-b \approx h(x+\delta x)-z = h(x) + A \delta x - z \f$
|
|
* Hence \f$ b = z - h(x) = - \mathtt{error\_vector}(x) \f$
|
|
*/
|
|
/* This version of linearize recalculates the noise model each time */
|
|
virtual boost::shared_ptr<gtsam::GaussianFactor> linearize(const gtsam::Values& x, const gtsam::Ordering& ordering) const {
|
|
// Only linearize if the factor is active
|
|
if (!this->active(x))
|
|
return boost::shared_ptr<gtsam::JacobianFactor>();
|
|
|
|
//std::cout<<"About to linearize"<<std::endl;
|
|
gtsam::Matrix A1, A2;
|
|
std::vector<gtsam::Matrix> A(this->size());
|
|
gtsam::Vector b = -whitenedError(x, A);
|
|
A1 = A[0];
|
|
A2 = A[1];
|
|
|
|
return gtsam::GaussianFactor::shared_ptr(
|
|
new gtsam::JacobianFactor(ordering[key1_], A1, ordering[key2_], A2, b, gtsam::noiseModel::Unit::Create(b.size())));
|
|
}
|
|
|
|
|
|
/* ************************************************************************* */
|
|
gtsam::Vector whitenedError(const gtsam::Values& x,
|
|
boost::optional<std::vector<gtsam::Matrix>&> H = boost::none) const {
|
|
|
|
bool debug = true;
|
|
|
|
const T& p1 = x.at<T>(key1_);
|
|
const T& p2 = x.at<T>(key2_);
|
|
|
|
Matrix H1, H2;
|
|
|
|
T hx = p1.between(p2, H1, H2); // h(x)
|
|
// manifold equivalent of h(x)-z -> log(z,h(x))
|
|
|
|
Vector err = measured_.localCoordinates(hx);
|
|
|
|
// Calculate indicator probabilities (inlier and outlier)
|
|
Vector err_wh_inlier = model_inlier_->whiten(err);
|
|
Vector err_wh_outlier = model_outlier_->whiten(err);
|
|
|
|
Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
|
|
Matrix invCov_outlier = model_outlier_->R().transpose() * model_outlier_->R();
|
|
|
|
double p_inlier = prior_inlier_ * invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) );
|
|
double p_outlier = prior_outlier_ * invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) );
|
|
|
|
double sumP = p_inlier + p_outlier;
|
|
p_inlier /= sumP;
|
|
p_outlier /= sumP;
|
|
// TODO: possibly need to bump up near-zero probabilities (as in linerFlow.h)
|
|
|
|
Vector err_wh_eq;
|
|
err_wh_eq.resize(err_wh_inlier.rows()*2);
|
|
err_wh_eq << sqrt(p_inlier) * err_wh_inlier.array() , sqrt(p_outlier) * err_wh_outlier.array();
|
|
|
|
if (H){
|
|
|
|
// stack Jacobians for the two indicators for each of the key
|
|
|
|
Matrix H1_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H1);
|
|
Matrix H1_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H1);
|
|
Matrix H1_aug = gtsam::stack(2, &H1_inlier, &H1_outlier);
|
|
|
|
Matrix H2_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H2);
|
|
Matrix H2_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H2);
|
|
Matrix H2_aug = gtsam::stack(2, &H2_inlier, &H2_outlier);
|
|
|
|
(*H)[0].resize(H1_aug.rows(),H1_aug.cols());
|
|
(*H)[1].resize(H2_aug.rows(),H2_aug.cols());
|
|
|
|
(*H)[0] = H1_aug;
|
|
(*H)[1] = H2_aug;
|
|
}
|
|
|
|
if (debug){
|
|
std::cout<<"unwhitened error: "<<err[0]<<" "<<err[1]<<" "<<err[2]<<std::endl;
|
|
std::cout<<"err_wh_inlier: "<<err_wh_inlier[0]<<" "<<err_wh_inlier[1]<<" "<<err_wh_inlier[2]<<std::endl;
|
|
std::cout<<"err_wh_outlier: "<<err_wh_outlier[0]<<" "<<err_wh_outlier[1]<<" "<<err_wh_outlier[2]<<std::endl;
|
|
|
|
std::cout<<"p_inlier, p_outlier, sumP: "<<p_inlier<<" "<<p_outlier<<" " << sumP << std::endl;
|
|
|
|
std::cout<<"prior_inlier_, prior_outlier_: "<<prior_inlier_<<" "<<prior_outlier_<< std::endl;
|
|
|
|
double s_inl = -0.5 * err_wh_inlier.dot(err_wh_inlier);
|
|
double s_outl = -0.5 * err_wh_outlier.dot(err_wh_outlier);
|
|
std::cout<<"s_inl, s_outl: "<<s_inl<<" "<<s_outl<<std::endl;
|
|
|
|
std::cout<<"norm of invCov_inlier, invCov_outlier: "<<invCov_inlier.norm()<<" "<<invCov_outlier.norm()<<std::endl;
|
|
double q_inl = invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) );
|
|
double q_outl = invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) );
|
|
std::cout<<"q_inl, q_outl: "<<q_inl<<" "<<q_outl<<std::endl;
|
|
|
|
// Matrix Cov_inlier = invCov_inlier.inverse();
|
|
// Matrix Cov_outlier = invCov_outlier.inverse();
|
|
// std::cout<<"Cov_inlier: "<<std::endl<<
|
|
// Cov_inlier(0,0) << " " << Cov_inlier(0,1) << " " << Cov_inlier(0,2) <<std::endl<<
|
|
// Cov_inlier(1,0) << " " << Cov_inlier(1,1) << " " << Cov_inlier(1,2) <<std::endl<<
|
|
// Cov_inlier(2,0) << " " << Cov_inlier(2,1) << " " << Cov_inlier(2,2) <<std::endl;
|
|
// std::cout<<"Cov_outlier: "<<std::endl<<
|
|
// Cov_outlier(0,0) << " " << Cov_outlier(0,1) << " " << Cov_outlier(0,2) <<std::endl<<
|
|
// Cov_outlier(1,0) << " " << Cov_outlier(1,1) << " " << Cov_outlier(1,2) <<std::endl<<
|
|
// Cov_outlier(2,0) << " " << Cov_outlier(2,1) << " " << Cov_outlier(2,2) <<std::endl;
|
|
std::cout<<"===="<<std::endl;
|
|
}
|
|
|
|
|
|
return err_wh_eq;
|
|
}
|
|
|
|
|
|
/* ************************************************************************* */
|
|
void calcIndicatorProb(const gtsam::Values& x,
|
|
double& p_inlier, double& p_outlier, Vector& err) const {
|
|
|
|
const T& p1 = x.at<T>(key1_);
|
|
const T& p2 = x.at<T>(key2_);
|
|
|
|
Matrix H1, H2;
|
|
|
|
T hx = p1.between(p2, H1, H2); // h(x)
|
|
// manifold equivalent of h(x)-z -> log(z,h(x))
|
|
|
|
err = measured_.localCoordinates(hx);
|
|
|
|
// Calculate indicator probabilities (inlier and outlier)
|
|
Vector err_wh_inlier = model_inlier_->whiten(err);
|
|
Vector err_wh_outlier = model_outlier_->whiten(err);
|
|
|
|
Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
|
|
Matrix invCov_outlier = model_outlier_->R().transpose() * model_outlier_->R();
|
|
|
|
p_inlier = prior_inlier_ * invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) );
|
|
p_outlier = prior_outlier_ * invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) );
|
|
|
|
double sumP = p_inlier + p_outlier;
|
|
p_inlier /= sumP;
|
|
p_outlier /= sumP;
|
|
// TODO: possibly need to bump up near-zero probabilities (as in linerFlow.h)
|
|
}
|
|
|
|
/** return the measured */
|
|
const VALUE& measured() const {
|
|
return measured_;
|
|
}
|
|
|
|
/** number of variables attached to this factor */
|
|
std::size_t size() const {
|
|
return 2;
|
|
}
|
|
|
|
virtual size_t dim() const {
|
|
return model_inlier_->R().rows() + model_inlier_->R().cols();
|
|
}
|
|
|
|
private:
|
|
|
|
/** Serialization function */
|
|
friend class boost::serialization::access;
|
|
template<class ARCHIVE>
|
|
void serialize(ARCHIVE & ar, const unsigned int version) {
|
|
ar & boost::serialization::make_nvp("NonlinearFactor",
|
|
boost::serialization::base_object<Base>(*this));
|
|
ar & BOOST_SERIALIZATION_NVP(measured_);
|
|
}
|
|
}; // \class BetweenFactorEM
|
|
|
|
} /// namespace gtsam
|