430 lines
15 KiB
C++
430 lines
15 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 BetweenFactorEM.h
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* @author Vadim Indelman
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**/
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#pragma once
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#include <ostream>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/Lie.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/linear/GaussianFactor.h>
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#include <gtsam/nonlinear/Marginals.h>
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namespace gtsam {
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/**
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* A class for a measurement predicted by "between(config[key1],config[key2])"
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* @tparam VALUE the Value type
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* @addtogroup SLAM
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*/
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template<class VALUE>
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class BetweenFactorEM: public NonlinearFactor {
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public:
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typedef VALUE T;
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private:
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typedef BetweenFactorEM<VALUE> This;
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typedef gtsam::NonlinearFactor Base;
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gtsam::Key key1_;
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gtsam::Key key2_;
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VALUE measured_; /** The measurement */
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SharedGaussian model_inlier_;
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SharedGaussian model_outlier_;
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double prior_inlier_;
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double prior_outlier_;
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bool flag_bump_up_near_zero_probs_;
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/** concept check by type */
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GTSAM_CONCEPT_LIE_TYPE(T)GTSAM_CONCEPT_TESTABLE_TYPE(T)
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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<BetweenFactorEM> shared_ptr;
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/** default constructor - only use for serialization */
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BetweenFactorEM() {
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}
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/** Constructor */
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BetweenFactorEM(Key key1, Key key2, const VALUE& measured,
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const SharedGaussian& model_inlier, const SharedGaussian& model_outlier,
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const double prior_inlier, const double prior_outlier,
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const bool flag_bump_up_near_zero_probs = false) :
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Base(cref_list_of<2>(key1)(key2)), key1_(key1), key2_(key2), measured_(
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measured), model_inlier_(model_inlier), model_outlier_(model_outlier), prior_inlier_(
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prior_inlier), prior_outlier_(prior_outlier), flag_bump_up_near_zero_probs_(
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flag_bump_up_near_zero_probs) {
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}
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virtual ~BetweenFactorEM() {
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}
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/** implement functions needed for Testable */
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/** print */
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virtual void print(const std::string& s, const KeyFormatter& keyFormatter =
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DefaultKeyFormatter) const {
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std::cout << s << "BetweenFactorEM(" << keyFormatter(key1_) << ","
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<< keyFormatter(key2_) << ")\n";
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measured_.print(" measured: ");
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model_inlier_->print(" noise model inlier: ");
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model_outlier_->print(" noise model outlier: ");
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std::cout << "(prior_inlier, prior_outlier_) = (" << prior_inlier_ << ","
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<< prior_outlier_ << ")\n";
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// Base::print(s, keyFormatter);
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}
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/** equals */
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virtual bool equals(const NonlinearFactor& f, double tol = 1e-9) const {
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const This *t = dynamic_cast<const This*>(&f);
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if (t && Base::equals(f))
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return key1_ == t->key1_ && key2_ == t->key2_
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&&
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// model_inlier_->equals(t->model_inlier_ ) && // TODO: fix here
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// model_outlier_->equals(t->model_outlier_ ) &&
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prior_outlier_ == t->prior_outlier_
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&& prior_inlier_ == t->prior_inlier_ && measured_.equals(t->measured_);
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else
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return false;
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}
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/** implement functions needed to derive from Factor */
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/* ************************************************************************* */
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virtual double error(const gtsam::Values& x) const {
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return whitenedError(x).squaredNorm();
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}
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/* ************************************************************************* */
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/**
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* Linearize a non-linearFactorN to get a gtsam::GaussianFactor,
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* \f$ Ax-b \approx h(x+\delta x)-z = h(x) + A \delta x - z \f$
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* Hence \f$ b = z - h(x) = - \mathtt{error\_vector}(x) \f$
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*/
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/* This version of linearize recalculates the noise model each time */
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virtual boost::shared_ptr<gtsam::GaussianFactor> linearize(
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const gtsam::Values& x) const {
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// Only linearize if the factor is active
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if (!this->active(x))
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return boost::shared_ptr<gtsam::JacobianFactor>();
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//std::cout<<"About to linearize"<<std::endl;
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gtsam::Matrix A1, A2;
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std::vector<gtsam::Matrix> A(this->size());
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gtsam::Vector b = -whitenedError(x, A);
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A1 = A[0];
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A2 = A[1];
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return gtsam::GaussianFactor::shared_ptr(
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new gtsam::JacobianFactor(key1_, A1, key2_, A2, b,
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gtsam::noiseModel::Unit::Create(b.size())));
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}
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/* ************************************************************************* */
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gtsam::Vector whitenedError(const gtsam::Values& x,
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boost::optional<std::vector<gtsam::Matrix>&> H = boost::none) const {
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bool debug = true;
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const T& p1 = x.at<T>(key1_);
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const T& p2 = x.at<T>(key2_);
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Matrix H1, H2;
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T hx = p1.between(p2, H1, H2); // h(x)
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// manifold equivalent of h(x)-z -> log(z,h(x))
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Vector err = measured_.localCoordinates(hx);
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// Calculate indicator probabilities (inlier and outlier)
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Vector p_inlier_outlier = calcIndicatorProb(x);
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double p_inlier = p_inlier_outlier[0];
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double p_outlier = p_inlier_outlier[1];
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Vector err_wh_inlier = model_inlier_->whiten(err);
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Vector err_wh_outlier = model_outlier_->whiten(err);
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Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
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Matrix invCov_outlier = model_outlier_->R().transpose()
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* model_outlier_->R();
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Vector err_wh_eq;
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err_wh_eq.resize(err_wh_inlier.rows() * 2);
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err_wh_eq << sqrt(p_inlier) * err_wh_inlier.array(), sqrt(p_outlier)
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* err_wh_outlier.array();
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if (H) {
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// stack Jacobians for the two indicators for each of the key
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Matrix H1_inlier = sqrt(p_inlier) * model_inlier_->Whiten(H1);
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Matrix H1_outlier = sqrt(p_outlier) * model_outlier_->Whiten(H1);
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Matrix H1_aug = gtsam::stack(2, &H1_inlier, &H1_outlier);
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Matrix H2_inlier = sqrt(p_inlier) * model_inlier_->Whiten(H2);
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Matrix H2_outlier = sqrt(p_outlier) * model_outlier_->Whiten(H2);
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Matrix H2_aug = gtsam::stack(2, &H2_inlier, &H2_outlier);
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(*H)[0].resize(H1_aug.rows(), H1_aug.cols());
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(*H)[1].resize(H2_aug.rows(), H2_aug.cols());
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(*H)[0] = H1_aug;
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(*H)[1] = H2_aug;
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}
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if (debug) {
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// std::cout<<"unwhitened error: "<<err[0]<<" "<<err[1]<<" "<<err[2]<<std::endl;
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// std::cout<<"err_wh_inlier: "<<err_wh_inlier[0]<<" "<<err_wh_inlier[1]<<" "<<err_wh_inlier[2]<<std::endl;
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// std::cout<<"err_wh_outlier: "<<err_wh_outlier[0]<<" "<<err_wh_outlier[1]<<" "<<err_wh_outlier[2]<<std::endl;
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//
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// std::cout<<"p_inlier, p_outlier, sumP: "<<p_inlier<<" "<<p_outlier<<" " << sumP << std::endl;
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//
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// std::cout<<"prior_inlier_, prior_outlier_: "<<prior_inlier_<<" "<<prior_outlier_<< std::endl;
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//
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// double s_inl = -0.5 * err_wh_inlier.dot(err_wh_inlier);
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// double s_outl = -0.5 * err_wh_outlier.dot(err_wh_outlier);
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// std::cout<<"s_inl, s_outl: "<<s_inl<<" "<<s_outl<<std::endl;
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//
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// std::cout<<"norm of invCov_inlier, invCov_outlier: "<<invCov_inlier.norm()<<" "<<invCov_outlier.norm()<<std::endl;
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// double q_inl = invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) );
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// double q_outl = invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) );
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// std::cout<<"q_inl, q_outl: "<<q_inl<<" "<<q_outl<<std::endl;
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// Matrix Cov_inlier = invCov_inlier.inverse();
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// Matrix Cov_outlier = invCov_outlier.inverse();
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// std::cout<<"Cov_inlier: "<<std::endl<<
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// Cov_inlier(0,0) << " " << Cov_inlier(0,1) << " " << Cov_inlier(0,2) <<std::endl<<
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// Cov_inlier(1,0) << " " << Cov_inlier(1,1) << " " << Cov_inlier(1,2) <<std::endl<<
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// Cov_inlier(2,0) << " " << Cov_inlier(2,1) << " " << Cov_inlier(2,2) <<std::endl;
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// std::cout<<"Cov_outlier: "<<std::endl<<
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// Cov_outlier(0,0) << " " << Cov_outlier(0,1) << " " << Cov_outlier(0,2) <<std::endl<<
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// Cov_outlier(1,0) << " " << Cov_outlier(1,1) << " " << Cov_outlier(1,2) <<std::endl<<
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// Cov_outlier(2,0) << " " << Cov_outlier(2,1) << " " << Cov_outlier(2,2) <<std::endl;
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// std::cout<<"===="<<std::endl;
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}
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return err_wh_eq;
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}
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/* ************************************************************************* */
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gtsam::Vector calcIndicatorProb(const gtsam::Values& x) const {
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bool debug = false;
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Vector err = unwhitenedError(x);
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// Calculate indicator probabilities (inlier and outlier)
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Vector err_wh_inlier = model_inlier_->whiten(err);
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Vector err_wh_outlier = model_outlier_->whiten(err);
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Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
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Matrix invCov_outlier = model_outlier_->R().transpose()
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* model_outlier_->R();
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double p_inlier = prior_inlier_ * std::sqrt(invCov_inlier.determinant())
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* exp(-0.5 * err_wh_inlier.dot(err_wh_inlier));
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double p_outlier = prior_outlier_ * std::sqrt(invCov_outlier.determinant())
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* exp(-0.5 * err_wh_outlier.dot(err_wh_outlier));
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if (debug) {
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std::cout << "in calcIndicatorProb. err_unwh: " << err[0] << ", "
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<< err[1] << ", " << err[2] << std::endl;
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std::cout << "in calcIndicatorProb. err_wh_inlier: " << err_wh_inlier[0]
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<< ", " << err_wh_inlier[1] << ", " << err_wh_inlier[2] << std::endl;
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std::cout << "in calcIndicatorProb. err_wh_inlier.dot(err_wh_inlier): "
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<< err_wh_inlier.dot(err_wh_inlier) << std::endl;
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std::cout << "in calcIndicatorProb. err_wh_outlier.dot(err_wh_outlier): "
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<< err_wh_outlier.dot(err_wh_outlier) << std::endl;
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std::cout
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<< "in calcIndicatorProb. p_inlier, p_outlier before normalization: "
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<< p_inlier << ", " << p_outlier << std::endl;
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}
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double sumP = p_inlier + p_outlier;
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p_inlier /= sumP;
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p_outlier /= sumP;
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if (flag_bump_up_near_zero_probs_) {
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// Bump up near-zero probabilities (as in linerFlow.h)
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double minP = 0.05; // == 0.1 / 2 indicator variables
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if (p_inlier < minP || p_outlier < minP) {
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if (p_inlier < minP)
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p_inlier = minP;
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if (p_outlier < minP)
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p_outlier = minP;
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sumP = p_inlier + p_outlier;
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p_inlier /= sumP;
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p_outlier /= sumP;
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}
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}
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return (Vector(2) << p_inlier, p_outlier).finished();
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}
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/* ************************************************************************* */
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gtsam::Vector unwhitenedError(const gtsam::Values& x) const {
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const T& p1 = x.at<T>(key1_);
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const T& p2 = x.at<T>(key2_);
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Matrix H1, H2;
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T hx = p1.between(p2, H1, H2); // h(x)
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return measured_.localCoordinates(hx);
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}
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/* ************************************************************************* */
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void set_flag_bump_up_near_zero_probs(bool flag) {
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flag_bump_up_near_zero_probs_ = flag;
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}
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/* ************************************************************************* */
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bool get_flag_bump_up_near_zero_probs() const {
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return flag_bump_up_near_zero_probs_;
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}
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/* ************************************************************************* */
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SharedGaussian get_model_inlier() const {
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return model_inlier_;
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}
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/* ************************************************************************* */
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SharedGaussian get_model_outlier() const {
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return model_outlier_;
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}
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/* ************************************************************************* */
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Matrix get_model_inlier_cov() const {
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return (model_inlier_->R().transpose() * model_inlier_->R()).inverse();
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}
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/* ************************************************************************* */
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Matrix get_model_outlier_cov() const {
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return (model_outlier_->R().transpose() * model_outlier_->R()).inverse();
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}
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/* ************************************************************************* */
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void updateNoiseModels(const gtsam::Values& values,
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const gtsam::NonlinearFactorGraph& graph) {
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/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
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* (note these are given in the E step, where indicator probabilities are calculated).
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*
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* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
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* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
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*
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* TODO: improve efficiency (info form)
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*/
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// get joint covariance of the involved states
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std::vector<gtsam::Key> Keys;
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Keys.push_back(key1_);
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Keys.push_back(key2_);
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Marginals marginals(graph, values, Marginals::QR);
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JointMarginal joint_marginal12 = marginals.jointMarginalCovariance(Keys);
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Matrix cov1 = joint_marginal12(key1_, key1_);
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Matrix cov2 = joint_marginal12(key2_, key2_);
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Matrix cov12 = joint_marginal12(key1_, key2_);
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updateNoiseModels_givenCovs(values, cov1, cov2, cov12);
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}
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/* ************************************************************************* */
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void updateNoiseModels_givenCovs(const gtsam::Values& values,
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const Matrix& cov1, const Matrix& cov2, const Matrix& cov12) {
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/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
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* (note these are given in the E step, where indicator probabilities are calculated).
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*
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* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
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* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
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*
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* TODO: improve efficiency (info form)
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*/
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const T& p1 = values.at<T>(key1_);
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const T& p2 = values.at<T>(key2_);
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Matrix H1, H2;
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p1.between(p2, H1, H2); // h(x)
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Matrix H;
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H.resize(H1.rows(), H1.rows() + H2.rows());
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H << H1, H2; // H = [H1 H2]
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Matrix joint_cov;
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joint_cov.resize(cov1.rows() + cov2.rows(), cov1.cols() + cov2.cols());
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joint_cov << cov1, cov12, cov12.transpose(), cov2;
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Matrix cov_state = H * joint_cov * H.transpose();
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// model_inlier_->print("before:");
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// update inlier and outlier noise models
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Matrix covRinlier =
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(model_inlier_->R().transpose() * model_inlier_->R()).inverse();
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model_inlier_ = gtsam::noiseModel::Gaussian::Covariance(
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covRinlier + cov_state);
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Matrix covRoutlier =
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(model_outlier_->R().transpose() * model_outlier_->R()).inverse();
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model_outlier_ = gtsam::noiseModel::Gaussian::Covariance(
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covRoutlier + cov_state);
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// model_inlier_->print("after:");
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// std::cout<<"covRinlier + cov_state: "<<covRinlier + cov_state<<std::endl;
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}
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/* ************************************************************************* */
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/** return the measured */
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const VALUE& measured() const {
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return measured_;
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}
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/** number of variables attached to this factor */
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std::size_t size() const {
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return 2;
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}
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virtual size_t dim() const {
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return model_inlier_->R().rows() + model_inlier_->R().cols();
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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
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& boost::serialization::make_nvp("NonlinearFactor",
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boost::serialization::base_object<Base>(*this));
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ar & BOOST_SERIALIZATION_NVP(measured_);
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}
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};
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// \class BetweenFactorEM
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}/// namespace gtsam
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