437 lines
15 KiB
C++
437 lines
15 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>
|
|
#include <gtsam/nonlinear/Marginals.h>
|
|
|
|
namespace gtsam {
|
|
|
|
/**
|
|
* A class for a measurement predicted by "between(config[key1],config[key2])"
|
|
* @tparam VALUE the Value type
|
|
* @ingroup slam
|
|
*/
|
|
template<class VALUE>
|
|
class BetweenFactorEM: public NonlinearFactor {
|
|
|
|
public:
|
|
|
|
typedef VALUE T;
|
|
|
|
private:
|
|
|
|
typedef BetweenFactorEM<VALUE> This;
|
|
typedef NonlinearFactor Base;
|
|
|
|
Key key1_;
|
|
Key key2_;
|
|
|
|
VALUE measured_; /** The measurement */
|
|
|
|
SharedGaussian model_inlier_;
|
|
SharedGaussian model_outlier_;
|
|
|
|
double prior_inlier_;
|
|
double prior_outlier_;
|
|
|
|
bool flag_bump_up_near_zero_probs_;
|
|
|
|
/** 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 std::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,
|
|
const bool flag_bump_up_near_zero_probs = false) :
|
|
Base(KeyVector{key1, key2}), key1_(key1), key2_(key2), measured_(
|
|
measured), model_inlier_(model_inlier), model_outlier_(model_outlier), prior_inlier_(
|
|
prior_inlier), prior_outlier_(prior_outlier), flag_bump_up_near_zero_probs_(
|
|
flag_bump_up_near_zero_probs) {
|
|
}
|
|
|
|
~BetweenFactorEM() override {
|
|
}
|
|
|
|
/** implement functions needed for Testable */
|
|
|
|
/** print */
|
|
void print(const std::string& s, const KeyFormatter& keyFormatter =
|
|
DefaultKeyFormatter) const override {
|
|
std::cout << s << "BetweenFactorEM(" << keyFormatter(key1_) << ","
|
|
<< keyFormatter(key2_) << ")\n";
|
|
measured_.print(" measured: ");
|
|
model_inlier_->print(" noise model inlier: ");
|
|
model_outlier_->print(" noise model outlier: ");
|
|
std::cout << "(prior_inlier, prior_outlier_) = (" << prior_inlier_ << ","
|
|
<< prior_outlier_ << ")\n";
|
|
// Base::print(s, keyFormatter);
|
|
}
|
|
|
|
/** equals */
|
|
bool equals(const NonlinearFactor& f, double tol = 1e-9) const override {
|
|
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 */
|
|
|
|
/* ************************************************************************* */
|
|
double error(const Values &x) const override {
|
|
return whitenedError(x).squaredNorm();
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
/**
|
|
* Linearize a non-linearFactorN to get a 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 */
|
|
std::shared_ptr<GaussianFactor> linearize(const Values &x) const override {
|
|
// Only linearize if the factor is active
|
|
if (!this->active(x))
|
|
return std::shared_ptr<JacobianFactor>();
|
|
|
|
//std::cout<<"About to linearize"<<std::endl;
|
|
Matrix A1, A2;
|
|
std::vector<Matrix> A(this->size());
|
|
Vector b = -whitenedError(x, A);
|
|
A1 = A[0];
|
|
A2 = A[1];
|
|
|
|
return GaussianFactor::shared_ptr(
|
|
new JacobianFactor(key1_, A1, key2_, A2, b,
|
|
noiseModel::Unit::Create(b.size())));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
Vector whitenedError(const Values& x,
|
|
OptionalMatrixVecType H = nullptr) 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 p_inlier_outlier = calcIndicatorProb(x);
|
|
double p_inlier = p_inlier_outlier[0];
|
|
double p_outlier = p_inlier_outlier[1];
|
|
|
|
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();
|
|
|
|
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 = 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 = 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;
|
|
}
|
|
|
|
// A function overload that takes a vector of matrices and passes it to the
|
|
// function above which uses a pointer to a vector instead.
|
|
Vector whitenedError(const Values& x, std::vector<Matrix>& H) const {
|
|
return whitenedError(x, &H);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
Vector calcIndicatorProb(const Values& x) const {
|
|
|
|
bool debug = false;
|
|
|
|
Vector err = unwhitenedError(x);
|
|
|
|
// 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_ * std::sqrt(invCov_inlier.determinant())
|
|
* exp(-0.5 * err_wh_inlier.dot(err_wh_inlier));
|
|
double p_outlier = prior_outlier_ * std::sqrt(invCov_outlier.determinant())
|
|
* exp(-0.5 * err_wh_outlier.dot(err_wh_outlier));
|
|
|
|
if (debug) {
|
|
std::cout << "in calcIndicatorProb. err_unwh: " << err[0] << ", "
|
|
<< err[1] << ", " << err[2] << std::endl;
|
|
std::cout << "in calcIndicatorProb. err_wh_inlier: " << err_wh_inlier[0]
|
|
<< ", " << err_wh_inlier[1] << ", " << err_wh_inlier[2] << std::endl;
|
|
std::cout << "in calcIndicatorProb. err_wh_inlier.dot(err_wh_inlier): "
|
|
<< err_wh_inlier.dot(err_wh_inlier) << std::endl;
|
|
std::cout << "in calcIndicatorProb. err_wh_outlier.dot(err_wh_outlier): "
|
|
<< err_wh_outlier.dot(err_wh_outlier) << std::endl;
|
|
|
|
std::cout
|
|
<< "in calcIndicatorProb. p_inlier, p_outlier before normalization: "
|
|
<< p_inlier << ", " << p_outlier << std::endl;
|
|
}
|
|
|
|
double sumP = p_inlier + p_outlier;
|
|
p_inlier /= sumP;
|
|
p_outlier /= sumP;
|
|
|
|
if (flag_bump_up_near_zero_probs_) {
|
|
// Bump up near-zero probabilities (as in linerFlow.h)
|
|
double minP = 0.05; // == 0.1 / 2 indicator variables
|
|
if (p_inlier < minP || p_outlier < minP) {
|
|
if (p_inlier < minP)
|
|
p_inlier = minP;
|
|
if (p_outlier < minP)
|
|
p_outlier = minP;
|
|
sumP = p_inlier + p_outlier;
|
|
p_inlier /= sumP;
|
|
p_outlier /= sumP;
|
|
}
|
|
}
|
|
|
|
return (Vector(2) << p_inlier, p_outlier).finished();
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
Vector unwhitenedError(const Values& x) 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)
|
|
|
|
return measured_.localCoordinates(hx);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
void set_flag_bump_up_near_zero_probs(bool flag) {
|
|
flag_bump_up_near_zero_probs_ = flag;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
bool get_flag_bump_up_near_zero_probs() const {
|
|
return flag_bump_up_near_zero_probs_;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
SharedGaussian get_model_inlier() const {
|
|
return model_inlier_;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
SharedGaussian get_model_outlier() const {
|
|
return model_outlier_;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
Matrix get_model_inlier_cov() const {
|
|
return (model_inlier_->R().transpose() * model_inlier_->R()).inverse();
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
Matrix get_model_outlier_cov() const {
|
|
return (model_outlier_->R().transpose() * model_outlier_->R()).inverse();
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
void updateNoiseModels(const Values& values,
|
|
const NonlinearFactorGraph& graph) {
|
|
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
|
|
* (note these are given in the E step, where indicator probabilities are calculated).
|
|
*
|
|
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
|
|
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
|
|
*
|
|
* TODO: improve efficiency (info form)
|
|
*/
|
|
|
|
// get joint covariance of the involved states
|
|
KeyVector Keys;
|
|
Keys.push_back(key1_);
|
|
Keys.push_back(key2_);
|
|
Marginals marginals(graph, values, Marginals::QR);
|
|
JointMarginal joint_marginal12 = marginals.jointMarginalCovariance(Keys);
|
|
Matrix cov1 = joint_marginal12(key1_, key1_);
|
|
Matrix cov2 = joint_marginal12(key2_, key2_);
|
|
Matrix cov12 = joint_marginal12(key1_, key2_);
|
|
|
|
updateNoiseModels_givenCovs(values, cov1, cov2, cov12);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
void updateNoiseModels_givenCovs(const Values& values,
|
|
const Matrix& cov1, const Matrix& cov2, const Matrix& cov12) {
|
|
/* Update model_inlier_ and model_outlier_ to account for uncertainty in robot trajectories
|
|
* (note these are given in the E step, where indicator probabilities are calculated).
|
|
*
|
|
* Principle: R += [H1 H2] * joint_cov12 * [H1 H2]', where H1, H2 are Jacobians of the
|
|
* unwhitened error w.r.t. states, and R is the measurement covariance (inlier or outlier modes).
|
|
*
|
|
* TODO: improve efficiency (info form)
|
|
*/
|
|
|
|
const T& p1 = values.at<T>(key1_);
|
|
const T& p2 = values.at<T>(key2_);
|
|
|
|
Matrix H1, H2;
|
|
p1.between(p2, H1, H2); // h(x)
|
|
|
|
Matrix H;
|
|
H.resize(H1.rows(), H1.rows() + H2.rows());
|
|
H << H1, H2; // H = [H1 H2]
|
|
|
|
Matrix joint_cov;
|
|
joint_cov.resize(cov1.rows() + cov2.rows(), cov1.cols() + cov2.cols());
|
|
joint_cov << cov1, cov12, cov12.transpose(), cov2;
|
|
|
|
Matrix cov_state = H * joint_cov * H.transpose();
|
|
|
|
// model_inlier_->print("before:");
|
|
|
|
// update inlier and outlier noise models
|
|
Matrix covRinlier =
|
|
(model_inlier_->R().transpose() * model_inlier_->R()).inverse();
|
|
model_inlier_ = noiseModel::Gaussian::Covariance(
|
|
covRinlier + cov_state);
|
|
|
|
Matrix covRoutlier =
|
|
(model_outlier_->R().transpose() * model_outlier_->R()).inverse();
|
|
model_outlier_ = noiseModel::Gaussian::Covariance(
|
|
covRoutlier + cov_state);
|
|
|
|
// model_inlier_->print("after:");
|
|
// std::cout<<"covRinlier + cov_state: "<<covRinlier + cov_state<<std::endl;
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
/** return the measured */
|
|
const VALUE& measured() const {
|
|
return measured_;
|
|
}
|
|
|
|
size_t dim() const override {
|
|
return model_inlier_->R().rows() + model_inlier_->R().cols();
|
|
}
|
|
|
|
private:
|
|
|
|
#if GTSAM_ENABLE_BOOST_SERIALIZATION
|
|
/** 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_);
|
|
}
|
|
#endif
|
|
};
|
|
// \class BetweenFactorEM
|
|
|
|
/// traits
|
|
template<class VALUE>
|
|
struct traits<BetweenFactorEM<VALUE> > : public Testable<BetweenFactorEM<VALUE> > {};
|
|
|
|
} // namespace gtsam
|