Added BetweenFactorEM with 2 indicator variables.
Vadim Indelman
parent
c2f5adc7d0
commit
d33f435eab
|
|
@ -13,6 +13,7 @@ virtual class gtsam::Point3;
|
|||
virtual class gtsam::Rot3;
|
||||
virtual class gtsam::Pose3;
|
||||
virtual class gtsam::noiseModel::Base;
|
||||
virtual class gtsam::noiseModel::Gaussian;
|
||||
virtual class gtsam::imuBias::ConstantBias;
|
||||
virtual class gtsam::NonlinearFactor;
|
||||
virtual class gtsam::GaussianFactor;
|
||||
|
|
@ -308,6 +309,15 @@ virtual class BetweenFactor : gtsam::NonlinearFactor {
|
|||
void serializable() const; // enabling serialization functionality
|
||||
};
|
||||
|
||||
//#include <gtsam_unstable/slam/BetweenFactorEM.h>
|
||||
//template<T = {gtsam::PoseRTV}>
|
||||
//virtual class BetweenFactorEM : gtsam::NonlinearFactor {
|
||||
// BetweenFactorEM(size_t key1, size_t key2, const T& relativePose,
|
||||
// const gtsam::noiseModel::Gaussian* model_inlier, const gtsam::noiseModel::Gaussian* model_outlier,
|
||||
// double prior_inlier, double prior_outlier);
|
||||
//
|
||||
// void serializable() const; // enabling serialization functionality
|
||||
//};
|
||||
|
||||
#include <gtsam/slam/RangeFactor.h>
|
||||
template<POSE, POINT>
|
||||
|
|
|
|||
|
|
@ -0,0 +1,276 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* 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
|
||||
|
|
@ -0,0 +1,477 @@
|
|||
/**
|
||||
* @file testBetweenFactorEM.cpp
|
||||
* @brief Unit test for the BetweenFactorEM
|
||||
* @author Vadim Indelman
|
||||
*/
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
|
||||
|
||||
#include <gtsam_unstable/slam/BetweenFactorEM.h>
|
||||
#include <gtsam/geometry/Pose2.h>
|
||||
#include <gtsam/nonlinear/Values.h>
|
||||
#include <gtsam/base/LieVector.h>
|
||||
#include <gtsam/base/numericalDerivative.h>
|
||||
|
||||
#include <gtsam/slam/BetweenFactor.h>
|
||||
|
||||
//#include <gtsam/nonlinear/NonlinearOptimizer.h>
|
||||
//#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
||||
//#include <gtsam/linear/GaussianSequentialSolver.h>
|
||||
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
LieVector predictionError(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactorEM<gtsam::Pose2>& factor){
|
||||
gtsam::Values values;
|
||||
values.insert(key1, p1);
|
||||
values.insert(key2, p2);
|
||||
// LieVector err = factor.whitenedError(values);
|
||||
// return err;
|
||||
return LieVector::Expmap(factor.whitenedError(values));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
LieVector predictionError_standard(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactor<gtsam::Pose2>& factor){
|
||||
gtsam::Values values;
|
||||
values.insert(key1, p1);
|
||||
values.insert(key2, p2);
|
||||
// LieVector err = factor.whitenedError(values);
|
||||
// return err;
|
||||
return LieVector::Expmap(factor.whitenedError(values));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( BetweenFactorEM, ConstructorAndEquals)
|
||||
{
|
||||
gtsam::Key key1(1);
|
||||
gtsam::Key key2(2);
|
||||
|
||||
gtsam::Pose2 p1(10.0, 15.0, 0.1);
|
||||
gtsam::Pose2 p2(15.0, 15.0, 0.3);
|
||||
gtsam::Pose2 noise(0.5, 0.4, 0.01);
|
||||
gtsam::Pose2 rel_pose_ideal = p1.between(p2);
|
||||
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise);
|
||||
|
||||
SharedGaussian model_inlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 0.5, 0.5, 0.05)));
|
||||
SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 5, 5, 1.0)));
|
||||
|
||||
double prior_outlier = 0.5;
|
||||
double prior_inlier = 0.5;
|
||||
|
||||
// Constructor
|
||||
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
|
||||
// Equals
|
||||
CHECK(assert_equal(f, g, 1e-5));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( BetweenFactorEM, EvaluateError)
|
||||
{
|
||||
gtsam::Key key1(1);
|
||||
gtsam::Key key2(2);
|
||||
|
||||
// Inlier test
|
||||
gtsam::Pose2 p1(10.0, 15.0, 0.1);
|
||||
gtsam::Pose2 p2(15.0, 15.0, 0.3);
|
||||
gtsam::Pose2 noise(0.5, 0.4, 0.01);
|
||||
gtsam::Pose2 rel_pose_ideal = p1.between(p2);
|
||||
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise);
|
||||
|
||||
SharedGaussian model_inlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 0.5, 0.5, 0.05)));
|
||||
SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 50.0, 50.0, 10.0)));
|
||||
|
||||
gtsam::Values values;
|
||||
values.insert(key1, p1);
|
||||
values.insert(key2, p2);
|
||||
|
||||
double prior_outlier = 0.5;
|
||||
double prior_inlier = 0.5;
|
||||
|
||||
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
|
||||
Vector actual_err_wh = f.whitenedError(values);
|
||||
|
||||
Vector actual_err_wh_inlier = Vector_(3, actual_err_wh[0], actual_err_wh[1], actual_err_wh[2]);
|
||||
Vector actual_err_wh_outlier = Vector_(3, actual_err_wh[3], actual_err_wh[4], actual_err_wh[5]);
|
||||
|
||||
// in case of inlier, inlier-mode whitented error should be dominant
|
||||
assert(actual_err_wh_inlier.norm() > 1000.0*actual_err_wh_outlier.norm());
|
||||
|
||||
cout << "Inlier test. norm of actual_err_wh_inlier, actual_err_wh_outlier: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier.norm()<<endl;
|
||||
cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl;
|
||||
|
||||
|
||||
// Outlier test
|
||||
noise = gtsam::Pose2(10.5, 20.4, 2.01);
|
||||
gtsam::Pose2 rel_pose_msr_test2 = rel_pose_ideal.compose(noise);
|
||||
|
||||
BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr_test2, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
|
||||
actual_err_wh = g.whitenedError(values);
|
||||
|
||||
actual_err_wh_inlier = Vector_(3, actual_err_wh[0], actual_err_wh[1], actual_err_wh[2]);
|
||||
actual_err_wh_outlier = Vector_(3, actual_err_wh[3], actual_err_wh[4], actual_err_wh[5]);
|
||||
|
||||
// in case of outlier, outlier-mode whitented error should be dominant
|
||||
assert(actual_err_wh_inlier.norm() < 1000.0*actual_err_wh_outlier.norm());
|
||||
|
||||
cout << "Outlier test. norm of actual_err_wh_inlier, actual_err_wh_outlier: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier<<endl;
|
||||
cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl;
|
||||
|
||||
// Compare with standard between factor for the inlier case
|
||||
prior_outlier = 0.0;
|
||||
prior_inlier = 1.0;
|
||||
BetweenFactorEM<gtsam::Pose2> h_EM(key1, key2, rel_pose_msr, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
actual_err_wh = h_EM.whitenedError(values);
|
||||
actual_err_wh_inlier = Vector_(3, actual_err_wh[0], actual_err_wh[1], actual_err_wh[2]);
|
||||
|
||||
BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier );
|
||||
Vector actual_err_wh_stnd = h.whitenedError(values);
|
||||
|
||||
cout<<"actual_err_wh: "<<actual_err_wh_inlier[0]<<", "<<actual_err_wh_inlier[1]<<", "<<actual_err_wh_inlier[2]<<endl;
|
||||
cout<<"actual_err_wh_stnd: "<<actual_err_wh_stnd[0]<<", "<<actual_err_wh_stnd[1]<<", "<<actual_err_wh_stnd[2]<<endl;
|
||||
|
||||
CHECK( assert_equal(actual_err_wh_inlier, actual_err_wh_stnd, 1e-8));
|
||||
}
|
||||
|
||||
///* ************************************************************************** */
|
||||
TEST (BetweenFactorEM, jacobian ) {
|
||||
|
||||
gtsam::Key key1(1);
|
||||
gtsam::Key key2(2);
|
||||
|
||||
// Inlier test
|
||||
gtsam::Pose2 p1(10.0, 15.0, 0.1);
|
||||
gtsam::Pose2 p2(15.0, 15.0, 0.3);
|
||||
gtsam::Pose2 noise(0.5, 0.4, 0.01);
|
||||
gtsam::Pose2 rel_pose_ideal = p1.between(p2);
|
||||
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise);
|
||||
|
||||
SharedGaussian model_inlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 0.5, 0.5, 0.05)));
|
||||
SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 50.0, 50.0, 10.0)));
|
||||
|
||||
gtsam::Values values;
|
||||
values.insert(key1, p1);
|
||||
values.insert(key2, p2);
|
||||
|
||||
double prior_outlier = 0.0;
|
||||
double prior_inlier = 1.0;
|
||||
|
||||
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
|
||||
prior_inlier, prior_outlier);
|
||||
|
||||
std::vector<gtsam::Matrix> H_actual(2);
|
||||
Vector actual_err_wh = f.whitenedError(values, H_actual);
|
||||
|
||||
Matrix H1_actual = H_actual[0];
|
||||
Matrix H2_actual = H_actual[1];
|
||||
|
||||
// compare to standard between factor
|
||||
BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier );
|
||||
Vector actual_err_wh_stnd = h.whitenedError(values);
|
||||
Vector actual_err_wh_inlier = Vector_(3, actual_err_wh[0], actual_err_wh[1], actual_err_wh[2]);
|
||||
CHECK( assert_equal(actual_err_wh_stnd, actual_err_wh_inlier, 1e-8));
|
||||
std::vector<gtsam::Matrix> H_actual_stnd_unwh(2);
|
||||
(void)h.unwhitenedError(values, H_actual_stnd_unwh);
|
||||
Matrix H1_actual_stnd_unwh = H_actual_stnd_unwh[0];
|
||||
Matrix H2_actual_stnd_unwh = H_actual_stnd_unwh[1];
|
||||
Matrix H1_actual_stnd = model_inlier->Whiten(H1_actual_stnd_unwh);
|
||||
Matrix H2_actual_stnd = model_inlier->Whiten(H2_actual_stnd_unwh);
|
||||
// CHECK( assert_equal(H1_actual_stnd, H1_actual, 1e-8));
|
||||
// CHECK( assert_equal(H2_actual_stnd, H2_actual, 1e-8));
|
||||
|
||||
double stepsize = 1.0e-9;
|
||||
Matrix H1_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, _1, p2, key1, key2, f), p1, stepsize);
|
||||
Matrix H2_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, p1, _1, key1, key2, f), p2, stepsize);
|
||||
|
||||
|
||||
// try to check numerical derivatives of a standard between factor
|
||||
Matrix H1_expected_stnd = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError_standard, _1, p2, key1, key2, h), p1, stepsize);
|
||||
CHECK( assert_equal(H1_expected_stnd, H1_actual_stnd, 1e-5));
|
||||
|
||||
|
||||
CHECK( assert_equal(H1_expected, H1_actual, 1e-8));
|
||||
CHECK( assert_equal(H2_expected, H2_actual, 1e-8));
|
||||
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( InertialNavFactor, Equals)
|
||||
{
|
||||
// gtsam::Key Pose1(11);
|
||||
// gtsam::Key Pose2(12);
|
||||
// gtsam::Key Vel1(21);
|
||||
// gtsam::Key Vel2(22);
|
||||
// gtsam::Key Bias1(31);
|
||||
//
|
||||
// Vector measurement_acc(Vector_(3,0.1,0.2,0.4));
|
||||
// Vector measurement_gyro(Vector_(3, -0.2, 0.5, 0.03));
|
||||
//
|
||||
// double measurement_dt(0.1);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> g(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
// CHECK(assert_equal(f, g, 1e-5));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( InertialNavFactor, Predict)
|
||||
{
|
||||
// gtsam::Key PoseKey1(11);
|
||||
// gtsam::Key PoseKey2(12);
|
||||
// gtsam::Key VelKey1(21);
|
||||
// gtsam::Key VelKey2(22);
|
||||
// gtsam::Key BiasKey1(31);
|
||||
//
|
||||
// double measurement_dt(0.1);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
//
|
||||
// // First test: zero angular motion, some acceleration
|
||||
// Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81));
|
||||
// Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
//
|
||||
// Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
|
||||
// LieVector Vel1(3, 0.50, -0.50, 0.40);
|
||||
// imuBias::ConstantBias Bias1;
|
||||
// Pose3 expectedPose2(Rot3(), Point3(2.05, 0.95, 3.04));
|
||||
// LieVector expectedVel2(3, 0.51, -0.48, 0.43);
|
||||
// Pose3 actualPose2;
|
||||
// LieVector actualVel2;
|
||||
// f.predict(Pose1, Vel1, Bias1, actualPose2, actualVel2);
|
||||
//
|
||||
// CHECK(assert_equal(expectedPose2, actualPose2, 1e-5));
|
||||
// CHECK(assert_equal(expectedVel2, actualVel2, 1e-5));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( InertialNavFactor, ErrorPosVel)
|
||||
{
|
||||
// gtsam::Key PoseKey1(11);
|
||||
// gtsam::Key PoseKey2(12);
|
||||
// gtsam::Key VelKey1(21);
|
||||
// gtsam::Key VelKey2(22);
|
||||
// gtsam::Key BiasKey1(31);
|
||||
//
|
||||
// double measurement_dt(0.1);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
//
|
||||
// // First test: zero angular motion, some acceleration
|
||||
// Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81));
|
||||
// Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
//
|
||||
// Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
|
||||
// Pose3 Pose2(Rot3(), Point3(2.05, 0.95, 3.04));
|
||||
// LieVector Vel1(3, 0.50, -0.50, 0.40);
|
||||
// LieVector Vel2(3, 0.51, -0.48, 0.43);
|
||||
// imuBias::ConstantBias Bias1;
|
||||
//
|
||||
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
|
||||
// Vector ExpectedErr(zero(9));
|
||||
//
|
||||
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( InertialNavFactor, ErrorRot)
|
||||
{
|
||||
// gtsam::Key PoseKey1(11);
|
||||
// gtsam::Key PoseKey2(12);
|
||||
// gtsam::Key VelKey1(21);
|
||||
// gtsam::Key VelKey2(22);
|
||||
// gtsam::Key BiasKey1(31);
|
||||
//
|
||||
// double measurement_dt(0.1);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
// // Second test: zero angular motion, some acceleration
|
||||
// Vector measurement_acc(Vector_(3,0.0,0.0,0.0-9.81));
|
||||
// Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
//
|
||||
// Pose3 Pose1(Rot3(), Point3(2.0,1.0,3.0));
|
||||
// Pose3 Pose2(Rot3::Expmap(measurement_gyro*measurement_dt), Point3(2.0,1.0,3.0));
|
||||
// LieVector Vel1(3,0.0,0.0,0.0);
|
||||
// LieVector Vel2(3,0.0,0.0,0.0);
|
||||
// imuBias::ConstantBias Bias1;
|
||||
//
|
||||
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
|
||||
// Vector ExpectedErr(zero(9));
|
||||
//
|
||||
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( InertialNavFactor, ErrorRotPosVel)
|
||||
{
|
||||
// gtsam::Key PoseKey1(11);
|
||||
// gtsam::Key PoseKey2(12);
|
||||
// gtsam::Key VelKey1(21);
|
||||
// gtsam::Key VelKey2(22);
|
||||
// gtsam::Key BiasKey1(31);
|
||||
//
|
||||
// double measurement_dt(0.1);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
// // Second test: zero angular motion, some acceleration - generated in matlab
|
||||
// Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343));
|
||||
// Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
//
|
||||
// Rot3 R1(0.487316618, 0.125253866, 0.86419557,
|
||||
// 0.580273724, 0.693095498, -0.427669306,
|
||||
// -0.652537293, 0.709880342, 0.265075427);
|
||||
// Point3 t1(2.0,1.0,3.0);
|
||||
// Pose3 Pose1(R1, t1);
|
||||
// LieVector Vel1(3,0.5,-0.5,0.4);
|
||||
// Rot3 R2(0.473618898, 0.119523052, 0.872582019,
|
||||
// 0.609241153, 0.67099888, -0.422594037,
|
||||
// -0.636011287, 0.731761397, 0.244979388);
|
||||
// Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
|
||||
// Pose3 Pose2(R2, t2);
|
||||
// LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
|
||||
// imuBias::ConstantBias Bias1;
|
||||
//
|
||||
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
|
||||
// Vector ExpectedErr(zero(9));
|
||||
//
|
||||
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
|
||||
}
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST (InertialNavFactor, Jacobian ) {
|
||||
|
||||
// gtsam::Key PoseKey1(11);
|
||||
// gtsam::Key PoseKey2(12);
|
||||
// gtsam::Key VelKey1(21);
|
||||
// gtsam::Key VelKey2(22);
|
||||
// gtsam::Key BiasKey1(31);
|
||||
//
|
||||
// double measurement_dt(0.01);
|
||||
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81));
|
||||
// Vector world_rho(Vector_(3, 0.0, -1.5724e-05, 0.0)); // NED system
|
||||
// gtsam::Vector ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
|
||||
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
|
||||
//
|
||||
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
|
||||
//
|
||||
// Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343));
|
||||
// Vector measurement_gyro(Vector_(3, 3.14, 3.14/2, -3.14));
|
||||
//
|
||||
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> factor(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
|
||||
//
|
||||
// Rot3 R1(0.487316618, 0.125253866, 0.86419557,
|
||||
// 0.580273724, 0.693095498, -0.427669306,
|
||||
// -0.652537293, 0.709880342, 0.265075427);
|
||||
// Point3 t1(2.0,1.0,3.0);
|
||||
// Pose3 Pose1(R1, t1);
|
||||
// LieVector Vel1(3,0.5,-0.5,0.4);
|
||||
// Rot3 R2(0.473618898, 0.119523052, 0.872582019,
|
||||
// 0.609241153, 0.67099888, -0.422594037,
|
||||
// -0.636011287, 0.731761397, 0.244979388);
|
||||
// Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
|
||||
// Pose3 Pose2(R2, t2);
|
||||
// LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
|
||||
// imuBias::ConstantBias Bias1;
|
||||
//
|
||||
// Matrix H1_actual, H2_actual, H3_actual, H4_actual, H5_actual;
|
||||
//
|
||||
// Vector ActualErr(factor.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2, H1_actual, H2_actual, H3_actual, H4_actual, H5_actual));
|
||||
//
|
||||
// // Checking for Pose part in the jacobians
|
||||
// // ******
|
||||
// Matrix H1_actualPose(H1_actual.block(0,0,6,H1_actual.cols()));
|
||||
// Matrix H2_actualPose(H2_actual.block(0,0,6,H2_actual.cols()));
|
||||
// Matrix H3_actualPose(H3_actual.block(0,0,6,H3_actual.cols()));
|
||||
// Matrix H4_actualPose(H4_actual.block(0,0,6,H4_actual.cols()));
|
||||
// Matrix H5_actualPose(H5_actual.block(0,0,6,H5_actual.cols()));
|
||||
//
|
||||
// // Calculate the Jacobian matrices H1 until H5 using the numerical derivative function
|
||||
// gtsam::Matrix H1_expectedPose, H2_expectedPose, H3_expectedPose, H4_expectedPose, H5_expectedPose;
|
||||
// H1_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1);
|
||||
// H2_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1);
|
||||
// H3_expectedPose = gtsam::numericalDerivative11<Pose3, imuBias::ConstantBias>(boost::bind(&predictionErrorPose, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1);
|
||||
// H4_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2);
|
||||
// H5_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2);
|
||||
//
|
||||
// // Verify they are equal for this choice of state
|
||||
// CHECK( gtsam::assert_equal(H1_expectedPose, H1_actualPose, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H2_expectedPose, H2_actualPose, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H3_expectedPose, H3_actualPose, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H4_expectedPose, H4_actualPose, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H5_expectedPose, H5_actualPose, 1e-6));
|
||||
//
|
||||
// // Checking for Vel part in the jacobians
|
||||
// // ******
|
||||
// Matrix H1_actualVel(H1_actual.block(6,0,3,H1_actual.cols()));
|
||||
// Matrix H2_actualVel(H2_actual.block(6,0,3,H2_actual.cols()));
|
||||
// Matrix H3_actualVel(H3_actual.block(6,0,3,H3_actual.cols()));
|
||||
// Matrix H4_actualVel(H4_actual.block(6,0,3,H4_actual.cols()));
|
||||
// Matrix H5_actualVel(H5_actual.block(6,0,3,H5_actual.cols()));
|
||||
//
|
||||
// // Calculate the Jacobian matrices H1 until H5 using the numerical derivative function
|
||||
// gtsam::Matrix H1_expectedVel, H2_expectedVel, H3_expectedVel, H4_expectedVel, H5_expectedVel;
|
||||
// H1_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1);
|
||||
// H2_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1);
|
||||
// H3_expectedVel = gtsam::numericalDerivative11<LieVector, imuBias::ConstantBias>(boost::bind(&predictionErrorVel, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1);
|
||||
// H4_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2);
|
||||
// H5_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2);
|
||||
//
|
||||
// // Verify they are equal for this choice of state
|
||||
// CHECK( gtsam::assert_equal(H1_expectedVel, H1_actualVel, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H2_expectedVel, H2_actualVel, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H3_expectedVel, H3_actualVel, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H4_expectedVel, H4_actualVel, 1e-6));
|
||||
// CHECK( gtsam::assert_equal(H5_expectedVel, H5_actualVel, 1e-6));
|
||||
}
|
||||
|
||||
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
||||
/* ************************************************************************* */
|
||||
Loading…
Reference in New Issue