diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp index ca6a89380..0b91644e2 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp @@ -73,7 +73,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, equals ) +TEST( ConcurrentIncrementalSmootherDL, equals ) { // TODO: Test 'equals' more vigorously @@ -99,7 +99,7 @@ TEST( ConcurrentIncrementalSmoother, equals ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getFactors ) +TEST( ConcurrentIncrementalSmootherDL, getFactors ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -150,7 +150,7 @@ TEST( ConcurrentIncrementalSmoother, getFactors ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) +TEST( ConcurrentIncrementalSmootherDL, getLinearizationPoint ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -201,19 +201,19 @@ TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getOrdering ) +TEST( ConcurrentIncrementalSmootherDL, getOrdering ) { // TODO: Think about how to check ordering... } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getDelta ) +TEST( ConcurrentIncrementalSmootherDL, getDelta ) { // TODO: Think about how to check ordering... } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, calculateEstimate ) +TEST( ConcurrentIncrementalSmootherDL, calculateEstimate ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -287,7 +287,7 @@ TEST( ConcurrentIncrementalSmoother, calculateEstimate ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, update_empty ) +TEST( ConcurrentIncrementalSmootherDL, update_empty ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -300,7 +300,7 @@ TEST( ConcurrentIncrementalSmoother, update_empty ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, update_multiple ) +TEST( ConcurrentIncrementalSmootherDL, update_multiple ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -358,7 +358,7 @@ TEST( ConcurrentIncrementalSmoother, update_multiple ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_empty ) +TEST( ConcurrentIncrementalSmootherDL, synchronize_empty ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -388,7 +388,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_empty ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_1 ) +TEST( ConcurrentIncrementalSmootherDL, synchronize_1 ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -450,7 +450,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_1 ) /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_2 ) +TEST( ConcurrentIncrementalSmootherDL, synchronize_2 ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -531,7 +531,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_2 ) /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_3 ) +TEST( ConcurrentIncrementalSmootherDL, synchronize_3 ) { // Create a set of optimizer parameters ISAM2Params parameters; diff --git a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp index 5c608b2cb..bdca9238c 100644 --- a/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp +++ b/gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp @@ -73,7 +73,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, equals ) +TEST( ConcurrentIncrementalSmootherGN, equals ) { // TODO: Test 'equals' more vigorously @@ -99,7 +99,7 @@ TEST( ConcurrentIncrementalSmoother, equals ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getFactors ) +TEST( ConcurrentIncrementalSmootherGN, getFactors ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -150,7 +150,7 @@ TEST( ConcurrentIncrementalSmoother, getFactors ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) +TEST( ConcurrentIncrementalSmootherGN, getLinearizationPoint ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -201,19 +201,19 @@ TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getOrdering ) +TEST( ConcurrentIncrementalSmootherGN, getOrdering ) { // TODO: Think about how to check ordering... } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, getDelta ) +TEST( ConcurrentIncrementalSmootherGN, getDelta ) { // TODO: Think about how to check ordering... } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, calculateEstimate ) +TEST( ConcurrentIncrementalSmootherGN, calculateEstimate ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -287,7 +287,7 @@ TEST( ConcurrentIncrementalSmoother, calculateEstimate ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, update_empty ) +TEST( ConcurrentIncrementalSmootherGN, update_empty ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -300,7 +300,7 @@ TEST( ConcurrentIncrementalSmoother, update_empty ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, update_multiple ) +TEST( ConcurrentIncrementalSmootherGN, update_multiple ) { // Create a Concurrent Batch Smoother ISAM2Params parameters; @@ -358,7 +358,7 @@ TEST( ConcurrentIncrementalSmoother, update_multiple ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_empty ) +TEST( ConcurrentIncrementalSmootherGN, synchronize_empty ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -388,7 +388,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_empty ) } /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_1 ) +TEST( ConcurrentIncrementalSmootherGN, synchronize_1 ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -450,7 +450,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_1 ) /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_2 ) +TEST( ConcurrentIncrementalSmootherGN, synchronize_2 ) { // Create a set of optimizer parameters ISAM2Params parameters; @@ -531,7 +531,7 @@ TEST( ConcurrentIncrementalSmoother, synchronize_2 ) /* ************************************************************************* */ -TEST( ConcurrentIncrementalSmoother, synchronize_3 ) +TEST( ConcurrentIncrementalSmootherGN, synchronize_3 ) { // Create a set of optimizer parameters ISAM2Params parameters; diff --git a/gtsam_unstable/slam/BetweenFactorEM.h b/gtsam_unstable/slam/BetweenFactorEM.h index 79682c754..279ffa6a5 100644 --- a/gtsam_unstable/slam/BetweenFactorEM.h +++ b/gtsam_unstable/slam/BetweenFactorEM.h @@ -1,299 +1,299 @@ -/* ---------------------------------------------------------------------------- - - * 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 - -#include -#include -#include -#include - -namespace gtsam { - - /** - * A class for a measurement predicted by "between(config[key1],config[key2])" - * @tparam VALUE the Value type - * @addtogroup SLAM - */ - template - class BetweenFactorEM: public NonlinearFactor { - - public: - - typedef VALUE T; - - private: - - typedef BetweenFactorEM 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 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) : - Base(cref_list_of<2>(key1)(key2)), 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 { - 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 */ - virtual bool equals(const NonlinearFactor& f, double tol=1e-9) const { - const This *t = dynamic_cast (&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 linearize(const gtsam::Values& x) const { - // Only linearize if the factor is active - if (!this->active(x)) - return boost::shared_ptr(); - - //std::cout<<"About to linearize"< A(this->size()); - gtsam::Vector b = -whitenedError(x, A); - A1 = A[0]; - A2 = A[1]; - - return gtsam::GaussianFactor::shared_ptr( - new gtsam::JacobianFactor(key1_, A1, key2_, A2, b, gtsam::noiseModel::Unit::Create(b.size()))); - } - - - /* ************************************************************************* */ - gtsam::Vector whitenedError(const gtsam::Values& x, - boost::optional&> H = boost::none) const { - - bool debug = true; - - const T& p1 = x.at(key1_); - const T& p2 = x.at(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 = 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: "<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; - - // 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); - } - - /* ************************************************************************* */ - gtsam::Vector unwhitenedError(const gtsam::Values& x) const { - - bool debug = true; - - const T& p1 = x.at(key1_); - const T& p2 = x.at(key2_); - - Matrix H1, H2; - - T hx = p1.between(p2, H1, H2); // h(x) - - return measured_.localCoordinates(hx); - } - - /* ************************************************************************* */ - /** 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 - void serialize(ARCHIVE & ar, const unsigned int version) { - ar & boost::serialization::make_nvp("NonlinearFactor", - boost::serialization::base_object(*this)); - ar & BOOST_SERIALIZATION_NVP(measured_); - } - }; // \class BetweenFactorEM - -} /// namespace gtsam +/* ---------------------------------------------------------------------------- + + * 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 + +#include +#include +#include +#include + +namespace gtsam { + + /** + * A class for a measurement predicted by "between(config[key1],config[key2])" + * @tparam VALUE the Value type + * @addtogroup SLAM + */ + template + class BetweenFactorEM: public NonlinearFactor { + + public: + + typedef VALUE T; + + private: + + typedef BetweenFactorEM 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 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) : + Base(cref_list_of<2>(key1)(key2)), 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 { + 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 */ + virtual bool equals(const NonlinearFactor& f, double tol=1e-9) const { + const This *t = dynamic_cast (&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 linearize(const gtsam::Values& x) const { + // Only linearize if the factor is active + if (!this->active(x)) + return boost::shared_ptr(); + + //std::cout<<"About to linearize"< A(this->size()); + gtsam::Vector b = -whitenedError(x, A); + A1 = A[0]; + A2 = A[1]; + + return gtsam::GaussianFactor::shared_ptr( + new gtsam::JacobianFactor(key1_, A1, key2_, A2, b, gtsam::noiseModel::Unit::Create(b.size()))); + } + + + /* ************************************************************************* */ + gtsam::Vector whitenedError(const gtsam::Values& x, + boost::optional&> H = boost::none) const { + + bool debug = true; + + const T& p1 = x.at(key1_); + const T& p2 = x.at(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 = 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: "<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.norm()) * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) ); + double p_outlier = prior_outlier_ * std::sqrt(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; + + // 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); + } + + /* ************************************************************************* */ + gtsam::Vector unwhitenedError(const gtsam::Values& x) const { + + bool debug = true; + + const T& p1 = x.at(key1_); + const T& p2 = x.at(key2_); + + Matrix H1, H2; + + T hx = p1.between(p2, H1, H2); // h(x) + + return measured_.localCoordinates(hx); + } + + /* ************************************************************************* */ + /** 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 + void serialize(ARCHIVE & ar, const unsigned int version) { + ar & boost::serialization::make_nvp("NonlinearFactor", + boost::serialization::base_object(*this)); + ar & BOOST_SERIALIZATION_NVP(measured_); + } + }; // \class BetweenFactorEM + +} /// namespace gtsam diff --git a/gtsam_unstable/slam/tests/testBetweenFactorEM.cpp b/gtsam_unstable/slam/tests/testBetweenFactorEM.cpp index a83c19dfa..c7772a125 100644 --- a/gtsam_unstable/slam/tests/testBetweenFactorEM.cpp +++ b/gtsam_unstable/slam/tests/testBetweenFactorEM.cpp @@ -1,477 +1,477 @@ -/** - * @file testBetweenFactorEM.cpp - * @brief Unit test for the BetweenFactorEM - * @author Vadim Indelman - */ - -#include - - -#include -#include -#include -#include -#include - -#include - -//#include -//#include -//#include - - -using namespace std; -using namespace gtsam; - - -/* ************************************************************************* */ -LieVector predictionError(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactorEM& 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& 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 f(key1, key2, rel_pose_msr, model_inlier, model_outlier, - prior_inlier, prior_outlier); - BetweenFactorEM 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 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 - CHECK(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: "< 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 - CHECK(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: "< 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 h(key1, key2, rel_pose_msr, model_inlier ); - Vector actual_err_wh_stnd = h.whitenedError(values); - - cout<<"actual_err_wh: "< f(key1, key2, rel_pose_msr, model_inlier, model_outlier, - prior_inlier, prior_outlier); - - std::vector 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 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 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(boost::bind(&predictionError, _1, p2, key1, key2, f), p1, stepsize); - Matrix H2_expected = gtsam::numericalDerivative11(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(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 f(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); -// InertialNavFactor 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 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 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 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 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 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(boost::bind(&predictionErrorPose, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); -// H2_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); -// H3_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); -// H4_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); -// H5_expectedPose = gtsam::numericalDerivative11(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(boost::bind(&predictionErrorVel, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); -// H2_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); -// H3_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); -// H4_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); -// H5_expectedVel = gtsam::numericalDerivative11(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);} -/* ************************************************************************* */ +/** + * @file testBetweenFactorEM.cpp + * @brief Unit test for the BetweenFactorEM + * @author Vadim Indelman + */ + +#include + + +#include +#include +#include +#include +#include + +#include + +//#include +//#include +//#include + + +using namespace std; +using namespace gtsam; + + +/* ************************************************************************* */ +LieVector predictionError(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactorEM& 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& 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 f(key1, key2, rel_pose_msr, model_inlier, model_outlier, + prior_inlier, prior_outlier); + BetweenFactorEM 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 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 + CHECK(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: "< 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 + CHECK(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: "< 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 h(key1, key2, rel_pose_msr, model_inlier ); + Vector actual_err_wh_stnd = h.whitenedError(values); + + cout<<"actual_err_wh: "< f(key1, key2, rel_pose_msr, model_inlier, model_outlier, + prior_inlier, prior_outlier); + + std::vector 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 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 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(boost::bind(&predictionError, _1, p2, key1, key2, f), p1, stepsize); + Matrix H2_expected = gtsam::numericalDerivative11(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(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 f(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); +// InertialNavFactor 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 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 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 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 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 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(boost::bind(&predictionErrorPose, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); +// H2_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); +// H3_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); +// H4_expectedPose = gtsam::numericalDerivative11(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); +// H5_expectedPose = gtsam::numericalDerivative11(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(boost::bind(&predictionErrorVel, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); +// H2_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); +// H3_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); +// H4_expectedVel = gtsam::numericalDerivative11(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); +// H5_expectedVel = gtsam::numericalDerivative11(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);} +/* ************************************************************************* */ diff --git a/gtsam_unstable/slam/tests/testSmartProjectionFactor.cpp b/gtsam_unstable/slam/tests/testSmartProjectionFactor.cpp index 9dfb8b48a..5aa3b3540 100644 --- a/gtsam_unstable/slam/tests/testSmartProjectionFactor.cpp +++ b/gtsam_unstable/slam/tests/testSmartProjectionFactor.cpp @@ -173,8 +173,8 @@ TEST( SmartProjectionFactor, noisy ){ /* ************************************************************************* */ -TEST( SmartProjectionFactor, 3poses ){ - cout << " ************************ MultiProjectionFactor: 3 cams + 3 landmarks **********************" << endl; +TEST( SmartProjectionFactor, 3poses_smart_projection_factor ){ + cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks **********************" << endl; Symbol x1('X', 1); Symbol x2('X', 2); @@ -239,17 +239,19 @@ TEST( SmartProjectionFactor, 3poses ){ graph.push_back(PriorFactor(x1, pose1, noisePrior)); graph.push_back(PriorFactor(x2, pose2, noisePrior)); - Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/100, 0., -M_PI/100), gtsam::Point3(0.1,0.1,0.1)); +// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3)); // noise from regular projection factor test below + Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/100, 0., -M_PI/100), gtsam::Point3(0.1,0.1,0.1)); // smaller noise Values values; values.insert(x1, pose1); - values.insert(x2, pose2*noise_pose); - values.insert(x3, pose3); + values.insert(x2, pose2); + // initialize third pose with some noise, we expect it to move back to original pose3 + values.insert(x3, pose3*noise_pose); + values.at(x3).print("Smart: Pose3 before optimization: "); LevenbergMarquardtParams params; params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; params.verbosity = NonlinearOptimizerParams::ERROR; - Values result; gttic_(SmartProjectionFactor); LevenbergMarquardtOptimizer optimizer(graph, values, params); @@ -257,7 +259,9 @@ TEST( SmartProjectionFactor, 3poses ){ gttoc_(SmartProjectionFactor); tictoc_finishedIteration_(); - result.print("results of 3 camera, 3 landmark optimization \n"); + // result.print("results of 3 camera, 3 landmark optimization \n"); + result.at(x3).print("Smart: Pose3 after optimization: "); + EXPECT(assert_equal(pose3,result.at(x3))); tictoc_print_(); } @@ -265,7 +269,7 @@ TEST( SmartProjectionFactor, 3poses ){ /* ************************************************************************* */ TEST( SmartProjectionFactor, 3poses_projection_factor ){ - cout << " ************************ Normal ProjectionFactor: 3 cams + 3 landmarks **********************" << endl; +// cout << " ************************ Normal ProjectionFactor: 3 cams + 3 landmarks **********************" << endl; Symbol x1('X', 1); Symbol x2('X', 2); @@ -287,7 +291,6 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){ // create third camera 1 meter above the first camera Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0)); - pose3.print("Pose3: "); SimpleCamera cam3(pose3, *K); // three landmarks ~5 meters infront of camera @@ -324,6 +327,7 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){ values.insert(L(1), landmark1); values.insert(L(2), landmark2); values.insert(L(3), landmark3); +// values.at(x3).print("Pose3 before optimization: "); LevenbergMarquardtParams params; // params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; @@ -331,14 +335,15 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){ LevenbergMarquardtOptimizer optimizer(graph, values, params); Values result = optimizer.optimize(); - result.print("Regular Projection Factor: results of 3 camera, 3 landmark optimization \n"); +// result.at(x3).print("Pose3 after optimization: "); + EXPECT(assert_equal(pose3,result.at(x3))); } /* ************************************************************************* */ TEST( SmartProjectionFactor, Hessian ){ - cout << " ************************ Normal ProjectionFactor: Hessian **********************" << endl; + cout << " ************************ SmartProjectionFactor: Hessian **********************" << endl; Symbol x1('X', 1); Symbol x2('X', 2); diff --git a/matlab/gtsam_examples/IMUKittiExample.m b/matlab/gtsam_examples/IMUKittiExample.m deleted file mode 100644 index 92239353b..000000000 --- a/matlab/gtsam_examples/IMUKittiExample.m +++ /dev/null @@ -1,226 +0,0 @@ -%close all -%clc - -import gtsam.*; - -%% Read metadata and compute relative sensor pose transforms -IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt'); -IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); -IMUinBody = Pose3.Expmap([ - IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz; - IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]); -if ~IMUinBody.equals(Pose3, 1e-5) - error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; -end - -VO_metadata = importdata('KittiRelativePose_metadata.txt'); -VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2); -VOinBody = Pose3.Expmap([ - VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; - VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); - -GPS_metadata = importdata('KittiGps_metadata.txt'); -GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); -GPSinBody = Pose3.Expmap([ - GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz; - GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]); - -VOinIMU = IMUinBody.inverse().compose(VOinBody); -GPSinIMU = IMUinBody.inverse().compose(GPSinBody); - -%% Read data and change coordinate frame of GPS and VO measurements to IMU frame -IMU_data = importdata('KittiEquivBiasedImu.txt'); -IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); -imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false); -[IMU_data.acc_omega] = deal(imum{:}); -IMU_data = rmfield(IMU_data, { 'accelX' 'accelY' 'accelZ' 'omegaX' 'omegaY' 'omegaZ' }); -clear imum - -VO_data = importdata('KittiRelativePose.txt'); -VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2); -% Merge relative pose fields and convert to Pose3 -logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ]; -logposes = num2cell(logposes, 2); -relposes = arrayfun(@(x) {gtsam.Pose3.Expmap(x{:}')}, logposes); -relposes = arrayfun(@(x) {VOinIMU.compose(x{:}).compose(VOinIMU.inverse())}, relposes); -[VO_data.RelativePose] = deal(relposes{:}); -VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' }); -clear logposes relposes - -GPS_data = importdata('KittiGps.txt'); -GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); - -%% Set initial conditions for the estimated trajectory -disp('TODO: we have GPS so this initialization is not right') -currentPoseGlobal = Pose3; % initial pose is the reference frame (navigation frame) -currentVelocityGlobal = [0;0;0]; % the vehicle is stationary at the beginning -bias_acc = [0;0;0]; % we initialize accelerometer biases to zero -bias_omega = [0;0;0]; % we initialize gyro biases to zero - -%% Solver object -isamParams = ISAM2Params; -isamParams.setRelinearizeSkip(1); -isam = gtsam.ISAM2(isamParams); - -%% create nonlinear factor graph -factors = NonlinearFactorGraph; -values = Values; - -%% Create prior on initial pose, velocity, and biases -sigma_init_x = 1.0; -sigma_init_v = 1.0; -sigma_init_b = 1.0; - -values.insert(symbol('x',0), currentPoseGlobal); -values.insert(symbol('v',0), LieVector(currentVelocityGlobal) ); -values.insert(symbol('b',0), imuBias.ConstantBias(bias_acc,bias_omega) ); - -disp('TODO: we have GPS so this initialization is not right') -% Prior on initial pose -factors.add(PriorFactorPose3(symbol('x',0), ... - currentPoseGlobal, noiseModel.Isotropic.Sigma(6, sigma_init_x))); -% Prior on initial velocity -factors.add(PriorFactorLieVector(symbol('v',0), ... - LieVector(currentVelocityGlobal), noiseModel.Isotropic.Sigma(3, sigma_init_v))); -% Prior on initial bias -factors.add(PriorFactorConstantBias(symbol('b',0), ... - imuBias.ConstantBias(bias_acc,bias_omega), noiseModel.Isotropic.Sigma(6, sigma_init_b))); - -%% Main loop: -% (1) we read the measurements -% (2) we create the corresponding factors in the graph -% (3) we solve the graph to obtain and optimal estimate of robot trajectory - -% lastTime = 0; TODO: delete? -% lastIndex = 0; TODO: delete? -currentSummarizedMeasurement = []; - -% Measurement types: -% 1: VO -% 2: GPS -% 3: IMU -times = sortrows( [ ... - [VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ... - %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ... - [IMU_data.Time]' 3*ones(length([IMU_data.Time]), 1); ... - ], 1); % this are the time-stamps at which we want to initialize a new node in the graph - -t_previous = 0; -poseIndex = 0; -for measurementIndex = 1:size(times,1) - % At each non=IMU measurement we initialize a new node in the graph - currentPoseKey = symbol('x',poseIndex); - currentVelKey = symbol('v',poseIndex); - currentBiasKey = symbol('b',poseIndex); - - t = times(measurementIndex, 1); - type = times(measurementIndex, 2); - - if type == 3 - % Integrate IMU - - if isempty(currentSummarizedMeasurement) - % Create initial empty summarized measurement - % we assume that each row of the IMU.txt file has the following structure: - % timestamp delta_t acc_x acc_y acc_z omega_x omega_y omega_z - currentBias = isam.calculateEstimate(currentBiasKey - 1); - currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... - currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... - IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); - end - - % Accumulate preintegrated measurement - deltaT = IMU_data(index).dt; - accMeas = IMU_data(index).acc_omega(1:3); - omegaMeas = IMU_data(index).acc_omega(4:6); - currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); - - else - % Create IMU factor - factors.add(ImuFactor( ... - currentPoseKey-1, currentVelKey-1, ... - currentPoseKey, currentVelKey, ... - currentBiasKey-1, currentSummarizedMeasurement, g, cor_v, ... - currentSummarizedMeasurement.PreintMeasCov)); - - % Reset summarized measurement - currentSummarizedMeasurement = []; - - if type == 1 - % Create VO factor - elseif type == 2 - % Create GPS factor - end - - poseIndex = poseIndex + 1; - end - - - % ======================================================================= - - - %% add factor corresponding to GPS measurements (if available at the current time) -% % ======================================================================= -% if isempty( find(GPS_data(:,1) == t ) ) == 0 % it is a GPS measurement -% if length( find(GPS_data(:,1)) ) > 1 -% error('more GPS measurements at the same time stamp: it should be an error') -% end -% -% index = find(GPS_data(:,1) == t ); % the row of the IMU_data matrix that we have to integrate -% GPSmeas = GPS_data(index,2:4); -% -% noiseModelGPS = ???; % noiseModelGPS.Isotropic.Sigma(6, sigma_init_x)) -% -% % add factor -% disp('TODO: is the GPS noise right?') -% factors.add(PriorFactor???(currentPoseKey, GPSmeas, noiseModelGPS) ); -% end - % ======================================================================= - - - %% add factor corresponding to VO measurements (if available at the current time) - % ======================================================================= - if isempty( find([VO_data.Time] == t, 1) )== 0 % it is a GPS measurement - if length( find([VO_data.Time] == t) ) > 1 - error('more VO measurements at the same time stamp: it should be an error') - end - - index = find([VO_data.Time] == t, 1); % the row of the IMU_data matrix that we have to integrate - - VOpose = VO_data(index).RelativePose; - noiseModelVO = noiseModel.Diagonal.Sigmas([ IMU_metadata.RotationSigma * [1;1;1]; IMU_metadata.TranslationSigma * [1;1;1] ]); - - % add factor - disp('TODO: is the VO noise right?') - factors.add(BetweenFactorPose3(lastVOPoseKey, currentPoseKey, VOpose, noiseModelVO)); - - lastVOPoseKey = currentPoseKey; - end - % ======================================================================= - - disp('TODO: add values') - % values.insert(, initialPose); - % values.insert(symbol('v',lastIndex+1), initialVel); - - %% Update solver - % ======================================================================= - isam.update(factors, values); - factors = NonlinearFactorGraph; - values = Values; - - isam.calculateEstimate(currentPoseKey); - % M = isam.marginalCovariance(key_pose); - % ======================================================================= - - previousPoseKey = currentPoseKey; - previousVelKey = currentVelKey; - t_previous = t; -end - -disp('TODO: display results') -% figure(1) -% hold on; -% plot(positions(1,:), positions(2,:), '-b'); -% plot3DTrajectory(isam.calculateEstimate, 'g-'); -% axis equal; -% legend('true trajectory', 'traj integrated in body', 'traj integrated in nav') diff --git a/matlab/gtsam_examples/IMUKittiExampleAdvanced.m b/matlab/gtsam_examples/IMUKittiExampleAdvanced.m new file mode 100644 index 000000000..1db60a5ad --- /dev/null +++ b/matlab/gtsam_examples/IMUKittiExampleAdvanced.m @@ -0,0 +1,191 @@ +close all +clc + +import gtsam.*; +disp('Example of application of ISAM2 for visual-inertial navigation on the KITTI VISION BENCHMARK SUITE (http://www.computervisiononline.com/dataset/kitti-vision-benchmark-suite)') + +%% Read metadata and compute relative sensor pose transforms +% IMU metadata +disp('-- Reading sensor metadata') +IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt'); +IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); +IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz; + IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]); +if ~IMUinBody.equals(Pose3, 1e-5) + error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; +end + +% VO metadata +VO_metadata = importdata('KittiRelativePose_metadata.txt'); +VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2); +VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; + VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); +VOinIMU = IMUinBody.inverse().compose(VOinBody); + +% GPS metadata +GPS_metadata = importdata('KittiGps_metadata.txt'); +GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); +GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz; + GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]); +GPSinIMU = IMUinBody.inverse().compose(GPSinBody); + +%% Read data and change coordinate frame of GPS and VO measurements to IMU frame +disp('-- Reading sensor data from file') +% IMU data +IMU_data = importdata('KittiEquivBiasedImu.txt'); +IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); +imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false); +[IMU_data.acc_omega] = deal(imum{:}); +%IMU_data = rmfield(IMU_data, { 'accelX' 'accelY' 'accelZ' 'omegaX' 'omegaY' 'omegaZ' }); +clear imum + +% VO data +VO_data = importdata('KittiRelativePose.txt'); +VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2); +% Merge relative pose fields and convert to Pose3 +logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ]; +logposes = num2cell(logposes, 2); +relposes = arrayfun(@(x) {gtsam.Pose3.Expmap(x{:}')}, logposes); +relposes = arrayfun(@(x) {VOinIMU.compose(x{:}).compose(VOinIMU.inverse())}, relposes); +[VO_data.RelativePose] = deal(relposes{:}); +VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' }); +noiseModelVO = noiseModel.Diagonal.Sigmas([ VO_metadata.RotationSigma * [1;1;1]; VO_metadata.TranslationSigma * [1;1;1] ]); +clear logposes relposes + +% % % GPS data +% % GPS_data = importdata('KittiGps.txt'); +% % GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); +% % % Convert GPS from lat/long to meters +% % [ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] ); +% % for i = 1:numel(x) +% % GPS_data(i).Position = gtsam.Point3(x(i), y(i), GPS_data(i).Altitude); +% % end +% % % % Calculate GPS sigma in meters +% % % [ xSig, ySig, ~ ] = deg2utm( [GPS_data.Latitude] + [GPS_data.PositionSigma], ... +% % % [GPS_data.Longitude] + [GPS_data.PositionSigma]); +% % % xSig = xSig - x; +% % % ySig = ySig - y; +% % %% Start at time of first GPS measurement +% % % firstGPSPose = 1; + +%% Get initial conditions for the estimated trajectory +% % % currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame) +currentPoseGlobal = Pose3; +currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning +currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); +sigma_init_x = noiseModel.Isotropic.Sigma(6, 0.01); +sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0); +sigma_init_b = noiseModel.Isotropic.Sigma(6, 0.01); +g = [0;0;-9.8]; +w_coriolis = [0;0;0]; + +%% Solver object +isamParams = ISAM2Params; +isamParams.setFactorization('QR'); +isamParams.setRelinearizeSkip(1); +isam = gtsam.ISAM2(isamParams); +newFactors = NonlinearFactorGraph; +newValues = Values; + +%% Main loop: +% (1) we read the measurements +% (2) we create the corresponding factors in the graph +% (3) we solve the graph to obtain and optimal estimate of robot trajectory +timestamps = sortrows( [ ... + [VO_data.Time]' 1*ones(length([VO_data.Time]), 1); ... +% % %[GPS_data.Time]' 2*ones(length([GPS_data.Time]), 1); ... + ], 1); % this are the time-stamps at which we want to initialize a new node in the graph + +timestamps = timestamps(15:end,:); % there seem to be issues with the initial IMU measurements +IMUtimes = [IMU_data.Time]; +VOPoseKeys = []; % here we store the keys of the poses involved in VO (between) factors + +for measurementIndex = 1:length(timestamps) + + % At each non=IMU measurement we initialize a new node in the graph + currentPoseKey = symbol('x',measurementIndex); + currentVelKey = symbol('v',measurementIndex); + currentBiasKey = symbol('b',measurementIndex); + t = timestamps(measurementIndex, 1); + type = timestamps(measurementIndex, 2); + + %% bookkeeping + if type == 1 % we store the keys corresponding to VO measurements + VOPoseKeys = [VOPoseKeys; currentPoseKey]; + end + + if measurementIndex == 1 + %% Create initial estimate and prior on initial pose, velocity, and biases + newValues.insert(currentPoseKey, currentPoseGlobal); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x)); + newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v)); + newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b)); + else + t_previous = timestamps(measurementIndex-1, 1); + %% Summarize IMU data between the previous GPS measurement and now + IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t); + + if ~isempty(IMUindices) % if there are IMU measurements to integrate + currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... + currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... + IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); + + for imuIndex = IMUindices + accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ]; + omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ]; + deltaT = IMU_data(imuIndex).dt; + currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); + end + + % Create IMU factor + newFactors.add(ImuFactor( ... + currentPoseKey-1, currentVelKey-1, ... + currentPoseKey, currentVelKey, ... + currentBiasKey, currentSummarizedMeasurement, g, w_coriolis)); + + else % if there are no IMU measurements + error('no IMU measurements in [t_previous, t]') + end + + % LC: sigma_init_b is wrong: this should be some uncertainty on bias evolution given in the IMU metadata + newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), sigma_init_b)); + + %% Create GPS factor + if type == 2 + newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position), ... + noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(measurementIndex).PositionSigma).^2*ones(3,1) ]))); + end + + %% Create VO factor + if type == 1 + VOpose = VO_data(measurementIndex).RelativePose; + newFactors.add(BetweenFactorPose3(VOPoseKeys(end-1), VOPoseKeys(end), VOpose, noiseModelVO)); + end + + % Add initial value + % newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position)); + newValues.insert(currentPoseKey,currentPoseGlobal); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + + % Update solver + % ======================================================================= + isam.update(newFactors, newValues); + newFactors = NonlinearFactorGraph; + newValues = Values; + + if rem(measurementIndex,100)==0 % plot every 100 time steps + cla; + plot3DTrajectory(isam.calculateEstimate, 'g-'); + axis equal + drawnow; + end + % ======================================================================= + currentPoseGlobal = isam.calculateEstimate(currentPoseKey); + currentVelocityGlobal = isam.calculateEstimate(currentVelKey); + currentBias = isam.calculateEstimate(currentBiasKey); + end + +end % end main loop diff --git a/matlab/gtsam_examples/IMUKittiExampleGPS.m b/matlab/gtsam_examples/IMUKittiExampleGPS.m new file mode 100644 index 000000000..49f01befe --- /dev/null +++ b/matlab/gtsam_examples/IMUKittiExampleGPS.m @@ -0,0 +1,149 @@ +close all +clc + +import gtsam.*; +disp('Example of application of ISAM2 for GPS-aided navigation on the KITTI VISION BENCHMARK SUITE (http://www.computervisiononline.com/dataset/kitti-vision-benchmark-suite)') + +%% Read metadata and compute relative sensor pose transforms +% IMU metadata +disp('-- Reading sensor metadata') +IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt'); +IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); +IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz; + IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]); +if ~IMUinBody.equals(Pose3, 1e-5) + error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; +end + +% GPS metadata +GPS_metadata = importdata('KittiRelativePose_metadata.txt'); +GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); + +%% Read data +disp('-- Reading sensor data from file') +% IMU data +IMU_data = importdata('KittiEquivBiasedImu.txt'); +IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); +imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false); +[IMU_data.acc_omega] = deal(imum{:}); +clear imum + +% GPS data +GPS_data = importdata('Gps_converted.txt'); +GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); +for i = 1:numel(GPS_data) + GPS_data(i).Position = gtsam.Point3(GPS_data(i).X, GPS_data(i).Y, GPS_data(i).Z); +end +noiseModelGPS = noiseModel.Diagonal.Precisions([ [0;0;0]; 1.0/0.07 * [1;1;1] ]); +firstGPSPose = 2; +GPSskip = 10; % Skip this many GPS measurements each time + +%% Get initial conditions for the estimated trajectory +currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame) +currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning +currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); +sigma_init_x = noiseModel.Isotropic.Precisions([ 0.0; 0.0; 0.0; 1; 1; 1 ]); +sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0); +sigma_init_b = noiseModel.Isotropic.Sigmas([ 0.100; 0.100; 0.100; 5.00e-05; 5.00e-05; 5.00e-05 ]); +sigma_between_b = [ IMU_metadata.AccelerometerBiasSigma * ones(3,1); IMU_metadata.GyroscopeBiasSigma * ones(3,1) ]; +g = [0;0;-9.8]; +w_coriolis = [0;0;0]; + +%% Solver object +isamParams = ISAM2Params; +isamParams.setFactorization('CHOLESKY'); +isamParams.setRelinearizeSkip(10); +isam = gtsam.ISAM2(isamParams); +newFactors = NonlinearFactorGraph; +newValues = Values; + +%% Main loop: +% (1) we read the measurements +% (2) we create the corresponding factors in the graph +% (3) we solve the graph to obtain and optimal estimate of robot trajectory +IMUtimes = [IMU_data.Time]; + +disp('-- Starting main loop: inference is performed at each time step, but we plot trajectory every 10 steps') + +for measurementIndex = firstGPSPose:length(GPS_data) + + % At each non=IMU measurement we initialize a new node in the graph + currentPoseKey = symbol('x',measurementIndex); + currentVelKey = symbol('v',measurementIndex); + currentBiasKey = symbol('b',measurementIndex); + t = GPS_data(measurementIndex, 1).Time; + + if measurementIndex == firstGPSPose + %% Create initial estimate and prior on initial pose, velocity, and biases + newValues.insert(currentPoseKey, currentPoseGlobal); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x)); + newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v)); + newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b)); + else + t_previous = GPS_data(measurementIndex-1, 1).Time; + %% Summarize IMU data between the previous GPS measurement and now + IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t); + + currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... + currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... + IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); + + for imuIndex = IMUindices + accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ]; + omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ]; + deltaT = IMU_data(imuIndex).dt; + currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); + end + + % Create IMU factor + newFactors.add(ImuFactor( ... + currentPoseKey-1, currentVelKey-1, ... + currentPoseKey, currentVelKey, ... + currentBiasKey, currentSummarizedMeasurement, g, w_coriolis)); + + % Bias evolution as given in the IMU metadata + newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), ... + noiseModel.Diagonal.Sigmas(sqrt(numel(IMUindices)) * sigma_between_b))); + + % Create GPS factor + GPSPose = Pose3(currentPoseGlobal.rotation, GPS_data(measurementIndex).Position); + if mod(measurementIndex, GPSskip) == 0 + newFactors.add(PriorFactorPose3(currentPoseKey, GPSPose, noiseModelGPS)); + end + + % Add initial value + newValues.insert(currentPoseKey, GPSPose); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + + % Update solver + % ======================================================================= + % We accumulate 2*GPSskip GPS measurements before updating the solver at + % first so that the heading becomes observable. + if measurementIndex > firstGPSPose + 2*GPSskip + isam.update(newFactors, newValues); + newFactors = NonlinearFactorGraph; + newValues = Values; + + if rem(measurementIndex,10)==0 % plot every 10 time steps + cla; + plot3DTrajectory(isam.calculateEstimate, 'g-'); + title('Estimated trajectory using ISAM2 (IMU+GPS)') + xlabel('[m]') + ylabel('[m]') + zlabel('[m]') + axis equal + drawnow; + end + % ======================================================================= + currentPoseGlobal = isam.calculateEstimate(currentPoseKey); + currentVelocityGlobal = isam.calculateEstimate(currentVelKey); + currentBias = isam.calculateEstimate(currentBiasKey); + end + end + +end % end main loop + +disp('-- Reached end of sensor data') diff --git a/matlab/gtsam_examples/IMUKittiExampleSimple.m b/matlab/gtsam_examples/IMUKittiExampleSimple.m deleted file mode 100644 index f3940a4b4..000000000 --- a/matlab/gtsam_examples/IMUKittiExampleSimple.m +++ /dev/null @@ -1,126 +0,0 @@ -%close all -%clc - -import gtsam.*; - -%% Read data -IMU_metadata = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu_metadata.txt')); -IMU_data = importdata(gtsam.findExampleDataFile('KittiEquivBiasedImu.txt')); -% Make text file column headers into struct fields -IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); -IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); - -GPS_metadata = importdata(gtsam.findExampleDataFile('KittiGps_metadata.txt')); -GPS_data = importdata(gtsam.findExampleDataFile('KittiGps.txt')); -% Make text file column headers into struct fields -GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2); -GPS_data = cell2struct(num2cell(GPS_data.data), GPS_data.colheaders, 2); - -%% Convert GPS from lat/long to meters -[ x, y, ~ ] = deg2utm( [GPS_data.Latitude], [GPS_data.Longitude] ); -for i = 1:numel(x) - GPS_data(i).Position = gtsam.Point3(x(i), y(i), GPS_data(i).Altitude); -end - -% % Calculate GPS sigma in meters -% [ xSig, ySig, ~ ] = deg2utm( [GPS_data.Latitude] + [GPS_data.PositionSigma], ... -% [GPS_data.Longitude] + [GPS_data.PositionSigma]); -% xSig = xSig - x; -% ySig = ySig - y; - -%% Start at time of first GPS measurement -firstGPSPose = 2; - -%% Get initial conditions for the estimated trajectory -currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame) -currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning -currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); - -%% Solver object -isamParams = ISAM2Params; -isamParams.setFactorization('QR'); -isamParams.setRelinearizeSkip(1); -isam = gtsam.ISAM2(isamParams); -newFactors = NonlinearFactorGraph; -newValues = Values; - -%% Create initial estimate and prior on initial pose, velocity, and biases -newValues.insert(symbol('x',firstGPSPose), currentPoseGlobal); -newValues.insert(symbol('v',firstGPSPose), currentVelocityGlobal); -newValues.insert(symbol('b',1), currentBias); - -sigma_init_x = noiseModel.Diagonal.Precisions([0;0;0; 1;1;1]); -sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0); -sigma_init_b = noiseModel.Isotropic.Sigma(6, 0.01); - -newFactors.add(PriorFactorPose3(symbol('x',firstGPSPose), currentPoseGlobal, sigma_init_x)); -newFactors.add(PriorFactorLieVector(symbol('v',firstGPSPose), currentVelocityGlobal, sigma_init_v)); -newFactors.add(PriorFactorConstantBias(symbol('b',1), currentBias, sigma_init_b)); - -%% Main loop: -% (1) we read the measurements -% (2) we create the corresponding factors in the graph -% (3) we solve the graph to obtain and optimal estimate of robot trajectory - -for poseIndex = firstGPSPose:length(GPS_data) - % At each non=IMU measurement we initialize a new node in the graph - currentPoseKey = symbol('x',poseIndex); - currentVelKey = symbol('v',poseIndex); - currentBiasKey = symbol('b',1); - - if poseIndex > firstGPSPose - % Summarize IMU data between the previous GPS measurement and now - IMUindices = find([IMU_data.Time] > GPS_data(poseIndex-1).Time ... - & [IMU_data.Time] <= GPS_data(poseIndex).Time); - - currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... - currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... - IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); - - for imuIndex = IMUindices - accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ]; - omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ]; - deltaT = IMU_data(imuIndex).dt; - currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); - end - - % Create IMU factor - newFactors.add(ImuFactor( ... - currentPoseKey-1, currentVelKey-1, ... - currentPoseKey, currentVelKey, ... - currentBiasKey, currentSummarizedMeasurement, [0;0;-9.8], [0;0;0])); - - % Create GPS factor - newFactors.add(PriorFactorPose3(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position), ... - noiseModel.Diagonal.Precisions([ zeros(3,1); 1./(GPS_data(poseIndex).PositionSigma).^2*ones(3,1) ]))); - - % Add initial value - newValues.insert(currentPoseKey, Pose3(currentPoseGlobal.rotation, GPS_data(poseIndex).Position)); - newValues.insert(currentVelKey, currentVelocityGlobal); - %newValues.insert(currentBiasKey, currentBias); - - % Update solver - % ======================================================================= - isam.update(newFactors, newValues); - newFactors = NonlinearFactorGraph; - newValues = Values; - - cla; - plot3DTrajectory(isam.calculateEstimate, 'g-'); - drawnow; - % ======================================================================= - - currentPoseGlobal = isam.calculateEstimate(currentPoseKey); - currentVelocityGlobal = isam.calculateEstimate(currentVelKey); - currentBias = isam.calculateEstimate(currentBiasKey); - - end -end - -disp('TODO: display results') -% figure(1) -% hold on; -% plot(positions(1,:), positions(2,:), '-b'); -% plot3DTrajectory(isam.calculateEstimate, 'g-'); -% axis equal; -% legend('true trajectory', 'traj integrated in body', 'traj integrated in nav') diff --git a/matlab/gtsam_examples/IMUKittiExampleVO.m b/matlab/gtsam_examples/IMUKittiExampleVO.m new file mode 100644 index 000000000..6434e750a --- /dev/null +++ b/matlab/gtsam_examples/IMUKittiExampleVO.m @@ -0,0 +1,152 @@ +close all +clc + +import gtsam.*; +disp('Example of application of ISAM2 for visual-inertial navigation on the KITTI VISION BENCHMARK SUITE (http://www.computervisiononline.com/dataset/kitti-vision-benchmark-suite)') + +%% Read metadata and compute relative sensor pose transforms +% IMU metadata +disp('-- Reading sensor metadata') +IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt'); +IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2); +IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz; + IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]); +if ~IMUinBody.equals(Pose3, 1e-5) + error 'Currently only support IMUinBody is identity, i.e. IMU and body frame are the same'; +end + +% VO metadata +VO_metadata = importdata('KittiRelativePose_metadata.txt'); +VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2); +VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz; + VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]); +VOinIMU = IMUinBody.inverse().compose(VOinBody); + +%% Read data and change coordinate frame of GPS and VO measurements to IMU frame +disp('-- Reading sensor data from file') +% IMU data +IMU_data = importdata('KittiEquivBiasedImu.txt'); +IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2); +imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false); +[IMU_data.acc_omega] = deal(imum{:}); +clear imum + +% VO data +VO_data = importdata('KittiRelativePose.txt'); +VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2); +% Merge relative pose fields and convert to Pose3 +logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ]; +logposes = num2cell(logposes, 2); +relposes = arrayfun(@(x) {gtsam.Pose3.Expmap(x{:}')}, logposes); +relposes = arrayfun(@(x) {VOinIMU.compose(x{:}).compose(VOinIMU.inverse())}, relposes); +[VO_data.RelativePose] = deal(relposes{:}); +VO_data = rmfield(VO_data, { 'dtx' 'dty' 'dtz' 'drx' 'dry' 'drz' }); +noiseModelVO = noiseModel.Diagonal.Sigmas([ VO_metadata.RotationSigma * [1;1;1]; VO_metadata.TranslationSigma * [1;1;1] ]); +clear logposes relposes + +%% Get initial conditions for the estimated trajectory +currentPoseGlobal = Pose3; +currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning +currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1)); +sigma_init_x = noiseModel.Isotropic.Sigmas([ 1.0; 1.0; 0.01; 0.01; 0.01; 0.01 ]); +sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0); +sigma_init_b = noiseModel.Isotropic.Sigmas([ 0.100; 0.100; 0.100; 5.00e-05; 5.00e-05; 5.00e-05 ]); +sigma_between_b = [ IMU_metadata.AccelerometerBiasSigma * ones(3,1); IMU_metadata.GyroscopeBiasSigma * ones(3,1) ]; +g = [0;0;-9.8]; +w_coriolis = [0;0;0]; + +%% Solver object +isamParams = ISAM2Params; +isamParams.setFactorization('CHOLESKY'); +isamParams.setRelinearizeSkip(10); +isam = gtsam.ISAM2(isamParams); +newFactors = NonlinearFactorGraph; +newValues = Values; + +%% Main loop: +% (1) we read the measurements +% (2) we create the corresponding factors in the graph +% (3) we solve the graph to obtain and optimal estimate of robot trajectory +timestamps = [VO_data.Time]'; + +timestamps = timestamps(15:end,:); % there seem to be issues with the initial IMU measurements +IMUtimes = [IMU_data.Time]; + +disp('-- Starting main loop: inference is performed at each time step, but we plot trajectory every 100 steps') + +for measurementIndex = 1:length(timestamps) + + % At each non=IMU measurement we initialize a new node in the graph + currentPoseKey = symbol('x',measurementIndex); + currentVelKey = symbol('v',measurementIndex); + currentBiasKey = symbol('b',measurementIndex); + t = timestamps(measurementIndex, 1); + + if measurementIndex == 1 + %% Create initial estimate and prior on initial pose, velocity, and biases + newValues.insert(currentPoseKey, currentPoseGlobal); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x)); + newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v)); + newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b)); + else + t_previous = timestamps(measurementIndex-1, 1); + %% Summarize IMU data between the previous GPS measurement and now + IMUindices = find(IMUtimes >= t_previous & IMUtimes <= t); + + currentSummarizedMeasurement = gtsam.ImuFactorPreintegratedMeasurements( ... + currentBias, IMU_metadata.AccelerometerSigma.^2 * eye(3), ... + IMU_metadata.GyroscopeSigma.^2 * eye(3), IMU_metadata.IntegrationSigma.^2 * eye(3)); + + for imuIndex = IMUindices + accMeas = [ IMU_data(imuIndex).accelX; IMU_data(imuIndex).accelY; IMU_data(imuIndex).accelZ ]; + omegaMeas = [ IMU_data(imuIndex).omegaX; IMU_data(imuIndex).omegaY; IMU_data(imuIndex).omegaZ ]; + deltaT = IMU_data(imuIndex).dt; + currentSummarizedMeasurement.integrateMeasurement(accMeas, omegaMeas, deltaT); + end + + % Create IMU factor + newFactors.add(ImuFactor( ... + currentPoseKey-1, currentVelKey-1, ... + currentPoseKey, currentVelKey, ... + currentBiasKey, currentSummarizedMeasurement, g, w_coriolis)); + + % LC: sigma_init_b is wrong: this should be some uncertainty on bias evolution given in the IMU metadata + newFactors.add(BetweenFactorConstantBias(currentBiasKey-1, currentBiasKey, imuBias.ConstantBias(zeros(3,1), zeros(3,1)), ... + noiseModel.Diagonal.Sigmas(sqrt(numel(IMUindices)) * sigma_between_b))); + + %% Create VO factor + VOpose = VO_data(measurementIndex).RelativePose; + newFactors.add(BetweenFactorPose3(currentPoseKey - 1, currentPoseKey, VOpose, noiseModelVO)); + + % Add initial value + newValues.insert(currentPoseKey, currentPoseGlobal.compose(VOpose)); + newValues.insert(currentVelKey, currentVelocityGlobal); + newValues.insert(currentBiasKey, currentBias); + + % Update solver + % ======================================================================= + isam.update(newFactors, newValues); + newFactors = NonlinearFactorGraph; + newValues = Values; + + if rem(measurementIndex,100)==0 % plot every 100 time steps + cla; + plot3DTrajectory(isam.calculateEstimate, 'g-'); + title('Estimated trajectory using ISAM2 (IMU+VO)') + xlabel('[m]') + ylabel('[m]') + zlabel('[m]') + axis equal + drawnow; + end + % ======================================================================= + currentPoseGlobal = isam.calculateEstimate(currentPoseKey); + currentVelocityGlobal = isam.calculateEstimate(currentVelKey); + currentBias = isam.calculateEstimate(currentBiasKey); + end + +end % end main loop + +disp('-- Reached end of sensor data')