12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'svn/trunk' 

Conflicts:
	gtsam_unstable/slam/BetweenFactorEM.h
	gtsam_unstable/slam/tests/testBetweenFactorEM.cpp
release/4.3a0
Richard Roberts 2013-08-12 21:47:36 +00:00
parent f1c9322d37 a518dae06a
commit d9c9682f6e
10 changed files with 1308 additions and 1163 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherDL.cpp
View File

@ -73,7 +73,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, equals ) TEST( ConcurrentIncrementalSmootherDL, equals )
{ {
// TODO: Test 'equals' more vigorously // TODO: Test 'equals' more vigorously
@ -99,7 +99,7 @@ TEST( ConcurrentIncrementalSmoother, equals )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getFactors ) TEST( ConcurrentIncrementalSmootherDL, getFactors )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -150,7 +150,7 @@ TEST( ConcurrentIncrementalSmoother, getFactors )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) TEST( ConcurrentIncrementalSmootherDL, getLinearizationPoint )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -201,19 +201,19 @@ TEST( ConcurrentIncrementalSmoother, getLinearizationPoint )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getOrdering ) TEST( ConcurrentIncrementalSmootherDL, getOrdering )
{ {
// TODO: Think about how to check ordering... // TODO: Think about how to check ordering...
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getDelta ) TEST( ConcurrentIncrementalSmootherDL, getDelta )
{ {
// TODO: Think about how to check ordering... // TODO: Think about how to check ordering...
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, calculateEstimate ) TEST( ConcurrentIncrementalSmootherDL, calculateEstimate )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -287,7 +287,7 @@ TEST( ConcurrentIncrementalSmoother, calculateEstimate )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, update_empty ) TEST( ConcurrentIncrementalSmootherDL, update_empty )
{ {
// Create a set of optimizer parameters // Create a set of optimizer parameters
ISAM2Params parameters; ISAM2Params parameters;
@ -300,7 +300,7 @@ TEST( ConcurrentIncrementalSmoother, update_empty )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, update_multiple ) TEST( ConcurrentIncrementalSmootherDL, update_multiple )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -358,7 +358,7 @@ TEST( ConcurrentIncrementalSmoother, update_multiple )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, synchronize_empty ) TEST( ConcurrentIncrementalSmootherDL, synchronize_empty )
{ {
// Create a set of optimizer parameters // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params parameters; ISAM2Params parameters;

24
gtsam_unstable/nonlinear/tests/testConcurrentIncrementalSmootherGN.cpp
View File

@ -73,7 +73,7 @@ Values BatchOptimize(const NonlinearFactorGraph& graph, const Values& theta, int
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, equals ) TEST( ConcurrentIncrementalSmootherGN, equals )
{ {
// TODO: Test 'equals' more vigorously // TODO: Test 'equals' more vigorously
@ -99,7 +99,7 @@ TEST( ConcurrentIncrementalSmoother, equals )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getFactors ) TEST( ConcurrentIncrementalSmootherGN, getFactors )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -150,7 +150,7 @@ TEST( ConcurrentIncrementalSmoother, getFactors )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getLinearizationPoint ) TEST( ConcurrentIncrementalSmootherGN, getLinearizationPoint )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -201,19 +201,19 @@ TEST( ConcurrentIncrementalSmoother, getLinearizationPoint )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getOrdering ) TEST( ConcurrentIncrementalSmootherGN, getOrdering )
{ {
// TODO: Think about how to check ordering... // TODO: Think about how to check ordering...
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, getDelta ) TEST( ConcurrentIncrementalSmootherGN, getDelta )
{ {
// TODO: Think about how to check ordering... // TODO: Think about how to check ordering...
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, calculateEstimate ) TEST( ConcurrentIncrementalSmootherGN, calculateEstimate )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -287,7 +287,7 @@ TEST( ConcurrentIncrementalSmoother, calculateEstimate )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, update_empty ) TEST( ConcurrentIncrementalSmootherGN, update_empty )
{ {
// Create a set of optimizer parameters // Create a set of optimizer parameters
ISAM2Params parameters; ISAM2Params parameters;
@ -300,7 +300,7 @@ TEST( ConcurrentIncrementalSmoother, update_empty )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, update_multiple ) TEST( ConcurrentIncrementalSmootherGN, update_multiple )
{ {
// Create a Concurrent Batch Smoother // Create a Concurrent Batch Smoother
ISAM2Params parameters; ISAM2Params parameters;
@ -358,7 +358,7 @@ TEST( ConcurrentIncrementalSmoother, update_multiple )
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( ConcurrentIncrementalSmoother, synchronize_empty ) TEST( ConcurrentIncrementalSmootherGN, synchronize_empty )
{ {
// Create a set of optimizer parameters // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params 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 // Create a set of optimizer parameters
ISAM2Params parameters; ISAM2Params parameters;

598
gtsam_unstable/slam/BetweenFactorEM.h
View File

@ -1,299 +1,299 @@
/* ---------------------------------------------------------------------------- /* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation, * GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415 * Atlanta, Georgia 30332-0415
* All Rights Reserved * All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list) * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information * See LICENSE for the license information
* -------------------------------------------------------------------------- */ * -------------------------------------------------------------------------- */
/** /**
* @file BetweenFactorEM.h * @file BetweenFactorEM.h
* @author Vadim Indelman * @author Vadim Indelman
**/ **/
#pragma once #pragma once
#include <ostream> #include <ostream>
#include <gtsam/base/Testable.h> #include <gtsam/base/Testable.h>
#include <gtsam/base/Lie.h> #include <gtsam/base/Lie.h>
#include <gtsam/nonlinear/NonlinearFactor.h> #include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/linear/GaussianFactor.h> #include <gtsam/linear/GaussianFactor.h>
namespace gtsam { namespace gtsam {
/** /**
* A class for a measurement predicted by "between(config[key1],config[key2])" * A class for a measurement predicted by "between(config[key1],config[key2])"
* @tparam VALUE the Value type * @tparam VALUE the Value type
* @addtogroup SLAM * @addtogroup SLAM
*/ */
template<class VALUE> template<class VALUE>
class BetweenFactorEM: public NonlinearFactor { class BetweenFactorEM: public NonlinearFactor {
public: public:
typedef VALUE T; typedef VALUE T;
private: private:
typedef BetweenFactorEM<VALUE> This; typedef BetweenFactorEM<VALUE> This;
typedef gtsam::NonlinearFactor Base; typedef gtsam::NonlinearFactor Base;
gtsam::Key key1_; gtsam::Key key1_;
gtsam::Key key2_; gtsam::Key key2_;
VALUE measured_; /** The measurement */ VALUE measured_; /** The measurement */
SharedGaussian model_inlier_; SharedGaussian model_inlier_;
SharedGaussian model_outlier_; SharedGaussian model_outlier_;
double prior_inlier_; double prior_inlier_;
double prior_outlier_; double prior_outlier_;
/** concept check by type */ /** concept check by type */
GTSAM_CONCEPT_LIE_TYPE(T) GTSAM_CONCEPT_LIE_TYPE(T)
GTSAM_CONCEPT_TESTABLE_TYPE(T) GTSAM_CONCEPT_TESTABLE_TYPE(T)
public: public:
// shorthand for a smart pointer to a factor // shorthand for a smart pointer to a factor
typedef typename boost::shared_ptr<BetweenFactorEM> shared_ptr; typedef typename boost::shared_ptr<BetweenFactorEM> shared_ptr;
/** default constructor - only use for serialization */ /** default constructor - only use for serialization */
BetweenFactorEM() {} BetweenFactorEM() {}
/** Constructor */ /** Constructor */
BetweenFactorEM(Key key1, Key key2, const VALUE& measured, BetweenFactorEM(Key key1, Key key2, const VALUE& measured,
const SharedGaussian& model_inlier, const SharedGaussian& model_outlier, const SharedGaussian& model_inlier, const SharedGaussian& model_outlier,
const double prior_inlier, const double prior_outlier) : const double prior_inlier, const double prior_outlier) :
Base(cref_list_of<2>(key1)(key2)), key1_(key1), key2_(key2), measured_(measured), Base(cref_list_of<2>(key1)(key2)), key1_(key1), key2_(key2), measured_(measured),
model_inlier_(model_inlier), model_outlier_(model_outlier), model_inlier_(model_inlier), model_outlier_(model_outlier),
prior_inlier_(prior_inlier), prior_outlier_(prior_outlier){ prior_inlier_(prior_inlier), prior_outlier_(prior_outlier){
} }
virtual ~BetweenFactorEM() {} virtual ~BetweenFactorEM() {}
/** implement functions needed for Testable */ /** implement functions needed for Testable */
/** print */ /** print */
virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const { virtual void print(const std::string& s, const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
std::cout << s << "BetweenFactorEM(" std::cout << s << "BetweenFactorEM("
<< keyFormatter(key1_) << "," << keyFormatter(key1_) << ","
<< keyFormatter(key2_) << ")\n"; << keyFormatter(key2_) << ")\n";
measured_.print(" measured: "); measured_.print(" measured: ");
model_inlier_->print(" noise model inlier: "); model_inlier_->print(" noise model inlier: ");
model_outlier_->print(" noise model outlier: "); model_outlier_->print(" noise model outlier: ");
std::cout << "(prior_inlier, prior_outlier_) = (" std::cout << "(prior_inlier, prior_outlier_) = ("
<< prior_inlier_ << "," << prior_inlier_ << ","
<< prior_outlier_ << ")\n"; << prior_outlier_ << ")\n";
// Base::print(s, keyFormatter); // Base::print(s, keyFormatter);
} }
/** equals */ /** equals */
virtual bool equals(const NonlinearFactor& f, double tol=1e-9) const { virtual bool equals(const NonlinearFactor& f, double tol=1e-9) const {
const This *t = dynamic_cast<const This*> (&f); const This *t = dynamic_cast<const This*> (&f);
if(t && Base::equals(f)) if(t && Base::equals(f))
return key1_ == t->key1_ && key2_ == t->key2_ && return key1_ == t->key1_ && key2_ == t->key2_ &&
// model_inlier_->equals(t->model_inlier_ ) && // TODO: fix here // model_inlier_->equals(t->model_inlier_ ) && // TODO: fix here
// model_outlier_->equals(t->model_outlier_ ) && // model_outlier_->equals(t->model_outlier_ ) &&
prior_outlier_ == t->prior_outlier_ && prior_inlier_ == t->prior_inlier_ && measured_.equals(t->measured_); prior_outlier_ == t->prior_outlier_ && prior_inlier_ == t->prior_inlier_ && measured_.equals(t->measured_);
else else
return false; return false;
} }
/** implement functions needed to derive from Factor */ /** implement functions needed to derive from Factor */
/* ************************************************************************* */ /* ************************************************************************* */
virtual double error(const gtsam::Values& x) const { virtual double error(const gtsam::Values& x) const {
return whitenedError(x).squaredNorm(); return whitenedError(x).squaredNorm();
} }
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
* Linearize a non-linearFactorN to get a gtsam::GaussianFactor, * 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$ * \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$ * Hence \f$ b = z - h(x) = - \mathtt{error\_vector}(x) \f$
*/ */
/* This version of linearize recalculates the noise model each time */ /* This version of linearize recalculates the noise model each time */
virtual boost::shared_ptr<gtsam::GaussianFactor> linearize(const gtsam::Values& x) const { virtual boost::shared_ptr<gtsam::GaussianFactor> linearize(const gtsam::Values& x) const {
// Only linearize if the factor is active // Only linearize if the factor is active
if (!this->active(x)) if (!this->active(x))
return boost::shared_ptr<gtsam::JacobianFactor>(); return boost::shared_ptr<gtsam::JacobianFactor>();
//std::cout<<"About to linearize"<<std::endl; //std::cout<<"About to linearize"<<std::endl;
gtsam::Matrix A1, A2; gtsam::Matrix A1, A2;
std::vector<gtsam::Matrix> A(this->size()); std::vector<gtsam::Matrix> A(this->size());
gtsam::Vector b = -whitenedError(x, A); gtsam::Vector b = -whitenedError(x, A);
A1 = A[0]; A1 = A[0];
A2 = A[1]; A2 = A[1];
return gtsam::GaussianFactor::shared_ptr( return gtsam::GaussianFactor::shared_ptr(
new gtsam::JacobianFactor(key1_, A1, key2_, A2, b, gtsam::noiseModel::Unit::Create(b.size()))); new gtsam::JacobianFactor(key1_, A1, key2_, A2, b, gtsam::noiseModel::Unit::Create(b.size())));
} }
/* ************************************************************************* */ /* ************************************************************************* */
gtsam::Vector whitenedError(const gtsam::Values& x, gtsam::Vector whitenedError(const gtsam::Values& x,
boost::optional<std::vector<gtsam::Matrix>&> H = boost::none) const { boost::optional<std::vector<gtsam::Matrix>&> H = boost::none) const {
bool debug = true; bool debug = true;
const T& p1 = x.at<T>(key1_); const T& p1 = x.at<T>(key1_);
const T& p2 = x.at<T>(key2_); const T& p2 = x.at<T>(key2_);
Matrix H1, H2; Matrix H1, H2;
T hx = p1.between(p2, H1, H2); // h(x) T hx = p1.between(p2, H1, H2); // h(x)
// manifold equivalent of h(x)-z -> log(z,h(x)) // manifold equivalent of h(x)-z -> log(z,h(x))
Vector err = measured_.localCoordinates(hx); Vector err = measured_.localCoordinates(hx);
// Calculate indicator probabilities (inlier and outlier) // Calculate indicator probabilities (inlier and outlier)
Vector p_inlier_outlier = calcIndicatorProb(x); Vector p_inlier_outlier = calcIndicatorProb(x);
double p_inlier = p_inlier_outlier[0]; double p_inlier = p_inlier_outlier[0];
double p_outlier = p_inlier_outlier[1]; double p_outlier = p_inlier_outlier[1];
Vector err_wh_inlier = model_inlier_->whiten(err); Vector err_wh_inlier = model_inlier_->whiten(err);
Vector err_wh_outlier = model_outlier_->whiten(err); Vector err_wh_outlier = model_outlier_->whiten(err);
Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R(); Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
Matrix invCov_outlier = model_outlier_->R().transpose() * model_outlier_->R(); Matrix invCov_outlier = model_outlier_->R().transpose() * model_outlier_->R();
Vector err_wh_eq; Vector err_wh_eq;
err_wh_eq.resize(err_wh_inlier.rows()*2); 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(); err_wh_eq << sqrt(p_inlier) * err_wh_inlier.array() , sqrt(p_outlier) * err_wh_outlier.array();
if (H){ if (H){
// stack Jacobians for the two indicators for each of the key // stack Jacobians for the two indicators for each of the key
Matrix H1_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H1); Matrix H1_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H1);
Matrix H1_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H1); Matrix H1_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H1);
Matrix H1_aug = gtsam::stack(2, &H1_inlier, &H1_outlier); Matrix H1_aug = gtsam::stack(2, &H1_inlier, &H1_outlier);
Matrix H2_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H2); Matrix H2_inlier = sqrt(p_inlier)*model_inlier_->Whiten(H2);
Matrix H2_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H2); Matrix H2_outlier = sqrt(p_outlier)*model_outlier_->Whiten(H2);
Matrix H2_aug = gtsam::stack(2, &H2_inlier, &H2_outlier); Matrix H2_aug = gtsam::stack(2, &H2_inlier, &H2_outlier);
(*H)[0].resize(H1_aug.rows(),H1_aug.cols()); (*H)[0].resize(H1_aug.rows(),H1_aug.cols());
(*H)[1].resize(H2_aug.rows(),H2_aug.cols()); (*H)[1].resize(H2_aug.rows(),H2_aug.cols());
(*H)[0] = H1_aug; (*H)[0] = H1_aug;
(*H)[1] = H2_aug; (*H)[1] = H2_aug;
} }
if (debug){ if (debug){
// std::cout<<"unwhitened error: "<<err[0]<<" "<<err[1]<<" "<<err[2]<<std::endl; // std::cout<<"unwhitened error: "<<err[0]<<" "<<err[1]<<" "<<err[2]<<std::endl;
// std::cout<<"err_wh_inlier: "<<err_wh_inlier[0]<<" "<<err_wh_inlier[1]<<" "<<err_wh_inlier[2]<<std::endl; // std::cout<<"err_wh_inlier: "<<err_wh_inlier[0]<<" "<<err_wh_inlier[1]<<" "<<err_wh_inlier[2]<<std::endl;
// std::cout<<"err_wh_outlier: "<<err_wh_outlier[0]<<" "<<err_wh_outlier[1]<<" "<<err_wh_outlier[2]<<std::endl; // std::cout<<"err_wh_outlier: "<<err_wh_outlier[0]<<" "<<err_wh_outlier[1]<<" "<<err_wh_outlier[2]<<std::endl;
// //
// std::cout<<"p_inlier, p_outlier, sumP: "<<p_inlier<<" "<<p_outlier<<" " << sumP << std::endl; // std::cout<<"p_inlier, p_outlier, sumP: "<<p_inlier<<" "<<p_outlier<<" " << sumP << std::endl;
// //
// std::cout<<"prior_inlier_, prior_outlier_: "<<prior_inlier_<<" "<<prior_outlier_<< std::endl; // std::cout<<"prior_inlier_, prior_outlier_: "<<prior_inlier_<<" "<<prior_outlier_<< std::endl;
// //
// double s_inl = -0.5 * err_wh_inlier.dot(err_wh_inlier); // double s_inl = -0.5 * err_wh_inlier.dot(err_wh_inlier);
// double s_outl = -0.5 * err_wh_outlier.dot(err_wh_outlier); // double s_outl = -0.5 * err_wh_outlier.dot(err_wh_outlier);
// std::cout<<"s_inl, s_outl: "<<s_inl<<" "<<s_outl<<std::endl; // std::cout<<"s_inl, s_outl: "<<s_inl<<" "<<s_outl<<std::endl;
// //
// std::cout<<"norm of invCov_inlier, invCov_outlier: "<<invCov_inlier.norm()<<" "<<invCov_outlier.norm()<<std::endl; // std::cout<<"norm of invCov_inlier, invCov_outlier: "<<invCov_inlier.norm()<<" "<<invCov_outlier.norm()<<std::endl;
// double q_inl = invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) ); // double q_inl = invCov_inlier.norm() * exp( -0.5 * err_wh_inlier.dot(err_wh_inlier) );
// double q_outl = invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) ); // double q_outl = invCov_outlier.norm() * exp( -0.5 * err_wh_outlier.dot(err_wh_outlier) );
// std::cout<<"q_inl, q_outl: "<<q_inl<<" "<<q_outl<<std::endl; // std::cout<<"q_inl, q_outl: "<<q_inl<<" "<<q_outl<<std::endl;
// Matrix Cov_inlier = invCov_inlier.inverse(); // Matrix Cov_inlier = invCov_inlier.inverse();
// Matrix Cov_outlier = invCov_outlier.inverse(); // Matrix Cov_outlier = invCov_outlier.inverse();
// std::cout<<"Cov_inlier: "<<std::endl<< // std::cout<<"Cov_inlier: "<<std::endl<<
// Cov_inlier(0,0) << " " << Cov_inlier(0,1) << " " << Cov_inlier(0,2) <<std::endl<< // Cov_inlier(0,0) << " " << Cov_inlier(0,1) << " " << Cov_inlier(0,2) <<std::endl<<
// Cov_inlier(1,0) << " " << Cov_inlier(1,1) << " " << Cov_inlier(1,2) <<std::endl<< // Cov_inlier(1,0) << " " << Cov_inlier(1,1) << " " << Cov_inlier(1,2) <<std::endl<<
// Cov_inlier(2,0) << " " << Cov_inlier(2,1) << " " << Cov_inlier(2,2) <<std::endl; // Cov_inlier(2,0) << " " << Cov_inlier(2,1) << " " << Cov_inlier(2,2) <<std::endl;
// std::cout<<"Cov_outlier: "<<std::endl<< // std::cout<<"Cov_outlier: "<<std::endl<<
// Cov_outlier(0,0) << " " << Cov_outlier(0,1) << " " << Cov_outlier(0,2) <<std::endl<< // Cov_outlier(0,0) << " " << Cov_outlier(0,1) << " " << Cov_outlier(0,2) <<std::endl<<
// Cov_outlier(1,0) << " " << Cov_outlier(1,1) << " " << Cov_outlier(1,2) <<std::endl<< // Cov_outlier(1,0) << " " << Cov_outlier(1,1) << " " << Cov_outlier(1,2) <<std::endl<<
// Cov_outlier(2,0) << " " << Cov_outlier(2,1) << " " << Cov_outlier(2,2) <<std::endl; // Cov_outlier(2,0) << " " << Cov_outlier(2,1) << " " << Cov_outlier(2,2) <<std::endl;
// std::cout<<"===="<<std::endl; // std::cout<<"===="<<std::endl;
} }
return err_wh_eq; return err_wh_eq;
} }
/* ************************************************************************* */ /* ************************************************************************* */
gtsam::Vector calcIndicatorProb(const gtsam::Values& x) const { gtsam::Vector calcIndicatorProb(const gtsam::Values& x) const {
Vector err = unwhitenedError(x); Vector err = unwhitenedError(x);
// Calculate indicator probabilities (inlier and outlier) // Calculate indicator probabilities (inlier and outlier)
Vector err_wh_inlier = model_inlier_->whiten(err); Vector err_wh_inlier = model_inlier_->whiten(err);
Vector err_wh_outlier = model_outlier_->whiten(err); Vector err_wh_outlier = model_outlier_->whiten(err);
Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R(); Matrix invCov_inlier = model_inlier_->R().transpose() * model_inlier_->R();
Matrix invCov_outlier = model_outlier_->R().transpose() * model_outlier_->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_inlier = prior_inlier_ * std::sqrt(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 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; double sumP = p_inlier + p_outlier;
p_inlier /= sumP; p_inlier /= sumP;
p_outlier /= sumP; p_outlier /= sumP;
// Bump up near-zero probabilities (as in linerFlow.h) // Bump up near-zero probabilities (as in linerFlow.h)
double minP = 0.05; // == 0.1 / 2 indicator variables double minP = 0.05; // == 0.1 / 2 indicator variables
if (p_inlier < minP || p_outlier < minP){ if (p_inlier < minP || p_outlier < minP){
if (p_inlier < minP) if (p_inlier < minP)
p_inlier = minP; p_inlier = minP;
if (p_outlier < minP) if (p_outlier < minP)
p_outlier = minP; p_outlier = minP;
sumP = p_inlier + p_outlier; sumP = p_inlier + p_outlier;
p_inlier /= sumP; p_inlier /= sumP;
p_outlier /= sumP; p_outlier /= sumP;
} }
return Vector_(2, p_inlier, p_outlier); return Vector_(2, p_inlier, p_outlier);
} }
/* ************************************************************************* */ /* ************************************************************************* */
gtsam::Vector unwhitenedError(const gtsam::Values& x) const { gtsam::Vector unwhitenedError(const gtsam::Values& x) const {
bool debug = true; bool debug = true;
const T& p1 = x.at<T>(key1_); const T& p1 = x.at<T>(key1_);
const T& p2 = x.at<T>(key2_); const T& p2 = x.at<T>(key2_);
Matrix H1, H2; Matrix H1, H2;
T hx = p1.between(p2, H1, H2); // h(x) T hx = p1.between(p2, H1, H2); // h(x)
return measured_.localCoordinates(hx); return measured_.localCoordinates(hx);
} }
/* ************************************************************************* */ /* ************************************************************************* */
/** return the measured */ /** return the measured */
const VALUE& measured() const { const VALUE& measured() const {
return measured_; return measured_;
} }
/** number of variables attached to this factor */ /** number of variables attached to this factor */
std::size_t size() const { std::size_t size() const {
return 2; return 2;
} }
virtual size_t dim() const { virtual size_t dim() const {
return model_inlier_->R().rows() + model_inlier_->R().cols(); return model_inlier_->R().rows() + model_inlier_->R().cols();
} }
private: private:
/** Serialization function */ /** Serialization function */
friend class boost::serialization::access; friend class boost::serialization::access;
template<class ARCHIVE> template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) { void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("NonlinearFactor", ar & boost::serialization::make_nvp("NonlinearFactor",
boost::serialization::base_object<Base>(*this)); boost::serialization::base_object<Base>(*this));
ar & BOOST_SERIALIZATION_NVP(measured_); ar & BOOST_SERIALIZATION_NVP(measured_);
} }
}; // \class BetweenFactorEM }; // \class BetweenFactorEM
} /// namespace gtsam } /// namespace gtsam

954
gtsam_unstable/slam/tests/testBetweenFactorEM.cpp
View File

@ -1,477 +1,477 @@
/** /**
* @file testBetweenFactorEM.cpp * @file testBetweenFactorEM.cpp
* @brief Unit test for the BetweenFactorEM * @brief Unit test for the BetweenFactorEM
* @author Vadim Indelman * @author Vadim Indelman
*/ */
#include <CppUnitLite/TestHarness.h> #include <CppUnitLite/TestHarness.h>
#include <gtsam_unstable/slam/BetweenFactorEM.h> #include <gtsam_unstable/slam/BetweenFactorEM.h>
#include <gtsam/geometry/Pose2.h> #include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/Values.h> #include <gtsam/nonlinear/Values.h>
#include <gtsam/base/LieVector.h> #include <gtsam/base/LieVector.h>
#include <gtsam/base/numericalDerivative.h> #include <gtsam/base/numericalDerivative.h>
#include <gtsam/slam/BetweenFactor.h> #include <gtsam/slam/BetweenFactor.h>
//#include <gtsam/nonlinear/NonlinearOptimizer.h> //#include <gtsam/nonlinear/NonlinearOptimizer.h>
//#include <gtsam/nonlinear/NonlinearFactorGraph.h> //#include <gtsam/nonlinear/NonlinearFactorGraph.h>
//#include <gtsam/linear/GaussianSequentialSolver.h> //#include <gtsam/linear/GaussianSequentialSolver.h>
using namespace std; using namespace std;
using namespace gtsam; using namespace gtsam;
/* ************************************************************************* */ /* ************************************************************************* */
LieVector predictionError(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactorEM<gtsam::Pose2>& factor){ LieVector predictionError(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactorEM<gtsam::Pose2>& factor){
gtsam::Values values; gtsam::Values values;
values.insert(key1, p1); values.insert(key1, p1);
values.insert(key2, p2); values.insert(key2, p2);
// LieVector err = factor.whitenedError(values); // LieVector err = factor.whitenedError(values);
// return err; // return err;
return LieVector::Expmap(factor.whitenedError(values)); return LieVector::Expmap(factor.whitenedError(values));
} }
/* ************************************************************************* */ /* ************************************************************************* */
LieVector predictionError_standard(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactor<gtsam::Pose2>& factor){ LieVector predictionError_standard(const Pose2& p1, const Pose2& p2, const gtsam::Key& key1, const gtsam::Key& key2, const BetweenFactor<gtsam::Pose2>& factor){
gtsam::Values values; gtsam::Values values;
values.insert(key1, p1); values.insert(key1, p1);
values.insert(key2, p2); values.insert(key2, p2);
// LieVector err = factor.whitenedError(values); // LieVector err = factor.whitenedError(values);
// return err; // return err;
return LieVector::Expmap(factor.whitenedError(values)); return LieVector::Expmap(factor.whitenedError(values));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( BetweenFactorEM, ConstructorAndEquals) TEST( BetweenFactorEM, ConstructorAndEquals)
{ {
gtsam::Key key1(1); gtsam::Key key1(1);
gtsam::Key key2(2); gtsam::Key key2(2);
gtsam::Pose2 p1(10.0, 15.0, 0.1); gtsam::Pose2 p1(10.0, 15.0, 0.1);
gtsam::Pose2 p2(15.0, 15.0, 0.3); gtsam::Pose2 p2(15.0, 15.0, 0.3);
gtsam::Pose2 noise(0.5, 0.4, 0.01); gtsam::Pose2 noise(0.5, 0.4, 0.01);
gtsam::Pose2 rel_pose_ideal = p1.between(p2); gtsam::Pose2 rel_pose_ideal = p1.between(p2);
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise); 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_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))); SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 5, 5, 1.0)));
double prior_outlier = 0.5; double prior_outlier = 0.5;
double prior_inlier = 0.5; double prior_inlier = 0.5;
// Constructor // Constructor
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
// Equals // Equals
CHECK(assert_equal(f, g, 1e-5)); CHECK(assert_equal(f, g, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( BetweenFactorEM, EvaluateError) TEST( BetweenFactorEM, EvaluateError)
{ {
gtsam::Key key1(1); gtsam::Key key1(1);
gtsam::Key key2(2); gtsam::Key key2(2);
// Inlier test // Inlier test
gtsam::Pose2 p1(10.0, 15.0, 0.1); gtsam::Pose2 p1(10.0, 15.0, 0.1);
gtsam::Pose2 p2(15.0, 15.0, 0.3); gtsam::Pose2 p2(15.0, 15.0, 0.3);
gtsam::Pose2 noise(0.5, 0.4, 0.01); gtsam::Pose2 noise(0.5, 0.4, 0.01);
gtsam::Pose2 rel_pose_ideal = p1.between(p2); gtsam::Pose2 rel_pose_ideal = p1.between(p2);
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise); 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_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))); SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 50.0, 50.0, 10.0)));
gtsam::Values values; gtsam::Values values;
values.insert(key1, p1); values.insert(key1, p1);
values.insert(key2, p2); values.insert(key2, p2);
double prior_outlier = 0.5; double prior_outlier = 0.5;
double prior_inlier = 0.5; double prior_inlier = 0.5;
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
Vector actual_err_wh = f.whitenedError(values); 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_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]); 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 // in case of inlier, inlier-mode whitented error should be dominant
CHECK(actual_err_wh_inlier.norm() > 1000.0*actual_err_wh_outlier.norm()); 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: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier.norm()<<endl; cout << "Inlier test. norm of actual_err_wh_inlier, actual_err_wh_outlier: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier.norm()<<endl;
cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl; cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl;
// Outlier test // Outlier test
noise = gtsam::Pose2(10.5, 20.4, 2.01); noise = gtsam::Pose2(10.5, 20.4, 2.01);
gtsam::Pose2 rel_pose_msr_test2 = rel_pose_ideal.compose(noise); gtsam::Pose2 rel_pose_msr_test2 = rel_pose_ideal.compose(noise);
BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr_test2, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> g(key1, key2, rel_pose_msr_test2, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
actual_err_wh = g.whitenedError(values); 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_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]); 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 // in case of outlier, outlier-mode whitented error should be dominant
CHECK(actual_err_wh_inlier.norm() < 1000.0*actual_err_wh_outlier.norm()); 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: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier<<endl; cout << "Outlier test. norm of actual_err_wh_inlier, actual_err_wh_outlier: "<<actual_err_wh_inlier.norm()<<","<<actual_err_wh_outlier<<endl;
cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl; cout<<actual_err_wh[0]<<" "<<actual_err_wh[1]<<" "<<actual_err_wh[2]<<actual_err_wh[3]<<" "<<actual_err_wh[4]<<" "<<actual_err_wh[5]<<endl;
// Compare with standard between factor for the inlier case // Compare with standard between factor for the inlier case
prior_outlier = 0.0; prior_outlier = 0.0;
prior_inlier = 1.0; prior_inlier = 1.0;
BetweenFactorEM<gtsam::Pose2> h_EM(key1, key2, rel_pose_msr, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> h_EM(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
actual_err_wh = h_EM.whitenedError(values); 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]); actual_err_wh_inlier = Vector_(3, actual_err_wh[0], actual_err_wh[1], actual_err_wh[2]);
BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier ); BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier );
Vector actual_err_wh_stnd = h.whitenedError(values); Vector actual_err_wh_stnd = h.whitenedError(values);
cout<<"actual_err_wh: "<<actual_err_wh_inlier[0]<<", "<<actual_err_wh_inlier[1]<<", "<<actual_err_wh_inlier[2]<<endl; cout<<"actual_err_wh: "<<actual_err_wh_inlier[0]<<", "<<actual_err_wh_inlier[1]<<", "<<actual_err_wh_inlier[2]<<endl;
cout<<"actual_err_wh_stnd: "<<actual_err_wh_stnd[0]<<", "<<actual_err_wh_stnd[1]<<", "<<actual_err_wh_stnd[2]<<endl; cout<<"actual_err_wh_stnd: "<<actual_err_wh_stnd[0]<<", "<<actual_err_wh_stnd[1]<<", "<<actual_err_wh_stnd[2]<<endl;
CHECK( assert_equal(actual_err_wh_inlier, actual_err_wh_stnd, 1e-8)); CHECK( assert_equal(actual_err_wh_inlier, actual_err_wh_stnd, 1e-8));
} }
///* ************************************************************************** */ ///* ************************************************************************** */
TEST (BetweenFactorEM, jacobian ) { TEST (BetweenFactorEM, jacobian ) {
gtsam::Key key1(1); gtsam::Key key1(1);
gtsam::Key key2(2); gtsam::Key key2(2);
// Inlier test // Inlier test
gtsam::Pose2 p1(10.0, 15.0, 0.1); gtsam::Pose2 p1(10.0, 15.0, 0.1);
gtsam::Pose2 p2(15.0, 15.0, 0.3); gtsam::Pose2 p2(15.0, 15.0, 0.3);
gtsam::Pose2 noise(0.5, 0.4, 0.01); gtsam::Pose2 noise(0.5, 0.4, 0.01);
gtsam::Pose2 rel_pose_ideal = p1.between(p2); gtsam::Pose2 rel_pose_ideal = p1.between(p2);
gtsam::Pose2 rel_pose_msr = rel_pose_ideal.compose(noise); 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_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))); SharedGaussian model_outlier(noiseModel::Diagonal::Sigmas(gtsam::Vector_(3, 50.0, 50.0, 10.0)));
gtsam::Values values; gtsam::Values values;
values.insert(key1, p1); values.insert(key1, p1);
values.insert(key2, p2); values.insert(key2, p2);
double prior_outlier = 0.0; double prior_outlier = 0.0;
double prior_inlier = 1.0; double prior_inlier = 1.0;
BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier, BetweenFactorEM<gtsam::Pose2> f(key1, key2, rel_pose_msr, model_inlier, model_outlier,
prior_inlier, prior_outlier); prior_inlier, prior_outlier);
std::vector<gtsam::Matrix> H_actual(2); std::vector<gtsam::Matrix> H_actual(2);
Vector actual_err_wh = f.whitenedError(values, H_actual); Vector actual_err_wh = f.whitenedError(values, H_actual);
Matrix H1_actual = H_actual[0]; Matrix H1_actual = H_actual[0];
Matrix H2_actual = H_actual[1]; Matrix H2_actual = H_actual[1];
// compare to standard between factor // compare to standard between factor
BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier ); BetweenFactor<gtsam::Pose2> h(key1, key2, rel_pose_msr, model_inlier );
Vector actual_err_wh_stnd = h.whitenedError(values); 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]); 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)); CHECK( assert_equal(actual_err_wh_stnd, actual_err_wh_inlier, 1e-8));
std::vector<gtsam::Matrix> H_actual_stnd_unwh(2); std::vector<gtsam::Matrix> H_actual_stnd_unwh(2);
(void)h.unwhitenedError(values, H_actual_stnd_unwh); (void)h.unwhitenedError(values, H_actual_stnd_unwh);
Matrix H1_actual_stnd_unwh = H_actual_stnd_unwh[0]; Matrix H1_actual_stnd_unwh = H_actual_stnd_unwh[0];
Matrix H2_actual_stnd_unwh = H_actual_stnd_unwh[1]; Matrix H2_actual_stnd_unwh = H_actual_stnd_unwh[1];
Matrix H1_actual_stnd = model_inlier->Whiten(H1_actual_stnd_unwh); Matrix H1_actual_stnd = model_inlier->Whiten(H1_actual_stnd_unwh);
Matrix H2_actual_stnd = model_inlier->Whiten(H2_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(H1_actual_stnd, H1_actual, 1e-8));
// CHECK( assert_equal(H2_actual_stnd, H2_actual, 1e-8)); // CHECK( assert_equal(H2_actual_stnd, H2_actual, 1e-8));
double stepsize = 1.0e-9; double stepsize = 1.0e-9;
Matrix H1_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, _1, p2, key1, key2, f), p1, stepsize); Matrix H1_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, _1, p2, key1, key2, f), p1, stepsize);
Matrix H2_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, p1, _1, key1, key2, f), p2, stepsize); Matrix H2_expected = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError, p1, _1, key1, key2, f), p2, stepsize);
// try to check numerical derivatives of a standard between factor // try to check numerical derivatives of a standard between factor
Matrix H1_expected_stnd = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError_standard, _1, p2, key1, key2, h), p1, stepsize); Matrix H1_expected_stnd = gtsam::numericalDerivative11<LieVector, Pose2>(boost::bind(&predictionError_standard, _1, p2, key1, key2, h), p1, stepsize);
CHECK( assert_equal(H1_expected_stnd, H1_actual_stnd, 1e-5)); CHECK( assert_equal(H1_expected_stnd, H1_actual_stnd, 1e-5));
CHECK( assert_equal(H1_expected, H1_actual, 1e-8)); CHECK( assert_equal(H1_expected, H1_actual, 1e-8));
CHECK( assert_equal(H2_expected, H2_actual, 1e-8)); CHECK( assert_equal(H2_expected, H2_actual, 1e-8));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( InertialNavFactor, Equals) TEST( InertialNavFactor, Equals)
{ {
// gtsam::Key Pose1(11); // gtsam::Key Pose1(11);
// gtsam::Key Pose2(12); // gtsam::Key Pose2(12);
// gtsam::Key Vel1(21); // gtsam::Key Vel1(21);
// gtsam::Key Vel2(22); // gtsam::Key Vel2(22);
// gtsam::Key Bias1(31); // gtsam::Key Bias1(31);
// //
// Vector measurement_acc(Vector_(3,0.1,0.2,0.4)); // Vector measurement_acc(Vector_(3,0.1,0.2,0.4));
// Vector measurement_gyro(Vector_(3, -0.2, 0.5, 0.03)); // Vector measurement_gyro(Vector_(3, -0.2, 0.5, 0.03));
// //
// double measurement_dt(0.1); // double measurement_dt(0.1);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> g(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> g(Pose1, Vel1, Bias1, Pose2, Vel2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// CHECK(assert_equal(f, g, 1e-5)); // CHECK(assert_equal(f, g, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( InertialNavFactor, Predict) TEST( InertialNavFactor, Predict)
{ {
// gtsam::Key PoseKey1(11); // gtsam::Key PoseKey1(11);
// gtsam::Key PoseKey2(12); // gtsam::Key PoseKey2(12);
// gtsam::Key VelKey1(21); // gtsam::Key VelKey1(21);
// gtsam::Key VelKey2(22); // gtsam::Key VelKey2(22);
// gtsam::Key BiasKey1(31); // gtsam::Key BiasKey1(31);
// //
// double measurement_dt(0.1); // double measurement_dt(0.1);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// //
// // First test: zero angular motion, some acceleration // // First test: zero angular motion, some acceleration
// Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81)); // Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81));
// Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0)); // Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// //
// Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00)); // Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
// LieVector Vel1(3, 0.50, -0.50, 0.40); // LieVector Vel1(3, 0.50, -0.50, 0.40);
// imuBias::ConstantBias Bias1; // imuBias::ConstantBias Bias1;
// Pose3 expectedPose2(Rot3(), Point3(2.05, 0.95, 3.04)); // Pose3 expectedPose2(Rot3(), Point3(2.05, 0.95, 3.04));
// LieVector expectedVel2(3, 0.51, -0.48, 0.43); // LieVector expectedVel2(3, 0.51, -0.48, 0.43);
// Pose3 actualPose2; // Pose3 actualPose2;
// LieVector actualVel2; // LieVector actualVel2;
// f.predict(Pose1, Vel1, Bias1, actualPose2, actualVel2); // f.predict(Pose1, Vel1, Bias1, actualPose2, actualVel2);
// //
// CHECK(assert_equal(expectedPose2, actualPose2, 1e-5)); // CHECK(assert_equal(expectedPose2, actualPose2, 1e-5));
// CHECK(assert_equal(expectedVel2, actualVel2, 1e-5)); // CHECK(assert_equal(expectedVel2, actualVel2, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( InertialNavFactor, ErrorPosVel) TEST( InertialNavFactor, ErrorPosVel)
{ {
// gtsam::Key PoseKey1(11); // gtsam::Key PoseKey1(11);
// gtsam::Key PoseKey2(12); // gtsam::Key PoseKey2(12);
// gtsam::Key VelKey1(21); // gtsam::Key VelKey1(21);
// gtsam::Key VelKey2(22); // gtsam::Key VelKey2(22);
// gtsam::Key BiasKey1(31); // gtsam::Key BiasKey1(31);
// //
// double measurement_dt(0.1); // double measurement_dt(0.1);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// //
// // First test: zero angular motion, some acceleration // // First test: zero angular motion, some acceleration
// Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81)); // Vector measurement_acc(Vector_(3,0.1,0.2,0.3-9.81));
// Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0)); // Vector measurement_gyro(Vector_(3, 0.0, 0.0, 0.0));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// //
// Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00)); // Pose3 Pose1(Rot3(), Point3(2.00, 1.00, 3.00));
// Pose3 Pose2(Rot3(), Point3(2.05, 0.95, 3.04)); // Pose3 Pose2(Rot3(), Point3(2.05, 0.95, 3.04));
// LieVector Vel1(3, 0.50, -0.50, 0.40); // LieVector Vel1(3, 0.50, -0.50, 0.40);
// LieVector Vel2(3, 0.51, -0.48, 0.43); // LieVector Vel2(3, 0.51, -0.48, 0.43);
// imuBias::ConstantBias Bias1; // imuBias::ConstantBias Bias1;
// //
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2)); // Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
// Vector ExpectedErr(zero(9)); // Vector ExpectedErr(zero(9));
// //
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5)); // CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( InertialNavFactor, ErrorRot) TEST( InertialNavFactor, ErrorRot)
{ {
// gtsam::Key PoseKey1(11); // gtsam::Key PoseKey1(11);
// gtsam::Key PoseKey2(12); // gtsam::Key PoseKey2(12);
// gtsam::Key VelKey1(21); // gtsam::Key VelKey1(21);
// gtsam::Key VelKey2(22); // gtsam::Key VelKey2(22);
// gtsam::Key BiasKey1(31); // gtsam::Key BiasKey1(31);
// //
// double measurement_dt(0.1); // double measurement_dt(0.1);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// // Second test: zero angular motion, some acceleration // // Second test: zero angular motion, some acceleration
// Vector measurement_acc(Vector_(3,0.0,0.0,0.0-9.81)); // Vector measurement_acc(Vector_(3,0.0,0.0,0.0-9.81));
// Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3)); // Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// //
// Pose3 Pose1(Rot3(), Point3(2.0,1.0,3.0)); // Pose3 Pose1(Rot3(), Point3(2.0,1.0,3.0));
// Pose3 Pose2(Rot3::Expmap(measurement_gyro*measurement_dt), 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 Vel1(3,0.0,0.0,0.0);
// LieVector Vel2(3,0.0,0.0,0.0); // LieVector Vel2(3,0.0,0.0,0.0);
// imuBias::ConstantBias Bias1; // imuBias::ConstantBias Bias1;
// //
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2)); // Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
// Vector ExpectedErr(zero(9)); // Vector ExpectedErr(zero(9));
// //
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5)); // CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( InertialNavFactor, ErrorRotPosVel) TEST( InertialNavFactor, ErrorRotPosVel)
{ {
// gtsam::Key PoseKey1(11); // gtsam::Key PoseKey1(11);
// gtsam::Key PoseKey2(12); // gtsam::Key PoseKey2(12);
// gtsam::Key VelKey1(21); // gtsam::Key VelKey1(21);
// gtsam::Key VelKey2(22); // gtsam::Key VelKey2(22);
// gtsam::Key BiasKey1(31); // gtsam::Key BiasKey1(31);
// //
// double measurement_dt(0.1); // double measurement_dt(0.1);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// // Second test: zero angular motion, some acceleration - generated in matlab // // Second test: zero angular motion, some acceleration - generated in matlab
// Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343)); // Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343));
// Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3)); // Vector measurement_gyro(Vector_(3, 0.1, 0.2, 0.3));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> f(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// //
// Rot3 R1(0.487316618, 0.125253866, 0.86419557, // Rot3 R1(0.487316618, 0.125253866, 0.86419557,
// 0.580273724, 0.693095498, -0.427669306, // 0.580273724, 0.693095498, -0.427669306,
// -0.652537293, 0.709880342, 0.265075427); // -0.652537293, 0.709880342, 0.265075427);
// Point3 t1(2.0,1.0,3.0); // Point3 t1(2.0,1.0,3.0);
// Pose3 Pose1(R1, t1); // Pose3 Pose1(R1, t1);
// LieVector Vel1(3,0.5,-0.5,0.4); // LieVector Vel1(3,0.5,-0.5,0.4);
// Rot3 R2(0.473618898, 0.119523052, 0.872582019, // Rot3 R2(0.473618898, 0.119523052, 0.872582019,
// 0.609241153, 0.67099888, -0.422594037, // 0.609241153, 0.67099888, -0.422594037,
// -0.636011287, 0.731761397, 0.244979388); // -0.636011287, 0.731761397, 0.244979388);
// Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800); // Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
// Pose3 Pose2(R2, t2); // Pose3 Pose2(R2, t2);
// LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000); // LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
// imuBias::ConstantBias Bias1; // imuBias::ConstantBias Bias1;
// //
// Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2)); // Vector ActualErr(f.evaluateError(Pose1, Vel1, Bias1, Pose2, Vel2));
// Vector ExpectedErr(zero(9)); // Vector ExpectedErr(zero(9));
// //
// CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5)); // CHECK(assert_equal(ExpectedErr, ActualErr, 1e-5));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST (InertialNavFactor, Jacobian ) { TEST (InertialNavFactor, Jacobian ) {
// gtsam::Key PoseKey1(11); // gtsam::Key PoseKey1(11);
// gtsam::Key PoseKey2(12); // gtsam::Key PoseKey2(12);
// gtsam::Key VelKey1(21); // gtsam::Key VelKey1(21);
// gtsam::Key VelKey2(22); // gtsam::Key VelKey2(22);
// gtsam::Key BiasKey1(31); // gtsam::Key BiasKey1(31);
// //
// double measurement_dt(0.01); // double measurement_dt(0.01);
// Vector world_g(Vector_(3, 0.0, 0.0, 9.81)); // 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 // 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 ECEF_omega_earth(Vector_(3, 0.0, 0.0, 7.292115e-5));
// gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth); // gtsam::Vector world_omega_earth(world_R_ECEF.matrix() * ECEF_omega_earth);
// //
// SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1)); // SharedGaussian model(noiseModel::Isotropic::Sigma(9, 0.1));
// //
// Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343)); // Vector measurement_acc(Vector_(3, 6.501390843381716, -6.763926150509185, -2.300389940090343));
// Vector measurement_gyro(Vector_(3, 3.14, 3.14/2, -3.14)); // Vector measurement_gyro(Vector_(3, 3.14, 3.14/2, -3.14));
// //
// InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> factor(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model); // InertialNavFactor<Pose3, LieVector, imuBias::ConstantBias> factor(PoseKey1, VelKey1, BiasKey1, PoseKey2, VelKey2, measurement_acc, measurement_gyro, measurement_dt, world_g, world_rho, world_omega_earth, model);
// //
// Rot3 R1(0.487316618, 0.125253866, 0.86419557, // Rot3 R1(0.487316618, 0.125253866, 0.86419557,
// 0.580273724, 0.693095498, -0.427669306, // 0.580273724, 0.693095498, -0.427669306,
// -0.652537293, 0.709880342, 0.265075427); // -0.652537293, 0.709880342, 0.265075427);
// Point3 t1(2.0,1.0,3.0); // Point3 t1(2.0,1.0,3.0);
// Pose3 Pose1(R1, t1); // Pose3 Pose1(R1, t1);
// LieVector Vel1(3,0.5,-0.5,0.4); // LieVector Vel1(3,0.5,-0.5,0.4);
// Rot3 R2(0.473618898, 0.119523052, 0.872582019, // Rot3 R2(0.473618898, 0.119523052, 0.872582019,
// 0.609241153, 0.67099888, -0.422594037, // 0.609241153, 0.67099888, -0.422594037,
// -0.636011287, 0.731761397, 0.244979388); // -0.636011287, 0.731761397, 0.244979388);
// Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800); // Point3 t2(2.052670960415706, 0.977252139079380, 2.942482135362800);
// Pose3 Pose2(R2, t2); // Pose3 Pose2(R2, t2);
// LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000); // LieVector Vel2(3,0.510000000000000, -0.480000000000000, 0.430000000000000);
// imuBias::ConstantBias Bias1; // imuBias::ConstantBias Bias1;
// //
// Matrix H1_actual, H2_actual, H3_actual, H4_actual, H5_actual; // 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)); // 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 // // Checking for Pose part in the jacobians
// // ****** // // ******
// Matrix H1_actualPose(H1_actual.block(0,0,6,H1_actual.cols())); // 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 H2_actualPose(H2_actual.block(0,0,6,H2_actual.cols()));
// Matrix H3_actualPose(H3_actual.block(0,0,6,H3_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 H4_actualPose(H4_actual.block(0,0,6,H4_actual.cols()));
// Matrix H5_actualPose(H5_actual.block(0,0,6,H5_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 // // Calculate the Jacobian matrices H1 until H5 using the numerical derivative function
// gtsam::Matrix H1_expectedPose, H2_expectedPose, H3_expectedPose, H4_expectedPose, H5_expectedPose; // gtsam::Matrix H1_expectedPose, H2_expectedPose, H3_expectedPose, H4_expectedPose, H5_expectedPose;
// H1_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); // H1_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1);
// H2_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); // H2_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1);
// H3_expectedPose = gtsam::numericalDerivative11<Pose3, imuBias::ConstantBias>(boost::bind(&predictionErrorPose, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); // H3_expectedPose = gtsam::numericalDerivative11<Pose3, imuBias::ConstantBias>(boost::bind(&predictionErrorPose, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1);
// H4_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); // H4_expectedPose = gtsam::numericalDerivative11<Pose3, Pose3>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2);
// H5_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2); // H5_expectedPose = gtsam::numericalDerivative11<Pose3, LieVector>(boost::bind(&predictionErrorPose, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2);
// //
// // Verify they are equal for this choice of state // // Verify they are equal for this choice of state
// CHECK( gtsam::assert_equal(H1_expectedPose, H1_actualPose, 1e-6)); // CHECK( gtsam::assert_equal(H1_expectedPose, H1_actualPose, 1e-6));
// CHECK( gtsam::assert_equal(H2_expectedPose, H2_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(H3_expectedPose, H3_actualPose, 1e-6));
// CHECK( gtsam::assert_equal(H4_expectedPose, H4_actualPose, 1e-6)); // CHECK( gtsam::assert_equal(H4_expectedPose, H4_actualPose, 1e-6));
// CHECK( gtsam::assert_equal(H5_expectedPose, H5_actualPose, 1e-6)); // CHECK( gtsam::assert_equal(H5_expectedPose, H5_actualPose, 1e-6));
// //
// // Checking for Vel part in the jacobians // // Checking for Vel part in the jacobians
// // ****** // // ******
// Matrix H1_actualVel(H1_actual.block(6,0,3,H1_actual.cols())); // 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 H2_actualVel(H2_actual.block(6,0,3,H2_actual.cols()));
// Matrix H3_actualVel(H3_actual.block(6,0,3,H3_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 H4_actualVel(H4_actual.block(6,0,3,H4_actual.cols()));
// Matrix H5_actualVel(H5_actual.block(6,0,3,H5_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 // // Calculate the Jacobian matrices H1 until H5 using the numerical derivative function
// gtsam::Matrix H1_expectedVel, H2_expectedVel, H3_expectedVel, H4_expectedVel, H5_expectedVel; // gtsam::Matrix H1_expectedVel, H2_expectedVel, H3_expectedVel, H4_expectedVel, H5_expectedVel;
// H1_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1); // H1_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, _1, Vel1, Bias1, Pose2, Vel2, factor), Pose1);
// H2_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1); // H2_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, _1, Bias1, Pose2, Vel2, factor), Vel1);
// H3_expectedVel = gtsam::numericalDerivative11<LieVector, imuBias::ConstantBias>(boost::bind(&predictionErrorVel, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1); // H3_expectedVel = gtsam::numericalDerivative11<LieVector, imuBias::ConstantBias>(boost::bind(&predictionErrorVel, Pose1, Vel1, _1, Pose2, Vel2, factor), Bias1);
// H4_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2); // H4_expectedVel = gtsam::numericalDerivative11<LieVector, Pose3>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, _1, Vel2, factor), Pose2);
// H5_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2); // H5_expectedVel = gtsam::numericalDerivative11<LieVector, LieVector>(boost::bind(&predictionErrorVel, Pose1, Vel1, Bias1, Pose2, _1, factor), Vel2);
// //
// // Verify they are equal for this choice of state // // Verify they are equal for this choice of state
// CHECK( gtsam::assert_equal(H1_expectedVel, H1_actualVel, 1e-6)); // CHECK( gtsam::assert_equal(H1_expectedVel, H1_actualVel, 1e-6));
// CHECK( gtsam::assert_equal(H2_expectedVel, H2_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(H3_expectedVel, H3_actualVel, 1e-6));
// CHECK( gtsam::assert_equal(H4_expectedVel, H4_actualVel, 1e-6)); // CHECK( gtsam::assert_equal(H4_expectedVel, H4_actualVel, 1e-6));
// CHECK( gtsam::assert_equal(H5_expectedVel, H5_actualVel, 1e-6)); // CHECK( gtsam::assert_equal(H5_expectedVel, H5_actualVel, 1e-6));
} }
/* ************************************************************************* */ /* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);} int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */ /* ************************************************************************* */

27
gtsam_unstable/slam/tests/testSmartProjectionFactor.cpp
View File

@ -173,8 +173,8 @@ TEST( SmartProjectionFactor, noisy ){
/* ************************************************************************* */ /* ************************************************************************* */
TEST( SmartProjectionFactor, 3poses ){ TEST( SmartProjectionFactor, 3poses_smart_projection_factor ){
cout << " ************************ MultiProjectionFactor: 3 cams + 3 landmarks **********************" << endl; cout << " ************************ SmartProjectionFactor: 3 cams + 3 landmarks **********************" << endl;
Symbol x1('X', 1); Symbol x1('X', 1);
Symbol x2('X', 2); Symbol x2('X', 2);
@ -239,17 +239,19 @@ TEST( SmartProjectionFactor, 3poses ){
graph.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior)); graph.push_back(PriorFactor<Pose3>(x1, pose1, noisePrior));
graph.push_back(PriorFactor<Pose3>(x2, pose2, noisePrior)); graph.push_back(PriorFactor<Pose3>(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 values;
values.insert(x1, pose1); values.insert(x1, pose1);
values.insert(x2, pose2*noise_pose); values.insert(x2, pose2);
values.insert(x3, pose3); // initialize third pose with some noise, we expect it to move back to original pose3
values.insert(x3, pose3*noise_pose);
values.at<Pose3>(x3).print("Smart: Pose3 before optimization: ");
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
params.verbosity = NonlinearOptimizerParams::ERROR; params.verbosity = NonlinearOptimizerParams::ERROR;
Values result; Values result;
gttic_(SmartProjectionFactor); gttic_(SmartProjectionFactor);
LevenbergMarquardtOptimizer optimizer(graph, values, params); LevenbergMarquardtOptimizer optimizer(graph, values, params);
@ -257,7 +259,9 @@ TEST( SmartProjectionFactor, 3poses ){
gttoc_(SmartProjectionFactor); gttoc_(SmartProjectionFactor);
tictoc_finishedIteration_(); tictoc_finishedIteration_();
result.print("results of 3 camera, 3 landmark optimization \n"); // result.print("results of 3 camera, 3 landmark optimization \n");
result.at<Pose3>(x3).print("Smart: Pose3 after optimization: ");
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
tictoc_print_(); tictoc_print_();
} }
@ -265,7 +269,7 @@ TEST( SmartProjectionFactor, 3poses ){
/* ************************************************************************* */ /* ************************************************************************* */
TEST( SmartProjectionFactor, 3poses_projection_factor ){ 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 x1('X', 1);
Symbol x2('X', 2); Symbol x2('X', 2);
@ -287,7 +291,6 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){
// create third camera 1 meter above the first camera // create third camera 1 meter above the first camera
Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0)); Pose3 pose3 = pose1 * Pose3(Rot3(), Point3(0,-1,0));
pose3.print("Pose3: ");
SimpleCamera cam3(pose3, *K); SimpleCamera cam3(pose3, *K);
// three landmarks ~5 meters infront of camera // three landmarks ~5 meters infront of camera
@ -324,6 +327,7 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){
values.insert(L(1), landmark1); values.insert(L(1), landmark1);
values.insert(L(2), landmark2); values.insert(L(2), landmark2);
values.insert(L(3), landmark3); values.insert(L(3), landmark3);
// values.at<Pose3>(x3).print("Pose3 before optimization: ");
LevenbergMarquardtParams params; LevenbergMarquardtParams params;
// params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA; // params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
@ -331,14 +335,15 @@ TEST( SmartProjectionFactor, 3poses_projection_factor ){
LevenbergMarquardtOptimizer optimizer(graph, values, params); LevenbergMarquardtOptimizer optimizer(graph, values, params);
Values result = optimizer.optimize(); Values result = optimizer.optimize();
result.print("Regular Projection Factor: results of 3 camera, 3 landmark optimization \n"); // result.at<Pose3>(x3).print("Pose3 after optimization: ");
EXPECT(assert_equal(pose3,result.at<Pose3>(x3)));
} }
/* ************************************************************************* */ /* ************************************************************************* */
TEST( SmartProjectionFactor, Hessian ){ TEST( SmartProjectionFactor, Hessian ){
cout << " ************************ Normal ProjectionFactor: Hessian **********************" << endl; cout << " ************************ SmartProjectionFactor: Hessian **********************" << endl;
Symbol x1('X', 1); Symbol x1('X', 1);
Symbol x2('X', 2); Symbol x2('X', 2);

226
matlab/gtsam_examples/IMUKittiExample.m
View File

@ -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')

191
matlab/gtsam_examples/IMUKittiExampleAdvanced.m Normal file
View File

@ -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

149
matlab/gtsam_examples/IMUKittiExampleGPS.m Normal file
View File

@ -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')

126
matlab/gtsam_examples/IMUKittiExampleSimple.m
View File

@ -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')

152
matlab/gtsam_examples/IMUKittiExampleVO.m Normal file
View File

@ -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')