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Merge branch 'develop'

release/4.3a0
Luca 2014-04-15 10:57:20 -04:00
parent f63db8859b fe81828a2c
commit bd218feada
29 changed files with 4061 additions and 594 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

79
examples/Data/dubrovnik-3-7-18-pre.txt Normal file
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@ -0,0 +1,79 @@
3 7 18
0 0 -3.859900e+02 3.871200e+02
1 0 -3.844000e+01 4.921200e+02
2 0 -6.679200e+02 1.231100e+02
0 1 3.838800e+02 -1.529999e+01
1 1 5.597500e+02 -1.061500e+02
0 2 5.915500e+02 1.364400e+02
1 2 8.638600e+02 -2.346997e+01
2 2 4.947200e+02 1.125200e+02
0 3 5.925000e+02 1.257500e+02
1 3 8.610800e+02 -3.521997e+01
2 3 4.985400e+02 1.015600e+02
0 4 3.487200e+02 5.583800e+02
1 4 7.760300e+02 4.835300e+02
2 4 7.780029e+00 3.263500e+02
0 5 1.401001e+01 9.642001e+01
1 5 2.071300e+02 1.183600e+02
1 6 5.431801e+02 2.948100e+02
2 6 -5.841998e+01 1.108300e+02
-1.6943983532198115e-02
1.1171804676513932e-02
2.4643508831711991e-03
7.3030995682610689e-01
-2.6490818471043420e-01
-1.7127892627337182e+00
1.4300319432711681e+03
-7.5572758535864072e-08
3.2377569465570913e-14
1.5049725341485708e-02
-1.8504564785154357e-01
-2.9278402790141456e-01
-1.0590476152349551e+00
-3.6017862414345798e-02
-1.5720340175803784e+00
1.4321374541298685e+03
-7.3171919892612292e-08
3.1759419019880947e-14
-3.0793597986873011e-01
3.2077907982952031e-01
2.2253985096991455e-01
8.5034483295909009e+00
6.7499603629668741e+00
-3.6383814384447088e+00
1.5720470590375264e+03
-1.5962623661947355e-08
-1.6507904848058800e-14
-1.2055995050700867e+01
1.2838775976205760e+01
-4.1099369264082803e+01
6.4168905904672933e+00
3.8897031177598462e-01
-2.3586282709150449e+01
1.3051100355717297e+01
3.8387587111611952e+00
-2.9777932175344951e+01
1.3060946673472820e+01
3.5910521225905803e+00
-2.9759080795372942e+01
1.4265764475421857e+01
2.4096216156436530e+01
-5.4823971067225500e+01
-2.5292283211391348e-01
2.2166082122808284e+00
-2.1712127480255084e+01
7.6465738085189585e+00
1.4185331909846619e+01
-5.2070299568846060e+01

5
gtsam/3rdparty/CMakeLists.txt vendored
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@ -25,8 +25,11 @@ if(NOT GTSAM_USE_SYSTEM_EIGEN)
FILES_MATCHING PATTERN "*.h")
endif()
option(GTSAM_BUILD_METIS "Build metis library" ON)
option(GTSAM_BUILD_METIS_EXECUTABLES "Build metis library executables" OFF)
add_subdirectory(metis-5.1.0)
if(GTSAM_BUILD_METIS)
add_subdirectory(metis-5.1.0)
endif(GTSAM_BUILD_METIS)
############ NOTE: When updating GeographicLib be sure to disable building their examples
############ and unit tests by commenting out their lines:
# add_subdirectory (examples)

8
gtsam/3rdparty/metis-5.1.0/CMakeLists.txt vendored
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@ -2,9 +2,13 @@ cmake_minimum_required(VERSION 2.8)
project(METIS)
# Add flags for currect directory and below
add_definitions(-Wno-unused-variable)
if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
add_definitions(-Wno-unused-variable)
add_definitions(-Wno-sometimes-uninitialized)
endif()
add_definitions(-Wno-unknown-pragmas)
add_definitions(-Wno-sometimes-uninitialized)
add_definitions(-Wunused-but-set-variable)
set(GKLIB_PATH ${PROJECT_SOURCE_DIR}/GKlib CACHE PATH "path to GKlib")
set(SHARED FALSE CACHE BOOL "build a shared library")

2
gtsam/3rdparty/metis-5.1.0/GKlib/GKlibSystem.cmake vendored
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@ -33,7 +33,7 @@ if(CMAKE_COMPILER_IS_GNUCC)
set(GKlib_COPTIONS "${GKlib_COPTIONS} -fPIC")
endif(NOT MINGW)
# GCC warnings.
set(GKlib_COPTIONS "${GKlib_COPTIONS} -Wall -pedantic -Wno-unused-but-set-variable -Wno-unused-variable -Wno-unknown-pragmas")
set(GKlib_COPTIONS "${GKlib_COPTIONS} -Wall -pedantic -Wno-unused-variable -Wno-unknown-pragmas")
elseif(${CMAKE_C_COMPILER_ID} MATCHES "Sun")
# Sun insists on -xc99.
set(GKlib_COPTIONS "${GKlib_COPTIONS} -xc99")

27
gtsam/geometry/EssentialMatrix.h
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@ -157,6 +157,33 @@ public:
/// @}
private:
/// @name Advanced Interface
/// @{
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("EssentialMatrix",
boost::serialization::base_object<Value>(*this));
ar & BOOST_SERIALIZATION_NVP(aRb_);
ar & BOOST_SERIALIZATION_NVP(aTb_);
ar & boost::serialization::make_nvp("E11", E_(0,0));
ar & boost::serialization::make_nvp("E12", E_(0,1));
ar & boost::serialization::make_nvp("E13", E_(0,2));
ar & boost::serialization::make_nvp("E21", E_(1,0));
ar & boost::serialization::make_nvp("E22", E_(1,1));
ar & boost::serialization::make_nvp("E23", E_(1,2));
ar & boost::serialization::make_nvp("E31", E_(2,0));
ar & boost::serialization::make_nvp("E32", E_(2,1));
ar & boost::serialization::make_nvp("E33", E_(2,2));
}
/// @}
};
} // gtsam

18
gtsam/geometry/Unit3.h
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@ -136,6 +136,24 @@ public:
Vector localCoordinates(const Unit3& s) const;
/// @}
private:
/// @name Advanced Interface
/// @{
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & boost::serialization::make_nvp("Unit3",
boost::serialization::base_object<Value>(*this));
ar & BOOST_SERIALIZATION_NVP(p_);
ar & BOOST_SERIALIZATION_NVP(B_);
}
/// @}
};
} // namespace gtsam

3
gtsam/linear/GaussianFactor.h
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@ -99,6 +99,9 @@ namespace gtsam {
/// Return the diagonal of the Hessian for this factor
virtual VectorValues hessianDiagonal() const = 0;
/// Return the diagonal of the Hessian for this factor (raw memory version)
virtual void hessianDiagonal(double* d) const = 0;
/// Return the block diagonal of the Hessian for this factor
virtual std::map<Key,Matrix> hessianBlockDiagonal() const = 0;

3
gtsam/linear/GaussianFactorGraph.h
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@ -88,6 +88,9 @@ namespace gtsam {
template<class DERIVEDFACTOR>
GaussianFactorGraph(const FactorGraph<DERIVEDFACTOR>& graph) : Base(graph) {}
/** Virtual destructor */
virtual ~GaussianFactorGraph() {}
/// @name Testable
/// @{

18
gtsam/linear/HessianFactor.cpp
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@ -358,6 +358,24 @@ VectorValues HessianFactor::hessianDiagonal() const {
return d;
}
/* ************************************************************************* */
// TODO: currently assumes all variables of the same size 9 and keys arranged from 0 to n
void HessianFactor::hessianDiagonal(double* d) const {
// Use eigen magic to access raw memory
typedef Eigen::Matrix<double, 9, 1> DVector;
typedef Eigen::Map<DVector> DMap;
// Loop over all variables in the factor
for (DenseIndex pos = 0; pos < (DenseIndex)size(); ++pos) {
Key j = keys_[pos];
// Get the diagonal block, and insert its diagonal
Matrix B = info_(pos, pos).selfadjointView();
DVector dj = B.diagonal();
DMap(d + 9 * j) += dj;
}
}
/* ************************************************************************* */
map<Key,Matrix> HessianFactor::hessianBlockDiagonal() const {
map<Key,Matrix> blocks;

3
gtsam/linear/HessianFactor.h
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@ -340,6 +340,9 @@ namespace gtsam {
/// Return the diagonal of the Hessian for this factor
virtual VectorValues hessianDiagonal() const;
/* ************************************************************************* */
virtual void hessianDiagonal(double* d) const;
/// Return the block diagonal of the Hessian for this factor
virtual std::map<Key,Matrix> hessianBlockDiagonal() const;

1170
gtsam/linear/JacobianFactor.cpp
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File diff suppressed because it is too large Load Diff

3
gtsam/linear/JacobianFactor.h
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@ -185,6 +185,9 @@ namespace gtsam {
/// Return the diagonal of the Hessian for this factor
virtual VectorValues hessianDiagonal() const;
/* ************************************************************************* */
virtual void hessianDiagonal(double* d) const;
/// Return the block diagonal of the Hessian for this factor
virtual std::map<Key,Matrix> hessianBlockDiagonal() const;

2
gtsam/linear/tests/testHessianFactorUnordered.cpp
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@ -471,7 +471,7 @@ TEST(HessianFactor, hessianDiagonal)
LONGS_EQUAL(2,actualBD.size());
EXPECT(assert_equal(G11,actualBD[0]));
EXPECT(assert_equal(G22,actualBD[1]));
}
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}

10
gtsam/nonlinear/ISAM2.cpp
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@ -151,13 +151,13 @@ void ISAM2Clique::print(const std::string& s, const KeyFormatter& formatter) con
}
/* ************************************************************************* */
ISAM2::ISAM2(const ISAM2Params& params): params_(params) {
ISAM2::ISAM2(const ISAM2Params& params): params_(params), update_count_(0) {
if(params_.optimizationParams.type() == typeid(ISAM2DoglegParams))
doglegDelta_ = boost::get<ISAM2DoglegParams>(params_.optimizationParams).initialDelta;
}
/* ************************************************************************* */
ISAM2::ISAM2() {
ISAM2::ISAM2() : update_count_(0) {
if(params_.optimizationParams.type() == typeid(ISAM2DoglegParams))
doglegDelta_ = boost::get<ISAM2DoglegParams>(params_.optimizationParams).initialDelta;
}
@ -521,8 +521,7 @@ ISAM2Result ISAM2::update(
gttic(ISAM2_update);
static int count = 0;
count++;
this->update_count_++;
lastAffectedVariableCount = 0;
lastAffectedFactorCount = 0;
@ -533,7 +532,8 @@ ISAM2Result ISAM2::update(
ISAM2Result result;
if(params_.enableDetailedResults)
result.detail = ISAM2Result::DetailedResults();
const bool relinearizeThisStep = force_relinearize || (params_.enableRelinearization && count % params_.relinearizeSkip == 0);
const bool relinearizeThisStep = force_relinearize
|| (params_.enableRelinearization && update_count_ % params_.relinearizeSkip == 0);
if(verbose) {
cout << "ISAM2::update\n";

2
gtsam/nonlinear/ISAM2.h
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@ -468,6 +468,8 @@ protected:
* variables and thus cannot have their linearization points changed. */
FastSet<Key> fixedVariables_;
int update_count_; ///< Counter incremented every update(), used to determine periodic relinearization
public:
typedef ISAM2 This; ///< This class

2
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
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@ -99,6 +99,8 @@ void LevenbergMarquardtParams::print(const std::string& str) const {
std::cout << " lambdaLowerBound: " << lambdaLowerBound << "\n";
std::cout << " minModelFidelity: " << minModelFidelity << "\n";
std::cout << " diagonalDamping: " << diagonalDamping << "\n";
std::cout << " min_diagonal: " << min_diagonal_ << "\n";
std::cout << " max_diagonal: " << max_diagonal_ << "\n";
std::cout << " verbosityLM: "
<< verbosityLMTranslator(verbosityLM) << "\n";
std::cout.flush();

10
gtsam_unstable/CMakeLists.txt
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@ -6,10 +6,13 @@ set (gtsam_unstable_subdirs
discrete
dynamics
nonlinear
partition
slam
)
if(GTSAM_BUILD_METIS) # Only build partition if metis is built
set (gtsam_unstable_subdirs ${gtsam_unstable_subdirs} partition)
endif(GTSAM_BUILD_METIS)
set(GTSAM_UNSTABLE_BOOST_LIBRARIES ${GTSAM_BOOST_LIBRARIES})
add_custom_target(check.unstable COMMAND ${CMAKE_CTEST_COMMAND} --output-on-failure)
@ -47,10 +50,13 @@ set(gtsam_unstable_srcs
${discrete_srcs}
${dynamics_srcs}
${nonlinear_srcs}
${partition_srcs}
${slam_srcs}
)
if(GTSAM_BUILD_METIS) # Only build partition if metis is built
set (gtsam_unstable_srcs ${gtsam_unstable_srcs} ${partition_srcs})
endif(GTSAM_BUILD_METIS)
# Versions - same as core gtsam library
set(gtsam_unstable_version ${GTSAM_VERSION_MAJOR}.${GTSAM_VERSION_MINOR}.${GTSAM_VERSION_PATCH})
set(gtsam_unstable_soversion ${GTSAM_VERSION_MAJOR})

5
gtsam_unstable/partition/GenericGraph.cpp
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@ -9,6 +9,7 @@
#include <boost/foreach.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/make_shared.hpp>
#include <boost/lexical_cast.hpp>
#include <gtsam/base/DSFVector.h>
@ -468,9 +469,9 @@ namespace gtsam { namespace partition {
}
if (minFoundConstraintsPerCamera < minNrConstraintsPerCamera)
throw runtime_error("checkSingularity:minConstraintsPerCamera < " + minFoundConstraintsPerCamera);
throw runtime_error("checkSingularity:minConstraintsPerCamera < " + boost::lexical_cast<string>(minFoundConstraintsPerCamera));
if (minFoundConstraintsPerLandmark < minNrConstraintsPerLandmark)
throw runtime_error("checkSingularity:minConstraintsPerLandmark < " + minFoundConstraintsPerLandmark);
throw runtime_error("checkSingularity:minConstraintsPerLandmark < " + boost::lexical_cast<string>(minFoundConstraintsPerLandmark));
}
}} // namespace

384
gtsam_unstable/slam/ImplicitSchurFactor.h Normal file
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@ -0,0 +1,384 @@
/**
* @file ImplicitSchurFactor.h
* @brief A new type of linear factor (GaussianFactor), which is subclass of GaussianFactor
* @author Frank Dellaert
* @author Luca Carlone
*/
#pragma once
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/VectorValues.h>
#include <boost/foreach.hpp>
#include <boost/make_shared.hpp>
#include <iostream>
namespace gtsam {
/**
* ImplicitSchurFactor
*/
template<size_t D> //
class ImplicitSchurFactor: public GaussianFactor {
public:
typedef ImplicitSchurFactor This; ///< Typedef to this class
typedef JacobianFactor Base; ///< Typedef to base class
typedef boost::shared_ptr<This> shared_ptr; ///< shared_ptr to this class
protected:
typedef Eigen::Matrix<double, 2, D> Matrix2D; ///< type of an F block
typedef Eigen::Matrix<double, 2, 3> Matrix23;
typedef Eigen::Matrix<double, D, D> MatrixDD; ///< camera hessian
typedef std::pair<Key, Matrix2D> KeyMatrix2D; ///< named F block
std::vector<KeyMatrix2D> Fblocks_; ///< All 2*D F blocks (one for each camera)
Matrix3 PointCovariance_; ///< the 3*3 matrix P = inv(E'E) (2*2 if degenerate)
Matrix E_; ///< The 2m*3 E Jacobian with respect to the point
Vector b_; ///< 2m-dimensional RHS vector
public:
/// Constructor
ImplicitSchurFactor() {
}
/// Construct from blcoks of F, E, inv(E'*E), and RHS vector b
ImplicitSchurFactor(const std::vector<KeyMatrix2D>& Fblocks, const Matrix& E,
const Matrix3& P, const Vector& b) :
Fblocks_(Fblocks), PointCovariance_(P), E_(E), b_(b) {
initKeys();
}
/// initialize keys from Fblocks
void initKeys() {
keys_.reserve(Fblocks_.size());
BOOST_FOREACH(const KeyMatrix2D& it, Fblocks_)
keys_.push_back(it.first);
}
/// Destructor
virtual ~ImplicitSchurFactor() {
}
// Write access, only use for construction!
inline std::vector<KeyMatrix2D>& Fblocks() {
return Fblocks_;
}
inline Matrix3& PointCovariance() {
return PointCovariance_;
}
inline Matrix& E() {
return E_;
}
inline Vector& b() {
return b_;
}
/// Get matrix P
inline const Matrix& getPointCovariance() const {
return PointCovariance_;
}
/// print
void print(const std::string& s, const KeyFormatter& formatter) const {
std::cout << " ImplicitSchurFactor " << std::endl;
Factor::print(s);
std::cout << " PointCovariance_ \n" << PointCovariance_ << std::endl;
std::cout << " E_ \n" << E_ << std::endl;
std::cout << " b_ \n" << b_.transpose() << std::endl;
}
/// equals
bool equals(const GaussianFactor& lf, double tol) const {
if (!dynamic_cast<const ImplicitSchurFactor*>(&lf))
return false;
else {
return false;
}
}
/// Degrees of freedom of camera
virtual DenseIndex getDim(const_iterator variable) const {
return D;
}
virtual Matrix augmentedJacobian() const {
throw std::runtime_error(
"ImplicitSchurFactor::augmentedJacobian non implemented");
return Matrix();
}
virtual std::pair<Matrix, Vector> jacobian() const {
throw std::runtime_error("ImplicitSchurFactor::jacobian non implemented");
return std::make_pair(Matrix(), Vector());
}
virtual Matrix augmentedInformation() const {
throw std::runtime_error(
"ImplicitSchurFactor::augmentedInformation non implemented");
return Matrix();
}
virtual Matrix information() const {
throw std::runtime_error(
"ImplicitSchurFactor::information non implemented");
return Matrix();
}
/// Return the diagonal of the Hessian for this factor
virtual VectorValues hessianDiagonal() const {
// diag(Hessian) = diag(F' * (I - E * PointCov * E') * F);
VectorValues d;
for (size_t pos = 0; pos < size(); ++pos) { // for each camera
Key j = keys_[pos];
// Calculate Fj'*Ej for the current camera (observing a single point)
// D x 3 = (D x 2) * (2 x 3)
const Matrix2D& Fj = Fblocks_[pos].second;
Eigen::Matrix<double, D, 3> FtE = Fj.transpose()
* E_.block<2, 3>(2 * pos, 0);
Eigen::Matrix<double, D, 1> dj;
for (size_t k = 0; k < D; ++k) { // for each diagonal element of the camera hessian
// Vector column_k_Fj = Fj.col(k);
dj(k) = Fj.col(k).squaredNorm(); // dot(column_k_Fj, column_k_Fj);
// Vector column_k_FtE = FtE.row(k);
// (1 x 1) = (1 x 3) * (3 * 3) * (3 x 1)
dj(k) -= FtE.row(k) * PointCovariance_ * FtE.row(k).transpose();
}
d.insert(j, dj);
}
return d;
}
/**
* @brief add the contribution of this factor to the diagonal of the hessian
* d(output) = d(input) + deltaHessianFactor
*/
void hessianDiagonal(double* d) const {
// diag(Hessian) = diag(F' * (I - E * PointCov * E') * F);
// Use eigen magic to access raw memory
typedef Eigen::Matrix<double, D, 1> DVector;
typedef Eigen::Map<DVector> DMap;
for (size_t pos = 0; pos < size(); ++pos) { // for each camera in the factor
Key j = keys_[pos];
// Calculate Fj'*Ej for the current camera (observing a single point)
// D x 3 = (D x 2) * (2 x 3)
const Matrix2D& Fj = Fblocks_[pos].second;
Eigen::Matrix<double, D, 3> FtE = Fj.transpose()
* E_.block<2, 3>(2 * pos, 0);
DVector dj;
for (size_t k = 0; k < D; ++k) { // for each diagonal element of the camera hessian
dj(k) = Fj.col(k).squaredNorm();
// (1 x 1) = (1 x 3) * (3 * 3) * (3 x 1)
dj(k) -= FtE.row(k) * PointCovariance_ * FtE.row(k).transpose();
}
DMap(d + D * j) += dj;
}
}
/// Return the block diagonal of the Hessian for this factor
virtual std::map<Key, Matrix> hessianBlockDiagonal() const {
std::map<Key, Matrix> blocks;
for (size_t pos = 0; pos < size(); ++pos) {
Key j = keys_[pos];
const Matrix2D& Fj = Fblocks_[pos].second;
// F'*F - F'*E*P*E'*F (9*2)*(2*9) - (9*2)*(2*3)*(3*3)*(3*2)*(2*9)
Eigen::Matrix<double, D, 3> FtE = Fj.transpose()
* E_.block<2, 3>(2 * pos, 0);
blocks[j] = Fj.transpose() * Fj
- FtE * PointCovariance_ * FtE.transpose();
// F'*(I - E*P*E')*F, TODO: this should work, but it does not :-(
// static const Eigen::Matrix<double, 2, 2> I2 = eye(2);
// Eigen::Matrix<double, 2, 2> Q = //
// I2 - E_.block<2, 3>(2 * pos, 0) * PointCovariance_ * E_.block<2, 3>(2 * pos, 0).transpose();
// blocks[j] = Fj.transpose() * Q * Fj;
}
return blocks;
}
virtual GaussianFactor::shared_ptr clone() const {
return boost::make_shared<ImplicitSchurFactor<D> >(Fblocks_,
PointCovariance_, E_, b_);
throw std::runtime_error("ImplicitSchurFactor::clone non implemented");
}
virtual bool empty() const {
return false;
}
virtual GaussianFactor::shared_ptr negate() const {
return boost::make_shared<ImplicitSchurFactor<D> >(Fblocks_,
PointCovariance_, E_, b_);
throw std::runtime_error("ImplicitSchurFactor::negate non implemented");
}
// Raw Vector version of y += F'*alpha*(I - E*P*E')*F*x, for testing
static
void multiplyHessianAdd(const Matrix& F, const Matrix& E,
const Matrix& PointCovariance, double alpha, const Vector& x, Vector& y) {
Vector e1 = F * x;
Vector d1 = E.transpose() * e1;
Vector d2 = PointCovariance * d1;
Vector e2 = E * d2;
Vector e3 = alpha * (e1 - e2);
y += F.transpose() * e3;
}
typedef std::vector<Vector2> Error2s;
/**
* @brief Calculate corrected error Q*e = (I - E*P*E')*e
*/
void projectError(const Error2s& e1, Error2s& e2) const {
// d1 = E.transpose() * e1 = (3*2m)*2m
Vector3 d1;
d1.setZero();
for (size_t k = 0; k < size(); k++)
d1 += E_.block < 2, 3 > (2 * k, 0).transpose() * e1[k];
// d2 = E.transpose() * e1 = (3*2m)*2m
Vector3 d2 = PointCovariance_ * d1;
// e3 = alpha*(e1 - E*d2) = 1*[2m-(2m*3)*3]
for (size_t k = 0; k < size(); k++)
e2[k] = e1[k] - E_.block < 2, 3 > (2 * k, 0) * d2;
}
/// needed to be GaussianFactor - (I - E*P*E')*(F*x - b)
virtual double error(const VectorValues& x) const {
// resize does not do malloc if correct size
e1.resize(size());
e2.resize(size());
// e1 = F * x - b = (2m*dm)*dm
for (size_t k = 0; k < size(); ++k)
e1[k] = Fblocks_[k].second * x.at(keys_[k]) - b_.segment < 2 > (k * 2);
projectError(e1, e2);
double result = 0;
for (size_t k = 0; k < size(); ++k)
result += dot(e2[k], e2[k]);
return 0.5 * result;
}
/// Scratch space for multiplyHessianAdd
mutable Error2s e1, e2;
/**
* @brief double* Hessian-vector multiply, i.e. y += F'*alpha*(I - E*P*E')*F*x
* RAW memory access! Assumes keys start at 0 and go to M-1, and x and and y are laid out that way
*/
void multiplyHessianAdd(double alpha, const double* x, double* y) const {
// Use eigen magic to access raw memory
typedef Eigen::Matrix<double, D, 1> DVector;
typedef Eigen::Map<DVector> DMap;
typedef Eigen::Map<const DVector> ConstDMap;
// resize does not do malloc if correct size
e1.resize(size());
e2.resize(size());
// e1 = F * x = (2m*dm)*dm
size_t k = 0;
BOOST_FOREACH(const KeyMatrix2D& it, Fblocks_) {
Key key = it.first;
e1[k++] = it.second * ConstDMap(x + D * key);
}
projectError(e1, e2);
// y += F.transpose()*e2 = (2d*2m)*2m
k = 0;
BOOST_FOREACH(const KeyMatrix2D& it, Fblocks_) {
Key key = it.first;
DMap(y + D * key) += it.second.transpose() * alpha * e2[k++];
}
}
void multiplyHessianAdd(double alpha, const double* x, double* y,
std::vector<size_t> keys) const {
}
;
/**
* @brief Hessian-vector multiply, i.e. y += F'*alpha*(I - E*P*E')*F*x
*/
void multiplyHessianAdd(double alpha, const VectorValues& x,
VectorValues& y) const {
// resize does not do malloc if correct size
e1.resize(size());
e2.resize(size());
// e1 = F * x = (2m*dm)*dm
for (size_t k = 0; k < size(); ++k)
e1[k] = Fblocks_[k].second * x.at(keys_[k]);
projectError(e1, e2);
// y += F.transpose()*e2 = (2d*2m)*2m
for (size_t k = 0; k < size(); ++k) {
Key key = keys_[k];
static const Vector empty;
std::pair<VectorValues::iterator, bool> it = y.tryInsert(key, empty);
Vector& yi = it.first->second;
// Create the value as a zero vector if it does not exist.
if (it.second)
yi = Vector::Zero(Fblocks_[k].second.cols());
yi += Fblocks_[k].second.transpose() * alpha * e2[k];
}
}
/**
* @brief Dummy version to measure overhead of key access
*/
void multiplyHessianDummy(double alpha, const VectorValues& x,
VectorValues& y) const {
BOOST_FOREACH(const KeyMatrix2D& Fi, Fblocks_) {
static const Vector empty;
Key key = Fi.first;
std::pair<VectorValues::iterator, bool> it = y.tryInsert(key, empty);
Vector& yi = it.first->second;
yi = x.at(key);
}
}
/**
* Calculate gradient, which is -F'Q*b, see paper
*/
VectorValues gradientAtZero() const {
// calculate Q*b
e1.resize(size());
e2.resize(size());
for (size_t k = 0; k < size(); k++)
e1[k] = b_.segment < 2 > (2 * k);
projectError(e1, e2);
// g = F.transpose()*e2
VectorValues g;
for (size_t k = 0; k < size(); ++k) {
Key key = keys_[k];
g.insert(key, -Fblocks_[k].second.transpose() * e2[k]);
}
// return it
return g;
}
};
// ImplicitSchurFactor
}

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/*
* @file JacobianFactorQ.h
* @date Oct 27, 2013
* @uthor Frank Dellaert
*/
#pragma once
#include "JacobianSchurFactor.h"
namespace gtsam {
/**
* JacobianFactor for Schur complement that uses Q noise model
*/
template<size_t D>
class JacobianFactorQ: public JacobianSchurFactor<D> {
typedef JacobianSchurFactor<D> Base;
public:
/// Default constructor
JacobianFactorQ() {
}
/// Constructor
JacobianFactorQ(const std::vector<typename Base::KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix3& P, const Vector& b,
const SharedDiagonal& model = SharedDiagonal()) :
JacobianSchurFactor<D>() {
size_t j = 0, m2 = E.rows(), m = m2 / 2;
// Calculate projector Q
Matrix Q = eye(m2) - E * P * E.transpose();
// Calculate pre-computed Jacobian matrices
// TODO: can we do better ?
typedef std::pair<Key, Matrix> KeyMatrix;
std::vector < KeyMatrix > QF;
QF.reserve(m);
// Below, we compute each 2m*D block A_j = Q_j * F_j = (2m*2) * (2*D)
BOOST_FOREACH(const typename Base::KeyMatrix2D& it, Fblocks)
QF.push_back(KeyMatrix(it.first, Q.block(0, 2 * j++, m2, 2) * it.second));
// Which is then passed to the normal JacobianFactor constructor
JacobianFactor::fillTerms(QF, Q * b, model);
}
};
}

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/*
* @file JacobianFactorQR.h
* @brief Jacobianfactor that combines and eliminates points
* @date Oct 27, 2013
* @uthor Frank Dellaert
*/
#pragma once
#include <gtsam_unstable/slam/JacobianSchurFactor.h>
namespace gtsam {
/**
* JacobianFactor for Schur complement that uses Q noise model
*/
template<size_t D>
class JacobianFactorQR: public JacobianSchurFactor<D> {
typedef JacobianSchurFactor<D> Base;
public:
/**
* Constructor
*/
JacobianFactorQR(const std::vector<typename Base::KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix3& P, const Vector& b,
const SharedDiagonal& model = SharedDiagonal()) :
JacobianSchurFactor<D>() {
// Create a number of Jacobian factors in a factor graph
GaussianFactorGraph gfg;
Symbol pointKey('p', 0);
size_t i = 0;
BOOST_FOREACH(const typename Base::KeyMatrix2D& it, Fblocks) {
gfg.add(pointKey, E.block<2, 3>(2 * i, 0), it.first, it.second,
b.segment<2>(2 * i), model);
i += 1;
}
//gfg.print("gfg");
// eliminate the point
GaussianBayesNet::shared_ptr bn;
GaussianFactorGraph::shared_ptr fg;
std::vector < Key > variables;
variables.push_back(pointKey);
boost::tie(bn, fg) = gfg.eliminatePartialSequential(variables, EliminateQR);
//fg->print("fg");
JacobianFactor::operator=(JacobianFactor(*fg));
}
};
// class
}// gtsam

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/*
* @file JacobianFactorSVD.h
* @date Oct 27, 2013
* @uthor Frank Dellaert
*/
#pragma once
#include "gtsam_unstable/slam/JacobianSchurFactor.h"
namespace gtsam {
/**
* JacobianFactor for Schur complement that uses Q noise model
*/
template<size_t D>
class JacobianFactorSVD: public JacobianSchurFactor<D> {
public:
typedef Eigen::Matrix<double, 2, D> Matrix2D;
typedef std::pair<Key, Matrix2D> KeyMatrix2D;
/// Default constructor
JacobianFactorSVD() {}
/// Constructor
JacobianFactorSVD(const std::vector<KeyMatrix2D>& Fblocks, const Matrix& Enull, const Vector& b,
const SharedDiagonal& model = SharedDiagonal()) : JacobianSchurFactor<D>() {
size_t numKeys = Enull.rows() / 2;
size_t j = 0, m2 = 2*numKeys-3;
// PLAIN NULL SPACE TRICK
// Matrix Q = Enull * Enull.transpose();
// BOOST_FOREACH(const KeyMatrix2D& it, Fblocks)
// QF.push_back(KeyMatrix(it.first, Q.block(0, 2 * j++, m2, 2) * it.second));
// JacobianFactor factor(QF, Q * b);
typedef std::pair<Key, Matrix> KeyMatrix;
std::vector<KeyMatrix> QF;
QF.reserve(numKeys);
BOOST_FOREACH(const KeyMatrix2D& it, Fblocks)
QF.push_back(KeyMatrix(it.first, (Enull.transpose()).block(0, 2 * j++, m2, 2) * it.second));
JacobianFactor::fillTerms(QF, Enull.transpose() * b, model);
}
};
}

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/*
* @file JacobianSchurFactor.h
* @brief Jacobianfactor that combines and eliminates points
* @date Oct 27, 2013
* @uthor Frank Dellaert
*/
#pragma once
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <boost/foreach.hpp>
namespace gtsam {
/**
* JacobianFactor for Schur complement that uses Q noise model
*/
template<size_t D>
class JacobianSchurFactor: public JacobianFactor {
public:
typedef Eigen::Matrix<double, 2, D> Matrix2D;
typedef std::pair<Key, Matrix2D> KeyMatrix2D;
// Use eigen magic to access raw memory
typedef Eigen::Matrix<double, D, 1> DVector;
typedef Eigen::Map<DVector> DMap;
typedef Eigen::Map<const DVector> ConstDMap;
/**
* @brief double* Matrix-vector multiply, i.e. y = A*x
* RAW memory access! Assumes keys start at 0 and go to M-1, and x is laid out that way
*/
Vector operator*(const double* x) const {
Vector Ax = zero(Ab_.rows());
if (empty()) return Ax;
// Just iterate over all A matrices and multiply in correct config part
for(size_t pos=0; pos<size(); ++pos)
Ax += Ab_(pos) * ConstDMap(x + D * keys_[pos]);
return model_ ? model_->whiten(Ax) : Ax;
}
/**
* @brief double* Transpose Matrix-vector multiply, i.e. x += A'*e
* RAW memory access! Assumes keys start at 0 and go to M-1, and y is laid out that way
*/
void transposeMultiplyAdd(double alpha, const Vector& e, double* x) const
{
Vector E = alpha * (model_ ? model_->whiten(e) : e);
// Just iterate over all A matrices and insert Ai^e into y
for(size_t pos=0; pos<size(); ++pos)
DMap(x + D * keys_[pos]) += Ab_(pos).transpose() * E;
}
/** y += alpha * A'*A*x */
void multiplyHessianAdd(double alpha, const VectorValues& x, VectorValues& y) const {
JacobianFactor::multiplyHessianAdd(alpha,x,y);
}
/**
* @brief double* Hessian-vector multiply, i.e. y += A'*(A*x)
* RAW memory access! Assumes keys start at 0 and go to M-1, and x and and y are laid out that way
*/
void multiplyHessianAdd(double alpha, const double* x, double* y) const {
// Vector Ax = (*this)*x;
// this->transposeMultiplyAdd(alpha,Ax,y);
if (empty()) return;
Vector Ax = zero(Ab_.rows());
// Just iterate over all A matrices and multiply in correct config part
for(size_t pos=0; pos<size(); ++pos)
Ax += Ab_(pos) * ConstDMap(x + D * keys_[pos]);
// Deal with noise properly, need to Double* whiten as we are dividing by variance
if (model_) { model_->whitenInPlace(Ax); model_->whitenInPlace(Ax); }
// multiply with alpha
Ax *= alpha;
// Again iterate over all A matrices and insert Ai^e into y
for(size_t pos=0; pos<size(); ++pos)
DMap(y + D * keys_[pos]) += Ab_(pos).transpose() * Ax;
}
}; // class
} // gtsam

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file RegularHessianFactor.h
* @brief HessianFactor class with constant sized blcoks
* @author Richard Roberts
* @date Dec 8, 2010
*/
#pragma once
#include <gtsam/linear/HessianFactor.h>
#include <boost/foreach.hpp>
#include <vector>
namespace gtsam {
template<size_t D>
class RegularHessianFactor: public HessianFactor {
private:
typedef Eigen::Matrix<double, D, D> MatrixDD; // camera hessian block
typedef Eigen::Matrix<double, D, 1> VectorD;
public:
/** Construct an n-way factor. Gs contains the upper-triangle blocks of the
* quadratic term (the Hessian matrix) provided in row-order, gs the pieces
* of the linear vector term, and f the constant term.
*/
RegularHessianFactor(const std::vector<Key>& js,
const std::vector<Matrix>& Gs, const std::vector<Vector>& gs, double f) :
HessianFactor(js, Gs, gs, f) {
}
/** Constructor with an arbitrary number of keys and with the augmented information matrix
* specified as a block matrix. */
template<typename KEYS>
RegularHessianFactor(const KEYS& keys,
const SymmetricBlockMatrix& augmentedInformation) :
HessianFactor(keys, augmentedInformation) {
}
/** y += alpha * A'*A*x */
void multiplyHessianAdd(double alpha, const VectorValues& x,
VectorValues& y) const {
HessianFactor::multiplyHessianAdd(alpha, x, y);
}
// Scratch space for multiplyHessianAdd
typedef Eigen::Matrix<double, D, 1> DVector;
mutable std::vector<DVector> y;
void multiplyHessianAdd(double alpha, const double* x,
double* yvalues) const {
// Create a vector of temporary y values, corresponding to rows i
y.resize(size());
BOOST_FOREACH(DVector & yi, y)
yi.setZero();
typedef Eigen::Map<DVector> DMap;
typedef Eigen::Map<const DVector> ConstDMap;
// Accessing the VectorValues one by one is expensive
// So we will loop over columns to access x only once per column
// And fill the above temporary y values, to be added into yvalues after
DVector xj(D);
for (DenseIndex j = 0; j < (DenseIndex) size(); ++j) {
Key key = keys_[j];
const double* xj = x + key * D;
DenseIndex i = 0;
for (; i < j; ++i)
y[i] += info_(i, j).knownOffDiagonal() * ConstDMap(xj);
// blocks on the diagonal are only half
y[i] += info_(j, j).selfadjointView() * ConstDMap(xj);
// for below diagonal, we take transpose block from upper triangular part
for (i = j + 1; i < (DenseIndex) size(); ++i)
y[i] += info_(i, j).knownOffDiagonal() * ConstDMap(xj);
}
// copy to yvalues
for (DenseIndex i = 0; i < (DenseIndex) size(); ++i) {
Key key = keys_[i];
DMap(yvalues + key * D) += alpha * y[i];
}
}
};
}

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SmartFactorBase.h
* @brief Base class to create smart factors on poses or cameras
* @author Luca Carlone
* @author Zsolt Kira
* @author Frank Dellaert
*/
#pragma once
#include "JacobianFactorQ.h"
#include "ImplicitSchurFactor.h"
#include "RegularHessianFactor.h"
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/geometry/PinholeCamera.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/dataset.h>
#include <boost/optional.hpp>
#include <boost/make_shared.hpp>
#include <vector>
namespace gtsam {
/// Base class with no internal point, completely functional
template<class POSE, class CALIBRATION, size_t D>
class SmartFactorBase: public NonlinearFactor {
protected:
// Keep a copy of measurement and calibration for I/O
std::vector<Point2> measured_; ///< 2D measurement for each of the m views
std::vector<SharedNoiseModel> noise_; ///< noise model used
///< (important that the order is the same as the keys that we use to create the factor)
boost::optional<POSE> body_P_sensor_; ///< The pose of the sensor in the body frame (one for all cameras)
/// Definitions for blocks of F
typedef Eigen::Matrix<double, 2, D> Matrix2D; // F
typedef Eigen::Matrix<double, D, 2> MatrixD2; // F'
typedef std::pair<Key, Matrix2D> KeyMatrix2D; // Fblocks
typedef Eigen::Matrix<double, D, D> MatrixDD; // camera hessian block
typedef Eigen::Matrix<double, 2, 3> Matrix23;
typedef Eigen::Matrix<double, D, 1> VectorD;
typedef Eigen::Matrix<double, 2, 2> Matrix2;
/// shorthand for base class type
typedef NonlinearFactor Base;
/// shorthand for this class
typedef SmartFactorBase<POSE, CALIBRATION, D> This;
public:
/// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<This> shared_ptr;
/// shorthand for a pinhole camera
typedef PinholeCamera<CALIBRATION> Camera;
typedef std::vector<Camera> Cameras;
/**
* Constructor
* @param body_P_sensor is the transform from body to sensor frame (default identity)
*/
SmartFactorBase(boost::optional<POSE> body_P_sensor = boost::none) :
body_P_sensor_(body_P_sensor) {
}
/** Virtual destructor */
virtual ~SmartFactorBase() {
}
/**
* add a new measurement and pose key
* @param measured_i is the 2m dimensional projection of a single landmark
* @param poseKey is the index corresponding to the camera observing the landmark
* @param noise_i is the measurement noise
*/
void add(const Point2& measured_i, const Key& poseKey_i,
const SharedNoiseModel& noise_i) {
this->measured_.push_back(measured_i);
this->keys_.push_back(poseKey_i);
this->noise_.push_back(noise_i);
}
/**
* variant of the previous add: adds a bunch of measurements, together with the camera keys and noises
*/
// ****************************************************************************************************
void add(std::vector<Point2>& measurements, std::vector<Key>& poseKeys,
std::vector<SharedNoiseModel>& noises) {
for (size_t i = 0; i < measurements.size(); i++) {
this->measured_.push_back(measurements.at(i));
this->keys_.push_back(poseKeys.at(i));
this->noise_.push_back(noises.at(i));
}
}
/**
* variant of the previous add: adds a bunch of measurements and uses the same noise model for all of them
*/
// ****************************************************************************************************
void add(std::vector<Point2>& measurements, std::vector<Key>& poseKeys,
const SharedNoiseModel& noise) {
for (size_t i = 0; i < measurements.size(); i++) {
this->measured_.push_back(measurements.at(i));
this->keys_.push_back(poseKeys.at(i));
this->noise_.push_back(noise);
}
}
/**
* Adds an entire SfM_track (collection of cameras observing a single point).
* The noise is assumed to be the same for all measurements
*/
// ****************************************************************************************************
void add(const SfM_Track& trackToAdd, const SharedNoiseModel& noise) {
for (size_t k = 0; k < trackToAdd.number_measurements(); k++) {
this->measured_.push_back(trackToAdd.measurements[k].second);
this->keys_.push_back(trackToAdd.measurements[k].first);
this->noise_.push_back(noise);
}
}
/** return the measurements */
const Vector& measured() const {
return measured_;
}
/** return the noise model */
const SharedNoiseModel& noise() const {
return noise_;
}
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
void print(const std::string& s = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const {
std::cout << s << "SmartFactorBase, z = \n";
for (size_t k = 0; k < measured_.size(); ++k) {
std::cout << "measurement, p = " << measured_[k] << "\t";
noise_[k]->print("noise model = ");
}
if (this->body_P_sensor_)
this->body_P_sensor_->print(" sensor pose in body frame: ");
Base::print("", keyFormatter);
}
/// equals
virtual bool equals(const NonlinearFactor& p, double tol = 1e-9) const {
const This *e = dynamic_cast<const This*>(&p);
bool areMeasurementsEqual = true;
for (size_t i = 0; i < measured_.size(); i++) {
if (this->measured_.at(i).equals(e->measured_.at(i), tol) == false)
areMeasurementsEqual = false;
break;
}
return e && Base::equals(p, tol) && areMeasurementsEqual
&& ((!body_P_sensor_ && !e->body_P_sensor_)
|| (body_P_sensor_ && e->body_P_sensor_
&& body_P_sensor_->equals(*e->body_P_sensor_)));
}
// ****************************************************************************************************
/// Calculate vector of re-projection errors, before applying noise model
Vector reprojectionError(const Cameras& cameras, const Point3& point) const {
Vector b = zero(2 * cameras.size());
size_t i = 0;
BOOST_FOREACH(const Camera& camera, cameras) {
const Point2& zi = this->measured_.at(i);
try {
Point2 e(camera.project(point) - zi);
b[2 * i] = e.x();
b[2 * i + 1] = e.y();
} catch (CheiralityException& e) {
std::cout << "Cheirality exception " << std::endl;
exit (EXIT_FAILURE);
}
i += 1;
}
return b;
}
// ****************************************************************************************************
/**
* Calculate the error of the factor.
* This is the log-likelihood, e.g. \f$ 0.5(h(x)-z)^2/\sigma^2 \f$ in case of Gaussian.
* In this class, we take the raw prediction error \f$ h(x)-z \f$, ask the noise model
* to transform it to \f$ (h(x)-z)^2/\sigma^2 \f$, and then multiply by 0.5.
* This is different from reprojectionError(cameras,point) as each point is whitened
*/
double totalReprojectionError(const Cameras& cameras,
const Point3& point) const {
double overallError = 0;
size_t i = 0;
BOOST_FOREACH(const Camera& camera, cameras) {
const Point2& zi = this->measured_.at(i);
try {
Point2 reprojectionError(camera.project(point) - zi);
overallError += 0.5
* this->noise_.at(i)->distance(reprojectionError.vector());
} catch (CheiralityException& e) {
std::cout << "Cheirality exception " << std::endl;
exit (EXIT_FAILURE);
}
i += 1;
}
return overallError;
}
// ****************************************************************************************************
/// Assumes non-degenerate !
void computeEP(Matrix& E, Matrix& PointCov, const Cameras& cameras,
const Point3& point) const {
int numKeys = this->keys_.size();
E = zeros(2 * numKeys, 3);
Vector b = zero(2 * numKeys);
Matrix Ei(2, 3);
for (size_t i = 0; i < this->measured_.size(); i++) {
try {
cameras[i].project(point, boost::none, Ei);
} catch (CheiralityException& e) {
std::cout << "Cheirality exception " << std::endl;
exit (EXIT_FAILURE);
}
this->noise_.at(i)->WhitenSystem(Ei, b);
E.block<2, 3>(2 * i, 0) = Ei;
}
// Matrix PointCov;
PointCov.noalias() = (E.transpose() * E).inverse();
}
// ****************************************************************************************************
/// Compute F, E only (called below in both vanilla and SVD versions)
/// Given a Point3, assumes dimensionality is 3
double computeJacobians(std::vector<KeyMatrix2D>& Fblocks, Matrix& E,
Vector& b, const Cameras& cameras, const Point3& point) const {
int numKeys = this->keys_.size();
E = zeros(2 * numKeys, 3);
b = zero(2 * numKeys);
double f = 0;
Matrix Fi(2, 6), Ei(2, 3), Hcali(2, D - 6), Hcam(2, D);
for (size_t i = 0; i < this->measured_.size(); i++) {
Vector bi;
try {
bi =
-(cameras[i].project(point, Fi, Ei, Hcali) - this->measured_.at(i)).vector();
} catch (CheiralityException& e) {
std::cout << "Cheirality exception " << std::endl;
exit (EXIT_FAILURE);
}
this->noise_.at(i)->WhitenSystem(Fi, Ei, Hcali, bi);
f += bi.squaredNorm();
if (D == 6) { // optimize only camera pose
Fblocks.push_back(KeyMatrix2D(this->keys_[i], Fi));
} else {
Hcam.block<2, 6>(0, 0) = Fi; // 2 x 6 block for the cameras
Hcam.block<2, D - 6>(0, 6) = Hcali; // 2 x nrCal block for the cameras
Fblocks.push_back(KeyMatrix2D(this->keys_[i], Hcam));
}
E.block<2, 3>(2 * i, 0) = Ei;
subInsert(b, bi, 2 * i);
}
return f;
}
// ****************************************************************************************************
/// Version that computes PointCov, with optional lambda parameter
double computeJacobians(std::vector<KeyMatrix2D>& Fblocks, Matrix& E,
Matrix3& PointCov, Vector& b, const Cameras& cameras, const Point3& point,
double lambda = 0.0, bool diagonalDamping = false) const {
double f = computeJacobians(Fblocks, E, b, cameras, point);
// Point covariance inv(E'*E)
Matrix3 EtE = E.transpose() * E;
Matrix3 DMatrix = eye(E.cols()); // damping matrix
if (diagonalDamping) { // diagonal of the hessian
DMatrix(0, 0) = EtE(0, 0);
DMatrix(1, 1) = EtE(1, 1);
DMatrix(2, 2) = EtE(2, 2);
}
PointCov.noalias() = (EtE + lambda * DMatrix).inverse();
return f;
}
// ****************************************************************************************************
// TODO, there should not be a Matrix version, really
double computeJacobians(Matrix& F, Matrix& E, Matrix3& PointCov, Vector& b,
const Cameras& cameras, const Point3& point,
const double lambda = 0.0) const {
int numKeys = this->keys_.size();
std::vector < KeyMatrix2D > Fblocks;
double f = computeJacobians(Fblocks, E, PointCov, b, cameras, point,
lambda);
F = zeros(2 * numKeys, D * numKeys);
for (size_t i = 0; i < this->keys_.size(); ++i) {
F.block<2, D>(2 * i, D * i) = Fblocks.at(i).second; // 2 x 6 block for the cameras
}
return f;
}
// ****************************************************************************************************
/// SVD version
double computeJacobiansSVD(std::vector<KeyMatrix2D>& Fblocks, Matrix& Enull,
Vector& b, const Cameras& cameras, const Point3& point, double lambda =
0.0, bool diagonalDamping = false) const {
Matrix E;
Matrix3 PointCov; // useless
double f = computeJacobians(Fblocks, E, PointCov, b, cameras, point, lambda,
diagonalDamping); // diagonalDamping should have no effect (only on PointCov)
// Do SVD on A
Eigen::JacobiSVD < Matrix > svd(E, Eigen::ComputeFullU);
Vector s = svd.singularValues();
// Enull = zeros(2 * numKeys, 2 * numKeys - 3);
int numKeys = this->keys_.size();
Enull = svd.matrixU().block(0, 3, 2 * numKeys, 2 * numKeys - 3); // last 2m-3 columns
return f;
}
// ****************************************************************************************************
/// Matrix version of SVD
// TODO, there should not be a Matrix version, really
double computeJacobiansSVD(Matrix& F, Matrix& Enull, Vector& b,
const Cameras& cameras, const Point3& point) const {
int numKeys = this->keys_.size();
std::vector < KeyMatrix2D > Fblocks;
double f = computeJacobiansSVD(Fblocks, Enull, b, cameras, point);
F.resize(2 * numKeys, D * numKeys);
F.setZero();
for (size_t i = 0; i < this->keys_.size(); ++i)
F.block<2, D>(2 * i, D * i) = Fblocks.at(i).second; // 2 x 6 block for the cameras
return f;
}
// ****************************************************************************************************
/// linearize returns a Hessianfactor that is an approximation of error(p)
boost::shared_ptr<RegularHessianFactor<D> > createHessianFactor(
const Cameras& cameras, const Point3& point, const double lambda = 0.0,
bool diagonalDamping = false) const {
int numKeys = this->keys_.size();
std::vector < KeyMatrix2D > Fblocks;
Matrix E;
Matrix3 PointCov;
Vector b;
double f = computeJacobians(Fblocks, E, PointCov, b, cameras, point, lambda,
diagonalDamping);
//#define HESSIAN_BLOCKS
#ifdef HESSIAN_BLOCKS
// Create structures for Hessian Factors
std::vector < Matrix > Gs(numKeys * (numKeys + 1) / 2);
std::vector < Vector > gs(numKeys);
sparseSchurComplement(Fblocks, E, PointCov, b, Gs, gs);
// schurComplement(Fblocks, E, PointCov, b, Gs, gs);
//std::vector < Matrix > Gs2(Gs.begin(), Gs.end());
//std::vector < Vector > gs2(gs.begin(), gs.end());
return boost::make_shared < RegularHessianFactor<D>
> (this->keys_, Gs, gs, f);
#else
size_t n1 = D * numKeys + 1;
std::vector<DenseIndex> dims(numKeys + 1); // this also includes the b term
std::fill(dims.begin(), dims.end() - 1, D);
dims.back() = 1;
SymmetricBlockMatrix augmentedHessian(dims, Matrix::Zero(n1, n1)); // for 10 cameras, size should be (10*D+1 x 10*D+1)
sparseSchurComplement(Fblocks, E, PointCov, b, augmentedHessian); // augmentedHessian.matrix().block<D,D> (i1,i2) = ...
augmentedHessian(numKeys,numKeys)(0,0) = f;
return boost::make_shared<RegularHessianFactor<D> >(
this->keys_, augmentedHessian);
#endif
}
// ****************************************************************************************************
boost::shared_ptr<RegularHessianFactor<D> > updateAugmentedHessian(
const Cameras& cameras, const Point3& point, const double lambda,
bool diagonalDamping, SymmetricBlockMatrix& augmentedHessian) const {
int numKeys = this->keys_.size();
std::vector < KeyMatrix2D > Fblocks;
Matrix E;
Matrix3 PointCov;
Vector b;
double f = computeJacobians(Fblocks, E, PointCov, b, cameras, point, lambda,
diagonalDamping);
std::vector<DenseIndex> dims(numKeys + 1); // this also includes the b term
std::fill(dims.begin(), dims.end() - 1, D);
dims.back() = 1;
updateSparseSchurComplement(Fblocks, E, PointCov, b, augmentedHessian); // augmentedHessian.matrix().block<D,D> (i1,i2) = ...
std::cout << "f "<< f <<std::endl;
augmentedHessian(numKeys,numKeys)(0,0) += f;
}
// ****************************************************************************************************
void schurComplement(const std::vector<KeyMatrix2D>& Fblocks, const Matrix& E,
const Matrix& PointCov, const Vector& b,
/*output ->*/std::vector<Matrix>& Gs, std::vector<Vector>& gs) const {
// Schur complement trick
// Gs = F' * F - F' * E * inv(E'*E) * E' * F
// gs = F' * (b - E * inv(E'*E) * E' * b)
// This version uses full matrices
int numKeys = this->keys_.size();
/// Compute Full F ????
Matrix F = zeros(2 * numKeys, D * numKeys);
for (size_t i = 0; i < this->keys_.size(); ++i)
F.block<2, D>(2 * i, D * i) = Fblocks.at(i).second; // 2 x 6 block for the cameras
Matrix H(D * numKeys, D * numKeys);
Vector gs_vector;
H.noalias() = F.transpose() * (F - (E * (PointCov * (E.transpose() * F))));
gs_vector.noalias() = F.transpose()
* (b - (E * (PointCov * (E.transpose() * b))));
// Populate Gs and gs
int GsCount2 = 0;
for (DenseIndex i1 = 0; i1 < numKeys; i1++) { // for each camera
DenseIndex i1D = i1 * D;
gs.at(i1) = gs_vector.segment < D > (i1D);
for (DenseIndex i2 = 0; i2 < numKeys; i2++) {
if (i2 >= i1) {
Gs.at(GsCount2) = H.block<D, D>(i1D, i2 * D);
GsCount2++;
}
}
}
}
// ****************************************************************************************************
void updateSparseSchurComplement(const std::vector<KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix& P /*Point Covariance*/, const Vector& b,
/*output ->*/SymmetricBlockMatrix& augmentedHessian) const {
// Schur complement trick
// Gs = F' * F - F' * E * P * E' * F
// gs = F' * (b - E * P * E' * b)
// a single point is observed in numKeys cameras
size_t numKeys = this->keys_.size(); // cameras observing current point
size_t aug_numKeys = augmentedHessian.rows() - 1; // all cameras in the group
// Blockwise Schur complement
for (size_t i1 = 0; i1 < numKeys; i1++) { // for each camera
const Matrix2D& Fi1 = Fblocks.at(i1).second;
const Matrix23 Ei1_P = E.block<2, 3>(2 * i1, 0) * P;
// D = (Dx2) * (2)
// (augmentedHessian.matrix()).block<D,1> (i1,numKeys+1) = Fi1.transpose() * b.segment < 2 > (2 * i1); // F' * b
size_t aug_i1 = this->keys_[i1];
std::cout << "i1 "<< i1 <<std::endl;
std::cout << "aug_i1 "<< aug_i1 <<std::endl;
std::cout << "aug_numKeys "<< aug_numKeys <<std::endl;
augmentedHessian(aug_i1,aug_numKeys) = //augmentedHessian(aug_i1,aug_numKeys) +
Fi1.transpose() * b.segment < 2 > (2 * i1) // F' * b
- Fi1.transpose() * (Ei1_P * (E.transpose() * b)); // D = (Dx2) * (2x3) * (3*2m) * (2m x 1)
// (DxD) = (Dx2) * ( (2xD) - (2x3) * (3x2) * (2xD) )
std::cout << "filled 1 " <<std::endl;
augmentedHessian(aug_i1,aug_i1) = //augmentedHessian(aug_i1,aug_i1) +
Fi1.transpose() * (Fi1 - Ei1_P * E.block<2, 3>(2 * i1, 0).transpose() * Fi1);
// upper triangular part of the hessian
for (size_t i2 = i1+1; i2 < numKeys; i2++) { // for each camera
const Matrix2D& Fi2 = Fblocks.at(i2).second;
size_t aug_i2 = this->keys_[i2];
std::cout << "i2 "<< i2 <<std::endl;
std::cout << "aug_i2 "<< aug_i2 <<std::endl;
// (DxD) = (Dx2) * ( (2x2) * (2xD) )
augmentedHessian(aug_i1, aug_i2) = //augmentedHessian(aug_i1, aug_i2)
- Fi1.transpose() * (Ei1_P * E.block<2, 3>(2 * i2, 0).transpose() * Fi2);
}
} // end of for over cameras
}
// ****************************************************************************************************
void sparseSchurComplement(const std::vector<KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix& P /*Point Covariance*/, const Vector& b,
/*output ->*/SymmetricBlockMatrix& augmentedHessian) const {
// Schur complement trick
// Gs = F' * F - F' * E * P * E' * F
// gs = F' * (b - E * P * E' * b)
// a single point is observed in numKeys cameras
size_t numKeys = this->keys_.size();
// Blockwise Schur complement
for (size_t i1 = 0; i1 < numKeys; i1++) { // for each camera
const Matrix2D& Fi1 = Fblocks.at(i1).second;
const Matrix23 Ei1_P = E.block<2, 3>(2 * i1, 0) * P;
// D = (Dx2) * (2)
// (augmentedHessian.matrix()).block<D,1> (i1,numKeys+1) = Fi1.transpose() * b.segment < 2 > (2 * i1); // F' * b
augmentedHessian(i1,numKeys) = Fi1.transpose() * b.segment < 2 > (2 * i1) // F' * b
- Fi1.transpose() * (Ei1_P * (E.transpose() * b)); // D = (Dx2) * (2x3) * (3*2m) * (2m x 1)
// (DxD) = (Dx2) * ( (2xD) - (2x3) * (3x2) * (2xD) )
augmentedHessian(i1,i1) =
Fi1.transpose() * (Fi1 - Ei1_P * E.block<2, 3>(2 * i1, 0).transpose() * Fi1);
// upper triangular part of the hessian
for (size_t i2 = i1+1; i2 < numKeys; i2++) { // for each camera
const Matrix2D& Fi2 = Fblocks.at(i2).second;
// (DxD) = (Dx2) * ( (2x2) * (2xD) )
augmentedHessian(i1,i2) =
-Fi1.transpose() * (Ei1_P * E.block<2, 3>(2 * i2, 0).transpose() * Fi2);
}
} // end of for over cameras
}
// ****************************************************************************************************
void sparseSchurComplement(const std::vector<KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix& P /*Point Covariance*/, const Vector& b,
/*output ->*/std::vector<Matrix>& Gs, std::vector<Vector>& gs) const {
// Schur complement trick
// Gs = F' * F - F' * E * P * E' * F
// gs = F' * (b - E * P * E' * b)
// a single point is observed in numKeys cameras
size_t numKeys = this->keys_.size();
int GsIndex = 0;
// Blockwise Schur complement
for (size_t i1 = 0; i1 < numKeys; i1++) { // for each camera
// GsIndex points to the upper triangular blocks
// 0 1 2 3 4
// X 5 6 7 8
// X X 9 10 11
// X X X 12 13
// X X X X 14
const Matrix2D& Fi1 = Fblocks.at(i1).second;
const Matrix23 Ei1_P = E.block<2, 3>(2 * i1, 0) * P;
{ // for i1 = i2
// D = (Dx2) * (2)
gs.at(i1) = Fi1.transpose() * b.segment < 2 > (2 * i1); // F' * b
// D = (Dx2) * (2x3) * (3*2m) * (2m x 1)
gs.at(i1) -= Fi1.transpose() * (Ei1_P * (E.transpose() * b));
// (DxD) = (Dx2) * ( (2xD) - (2x3) * (3x2) * (2xD) )
Gs.at(GsIndex) = Fi1.transpose() * (Fi1 - Ei1_P * E.block<2, 3>(2 * i1, 0).transpose() * Fi1);
GsIndex++;
}
// upper triangular part of the hessian
for (size_t i2 = i1+1; i2 < numKeys; i2++) { // for each camera
const Matrix2D& Fi2 = Fblocks.at(i2).second;
// (DxD) = (Dx2) * ( (2x2) * (2xD) )
Gs.at(GsIndex) = -Fi1.transpose() * (Ei1_P * E.block<2, 3>(2 * i2, 0).transpose() * Fi2);
GsIndex++;
}
} // end of for over cameras
}
// ****************************************************************************************************
boost::shared_ptr<ImplicitSchurFactor<D> > createImplicitSchurFactor(
const Cameras& cameras, const Point3& point, double lambda = 0.0,
bool diagonalDamping = false) const {
typename boost::shared_ptr<ImplicitSchurFactor<D> > f(
new ImplicitSchurFactor<D>());
computeJacobians(f->Fblocks(), f->E(), f->PointCovariance(), f->b(),
cameras, point, lambda, diagonalDamping);
f->initKeys();
return f;
}
// ****************************************************************************************************
boost::shared_ptr<JacobianFactorQ<D> > createJacobianQFactor(
const Cameras& cameras, const Point3& point, double lambda = 0.0,
bool diagonalDamping = false) const {
std::vector < KeyMatrix2D > Fblocks;
Matrix E;
Matrix3 PointCov;
Vector b;
computeJacobians(Fblocks, E, PointCov, b, cameras, point, lambda,
diagonalDamping);
return boost::make_shared < JacobianFactorQ<D> > (Fblocks, E, PointCov, b);
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(measured_);
ar & BOOST_SERIALIZATION_NVP(body_P_sensor_);
}
};
} // \ namespace gtsam

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SmartProjectionFactor.h
* @brief Base class to create smart factors on poses or cameras
* @author Luca Carlone
* @author Zsolt Kira
* @author Frank Dellaert
*/
#pragma once
#include "SmartFactorBase.h"
#include <gtsam_unstable/geometry/triangulation.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/slam/dataset.h>
#include <gtsam_unstable/geometry/triangulation.h>
#include <boost/optional.hpp>
#include <boost/make_shared.hpp>
#include <vector>
namespace gtsam {
/**
* Structure for storing some state memory, used to speed up optimization
* @addtogroup SLAM
*/
class SmartProjectionFactorState {
protected:
public:
SmartProjectionFactorState() {
}
// Hessian representation (after Schur complement)
bool calculatedHessian;
Matrix H;
Vector gs_vector;
std::vector<Matrix> Gs;
std::vector<Vector> gs;
double f;
};
/**
* SmartProjectionFactor: triangulates point
* TODO: why LANDMARK parameter?
*/
template<class POSE, class LANDMARK, class CALIBRATION, size_t D>
class SmartProjectionFactor: public SmartFactorBase<POSE, CALIBRATION, D> {
protected:
// Some triangulation parameters
const double rankTolerance_; ///< threshold to decide whether triangulation is degenerate_
const double retriangulationThreshold_; ///< threshold to decide whether to re-triangulate
mutable std::vector<Pose3> cameraPosesTriangulation_; ///< current triangulation poses
const bool manageDegeneracy_; ///< if set to true will use the rotation-only version for degenerate cases
const bool enableEPI_; ///< if set to true, will refine triangulation using LM
const double linearizationThreshold_; ///< threshold to decide whether to re-linearize
mutable std::vector<Pose3> cameraPosesLinearization_; ///< current linearization poses
mutable Point3 point_; ///< Current estimate of the 3D point
mutable bool degenerate_;
mutable bool cheiralityException_;
// verbosity handling for Cheirality Exceptions
const bool throwCheirality_; ///< If true, rethrows Cheirality exceptions (default: false)
const bool verboseCheirality_; ///< If true, prints text for Cheirality exceptions (default: false)
boost::shared_ptr<SmartProjectionFactorState> state_;
/// shorthand for smart projection factor state variable
typedef boost::shared_ptr<SmartProjectionFactorState> SmartFactorStatePtr;
/// shorthand for base class type
typedef SmartFactorBase<POSE, CALIBRATION, D> Base;
/// shorthand for this class
typedef SmartProjectionFactor<POSE, LANDMARK, CALIBRATION, D> This;
public:
/// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<This> shared_ptr;
/// shorthand for a pinhole camera
typedef PinholeCamera<CALIBRATION> Camera;
typedef std::vector<Camera> Cameras;
/**
* Constructor
* @param rankTol tolerance used to check if point triangulation is degenerate
* @param linThreshold threshold on relative pose changes used to decide whether to relinearize (selective relinearization)
* @param manageDegeneracy is true, in presence of degenerate triangulation, the factor is converted to a rotation-only constraint,
* otherwise the factor is simply neglected
* @param enableEPI if set to true linear triangulation is refined with embedded LM iterations
* @param body_P_sensor is the transform from body to sensor frame (default identity)
*/
SmartProjectionFactor(const double rankTol, const double linThreshold,
const bool manageDegeneracy, const bool enableEPI,
boost::optional<POSE> body_P_sensor = boost::none,
SmartFactorStatePtr state = SmartFactorStatePtr(
new SmartProjectionFactorState())) :
Base(body_P_sensor), rankTolerance_(rankTol), retriangulationThreshold_(
1e-5), manageDegeneracy_(manageDegeneracy), enableEPI_(enableEPI), linearizationThreshold_(
linThreshold), degenerate_(false), cheiralityException_(false), throwCheirality_(
false), verboseCheirality_(false), state_(state) {
}
/** Virtual destructor */
virtual ~SmartProjectionFactor() {
}
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
void print(const std::string& s = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const {
std::cout << s << "SmartProjectionFactor, z = \n";
std::cout << "rankTolerance_ = " << rankTolerance_ << std::endl;
std::cout << "degenerate_ = " << degenerate_ << std::endl;
std::cout << "cheiralityException_ = " << cheiralityException_ << std::endl;
Base::print("", keyFormatter);
}
/// Check if the new linearization point_ is the same as the one used for previous triangulation
bool decideIfTriangulate(const Cameras& cameras) const {
// several calls to linearize will be done from the same linearization point_, hence it is not needed to re-triangulate
// Note that this is not yet "selecting linearization", that will come later, and we only check if the
// current linearization is the "same" (up to tolerance) w.r.t. the last time we triangulated the point_
size_t m = cameras.size();
bool retriangulate = false;
// if we do not have a previous linearization point_ or the new linearization point_ includes more poses
if (cameraPosesTriangulation_.empty()
|| cameras.size() != cameraPosesTriangulation_.size())
retriangulate = true;
if (!retriangulate) {
for (size_t i = 0; i < cameras.size(); i++) {
if (!cameras[i].pose().equals(cameraPosesTriangulation_[i],
retriangulationThreshold_)) {
retriangulate = true; // at least two poses are different, hence we retriangulate
break;
}
}
}
if (retriangulate) { // we store the current poses used for triangulation
cameraPosesTriangulation_.clear();
cameraPosesTriangulation_.reserve(m);
for (size_t i = 0; i < m; i++)
// cameraPosesTriangulation_[i] = cameras[i].pose();
cameraPosesTriangulation_.push_back(cameras[i].pose());
}
return retriangulate; // if we arrive to this point_ all poses are the same and we don't need re-triangulation
}
/// This function checks if the new linearization point_ is 'close' to the previous one used for linearization
bool decideIfLinearize(const Cameras& cameras) const {
// "selective linearization"
// The function evaluates how close are the old and the new poses, transformed in the ref frame of the first pose
// (we only care about the "rigidity" of the poses, not about their absolute pose)
if (this->linearizationThreshold_ < 0) //by convention if linearizationThreshold is negative we always relinearize
return true;
// if we do not have a previous linearization point_ or the new linearization point_ includes more poses
if (cameraPosesLinearization_.empty()
|| (cameras.size() != cameraPosesLinearization_.size()))
return true;
Pose3 firstCameraPose, firstCameraPoseOld;
for (size_t i = 0; i < cameras.size(); i++) {
if (i == 0) { // we store the initial pose, this is useful for selective re-linearization
firstCameraPose = cameras[i].pose();
firstCameraPoseOld = cameraPosesLinearization_[i];
continue;
}
// we compare the poses in the frame of the first pose
Pose3 localCameraPose = firstCameraPose.between(cameras[i].pose());
Pose3 localCameraPoseOld = firstCameraPoseOld.between(
cameraPosesLinearization_[i]);
if (!localCameraPose.equals(localCameraPoseOld,
this->linearizationThreshold_))
return true; // at least two "relative" poses are different, hence we re-linearize
}
return false; // if we arrive to this point_ all poses are the same and we don't need re-linearize
}
/// triangulateSafe
size_t triangulateSafe(const Values& values) const {
return triangulateSafe(this->cameras(values));
}
/// triangulateSafe
size_t triangulateSafe(const Cameras& cameras) const {
size_t m = cameras.size();
if (m < 2) { // if we have a single pose the corresponding factor is uninformative
degenerate_ = true;
return m;
}
bool retriangulate = decideIfTriangulate(cameras);
if (retriangulate) {
// We triangulate the 3D position of the landmark
try {
// std::cout << "triangulatePoint3 i \n" << rankTolerance << std::endl;
point_ = triangulatePoint3<CALIBRATION>(cameras, this->measured_,
rankTolerance_, enableEPI_);
degenerate_ = false;
cheiralityException_ = false;
} catch (TriangulationUnderconstrainedException& e) {
// if TriangulationUnderconstrainedException can be
// 1) There is a single pose for triangulation - this should not happen because we checked the number of poses before
// 2) The rank of the matrix used for triangulation is < 3: rotation-only, parallel cameras (or motion towards the landmark)
// in the second case we want to use a rotation-only smart factor
degenerate_ = true;
cheiralityException_ = false;
} catch (TriangulationCheiralityException& e) {
// point is behind one of the cameras: can be the case of close-to-parallel cameras or may depend on outliers
// we manage this case by either discarding the smart factor, or imposing a rotation-only constraint
cheiralityException_ = true;
}
}
return m;
}
/// triangulate
bool triangulateForLinearize(const Cameras& cameras) const {
bool isDebug = false;
size_t nrCameras = this->triangulateSafe(cameras);
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
if (isDebug) {
std::cout << "createImplicitSchurFactor: degenerate configuration"
<< std::endl;
}
return false;
} else {
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) { // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
}
return true;
}
}
/// linearize returns a Hessianfactor that is an approximation of error(p)
boost::shared_ptr<RegularHessianFactor<D> > createHessianFactor(
const Cameras& cameras, const double lambda = 0.0) const {
bool isDebug = false;
int numKeys = this->keys_.size();
// Create structures for Hessian Factors
std::vector < Key > js;
std::vector < Matrix > Gs(numKeys * (numKeys + 1) / 2);
std::vector < Vector > gs(numKeys);
if (this->measured_.size() != cameras.size()) {
std::cout
<< "SmartProjectionHessianFactor: this->measured_.size() inconsistent with input"
<< std::endl;
exit(1);
}
this->triangulateSafe(cameras);
if (numKeys < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
// std::cout << "In linearize: exception" << std::endl;
BOOST_FOREACH(gtsam::Matrix& m, Gs)
m = zeros(D, D);
BOOST_FOREACH(Vector& v, gs)
v = zero(D);
return boost::make_shared<RegularHessianFactor<D> >(this->keys_, Gs, gs,
0.0);
}
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) { // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
}
bool doLinearize = this->decideIfLinearize(cameras);
if (this->linearizationThreshold_ >= 0 && doLinearize) // if we apply selective relinearization and we need to relinearize
for (size_t i = 0; i < cameras.size(); i++)
this->cameraPosesLinearization_[i] = cameras[i].pose();
if (!doLinearize) { // return the previous Hessian factor
std::cout << "=============================" << std::endl;
std::cout << "doLinearize " << doLinearize << std::endl;
std::cout << "this->linearizationThreshold_ "
<< this->linearizationThreshold_ << std::endl;
std::cout << "this->degenerate_ " << this->degenerate_ << std::endl;
std::cout
<< "something wrong in SmartProjectionHessianFactor: selective relinearization should be disabled"
<< std::endl;
exit(1);
return boost::make_shared<RegularHessianFactor<D> >(this->keys_,
this->state_->Gs, this->state_->gs, this->state_->f);
}
// ==================================================================
Matrix F, E;
Matrix3 PointCov;
Vector b;
double f = computeJacobians(F, E, PointCov, b, cameras, lambda);
// Schur complement trick
// Frank says: should be possible to do this more efficiently?
// And we care, as in grouped factors this is called repeatedly
Matrix H(D * numKeys, D * numKeys);
Vector gs_vector;
H.noalias() = F.transpose() * (F - (E * (PointCov * (E.transpose() * F))));
gs_vector.noalias() = F.transpose()
* (b - (E * (PointCov * (E.transpose() * b))));
if (isDebug)
std::cout << "gs_vector size " << gs_vector.size() << std::endl;
// Populate Gs and gs
int GsCount2 = 0;
for (DenseIndex i1 = 0; i1 < numKeys; i1++) { // for each camera
DenseIndex i1D = i1 * D;
gs.at(i1) = gs_vector.segment < D > (i1D);
for (DenseIndex i2 = 0; i2 < numKeys; i2++) {
if (i2 >= i1) {
Gs.at(GsCount2) = H.block < D, D > (i1D, i2 * D);
GsCount2++;
}
}
}
// ==================================================================
if (this->linearizationThreshold_ >= 0) { // if we do not use selective relinearization we don't need to store these variables
this->state_->Gs = Gs;
this->state_->gs = gs;
this->state_->f = f;
}
return boost::make_shared<RegularHessianFactor<D> >(this->keys_, Gs, gs, f);
}
// create factor
boost::shared_ptr<ImplicitSchurFactor<D> > createImplicitSchurFactor(
const Cameras& cameras, double lambda) const {
if (triangulateForLinearize(cameras))
return Base::createImplicitSchurFactor(cameras, point_, lambda);
else
return boost::shared_ptr<ImplicitSchurFactor<D> >();
}
/// create factor
boost::shared_ptr<JacobianFactorQ<D> > createJacobianQFactor(
const Cameras& cameras, double lambda) const {
if (triangulateForLinearize(cameras))
return Base::createJacobianQFactor(cameras, point_, lambda);
else
return boost::shared_ptr<JacobianFactorQ<D> >();
}
/// Create a factor, takes values
boost::shared_ptr<JacobianFactorQ<D> > createJacobianQFactor(
const Values& values, double lambda) const {
Cameras myCameras;
// TODO triangulate twice ??
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
return createJacobianQFactor(myCameras, lambda);
else
return boost::shared_ptr<JacobianFactorQ<D> >();
}
/// Returns true if nonDegenerate
bool computeCamerasAndTriangulate(const Values& values,
Cameras& myCameras) const {
Values valuesFactor;
// Select only the cameras
BOOST_FOREACH(const Key key, this->keys_)
valuesFactor.insert(key, values.at(key));
myCameras = this->cameras(valuesFactor);
size_t nrCameras = this->triangulateSafe(myCameras);
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_)))
return false;
// instead, if we want to manage the exception..
if (this->cheiralityException_ || this->degenerate_) // if we want to manage the exceptions with rotation-only factors
this->degenerate_ = true;
if (this->degenerate_) {
std::cout << "SmartProjectionFactor: this is not ready" << std::endl;
std::cout << "this->cheiralityException_ " << this->cheiralityException_
<< std::endl;
std::cout << "this->degenerate_ " << this->degenerate_ << std::endl;
}
return true;
}
/// Takes values
bool computeEP(Matrix& E, Matrix& PointCov, const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
computeEP(E, PointCov, myCameras);
return nonDegenerate;
}
/// Assumes non-degenerate !
void computeEP(Matrix& E, Matrix& PointCov, const Cameras& cameras) const {
return Base::computeEP(E, PointCov, cameras, point_);
}
/// Version that takes values, and creates the point
bool computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& E, Matrix& PointCov, Vector& b, const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
computeJacobians(Fblocks, E, PointCov, b, myCameras, 0.0);
return nonDegenerate;
}
/// Compute F, E only (called below in both vanilla and SVD versions)
/// Assumes the point has been computed
/// Note E can be 2m*3 or 2m*2, in case point is degenerate
double computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& E, Vector& b, const Cameras& cameras) const {
if (this->degenerate_) {
std::cout << "manage degeneracy " << manageDegeneracy_ << std::endl;
std::cout << "point " << point_ << std::endl;
std::cout
<< "SmartProjectionFactor: Management of degeneracy is disabled - not ready to be used"
<< std::endl;
if (D > 6) {
std::cout
<< "Management of degeneracy is not yet ready when one also optimizes for the calibration "
<< std::endl;
}
int numKeys = this->keys_.size();
E = zeros(2 * numKeys, 2);
b = zero(2 * numKeys);
double f = 0;
for (size_t i = 0; i < this->measured_.size(); i++) {
if (i == 0) { // first pose
this->point_ = cameras[i].backprojectPointAtInfinity(
this->measured_.at(i));
// 3D parametrization of point at infinity: [px py 1]
}
Matrix Fi, Ei;
Vector bi = -(cameras[i].projectPointAtInfinity(this->point_, Fi, Ei)
- this->measured_.at(i)).vector();
this->noise_.at(i)->WhitenSystem(Fi, Ei, bi);
f += bi.squaredNorm();
Fblocks.push_back(typename Base::KeyMatrix2D(this->keys_[i], Fi));
E.block < 2, 2 > (2 * i, 0) = Ei;
subInsert(b, bi, 2 * i);
}
return f;
} else {
// nondegenerate: just return Base version
return Base::computeJacobians(Fblocks, E, b, cameras, point_);
} // end else
}
/// Version that computes PointCov, with optional lambda parameter
double computeJacobians(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& E, Matrix& PointCov, Vector& b, const Cameras& cameras,
const double lambda = 0.0) const {
double f = computeJacobians(Fblocks, E, b, cameras);
// Point covariance inv(E'*E)
PointCov.noalias() = (E.transpose() * E + lambda * eye(E.cols())).inverse();
return f;
}
/// takes values
bool computeJacobiansSVD(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& Enull, Vector& b, const Values& values) const {
typename Base::Cameras myCameras;
double good = computeCamerasAndTriangulate(values, myCameras);
if (good)
computeJacobiansSVD(Fblocks, Enull, b, myCameras);
return true;
}
/// SVD version
double computeJacobiansSVD(std::vector<typename Base::KeyMatrix2D>& Fblocks,
Matrix& Enull, Vector& b, const Cameras& cameras) const {
return Base::computeJacobiansSVD(Fblocks, Enull, b, cameras, point_);
}
/// Returns Matrix, TODO: maybe should not exist -> not sparse !
// TODO should there be a lambda?
double computeJacobiansSVD(Matrix& F, Matrix& Enull, Vector& b,
const Cameras& cameras) const {
return Base::computeJacobiansSVD(F, Enull, b, cameras, point_);
}
/// Returns Matrix, TODO: maybe should not exist -> not sparse !
double computeJacobians(Matrix& F, Matrix& E, Matrix3& PointCov, Vector& b,
const Cameras& cameras, const double lambda) const {
return Base::computeJacobians(F, E, PointCov, b, cameras, point_, lambda);
}
/// Calculate vector of re-projection errors, before applying noise model
/// Assumes triangulation was done and degeneracy handled
Vector reprojectionError(const Cameras& cameras) const {
return Base::reprojectionError(cameras, point_);
}
/// Calculate vector of re-projection errors, before applying noise model
Vector reprojectionError(const Values& values) const {
Cameras myCameras;
bool nonDegenerate = computeCamerasAndTriangulate(values, myCameras);
if (nonDegenerate)
return reprojectionError(myCameras);
else
return zero(myCameras.size() * 2);
}
/**
* Calculate the error of the factor.
* This is the log-likelihood, e.g. \f$ 0.5(h(x)-z)^2/\sigma^2 \f$ in case of Gaussian.
* In this class, we take the raw prediction error \f$ h(x)-z \f$, ask the noise model
* to transform it to \f$ (h(x)-z)^2/\sigma^2 \f$, and then multiply by 0.5.
*/
double totalReprojectionError(const Cameras& cameras,
boost::optional<Point3> externalPoint = boost::none) const {
size_t nrCameras;
if (externalPoint) {
nrCameras = this->keys_.size();
point_ = *externalPoint;
degenerate_ = false;
cheiralityException_ = false;
} else {
nrCameras = this->triangulateSafe(cameras);
}
if (nrCameras < 2
|| (!this->manageDegeneracy_
&& (this->cheiralityException_ || this->degenerate_))) {
// if we don't want to manage the exceptions we discard the factor
// std::cout << "In error evaluation: exception" << std::endl;
return 0.0;
}
if (this->cheiralityException_) { // if we want to manage the exceptions with rotation-only factors
std::cout
<< "SmartProjectionHessianFactor: cheirality exception (this should not happen if CheiralityException is disabled)!"
<< std::endl;
this->degenerate_ = true;
}
if (this->degenerate_) {
// return 0.0; // TODO: this maybe should be zero?
std::cout
<< "SmartProjectionHessianFactor: trying to manage degeneracy (this should not happen is manageDegeneracy is disabled)!"
<< std::endl;
size_t i = 0;
double overallError = 0;
BOOST_FOREACH(const Camera& camera, cameras) {
const Point2& zi = this->measured_.at(i);
if (i == 0) // first pose
this->point_ = camera.backprojectPointAtInfinity(zi); // 3D parametrization of point at infinity
Point2 reprojectionError(
camera.projectPointAtInfinity(this->point_) - zi);
overallError += 0.5
* this->noise_.at(i)->distance(reprojectionError.vector());
i += 1;
}
return overallError;
} else {
// Just use version in base class
return Base::totalReprojectionError(cameras, point_);
}
}
/// Cameras are computed in derived class
virtual Cameras cameras(const Values& values) const = 0;
/** return the landmark */
boost::optional<Point3> point() const {
return point_;
}
/** COMPUTE the landmark */
boost::optional<Point3> point(const Values& values) const {
triangulateSafe(values);
return point_;
}
/** return the degenerate state */
inline bool isDegenerate() const {
return (cheiralityException_ || degenerate_);
}
/** return the cheirality status flag */
inline bool isPointBehindCamera() const {
return cheiralityException_;
}
/** return chirality verbosity */
inline bool verboseCheirality() const {
return verboseCheirality_;
}
/** return flag for throwing cheirality exceptions */
inline bool throwCheirality() const {
return throwCheirality_;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(throwCheirality_);
ar & BOOST_SERIALIZATION_NVP(verboseCheirality_);
}
};
} // \ namespace gtsam

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SmartProjectionPoseFactor.h
* @brief Produces an Hessian factors on POSES from monocular measurements of a single landmark
* @author Luca Carlone
* @author Chris Beall
* @author Zsolt Kira
*/
#pragma once
#include "SmartProjectionFactor.h"
namespace gtsam {
/**
* The calibration is known here. The factor only constraints poses (variable dimension is 6)
* @addtogroup SLAM
*/
template<class POSE, class LANDMARK, class CALIBRATION>
class SmartProjectionPoseFactor: public SmartProjectionFactor<POSE, LANDMARK, CALIBRATION, 6> {
protected:
// Known calibration
std::vector<boost::shared_ptr<CALIBRATION> > K_all_; ///< shared pointer to calibration object (one for each camera)
public:
/// shorthand for base class type
typedef SmartProjectionFactor<POSE, LANDMARK, CALIBRATION, 6> Base;
/// shorthand for this class
typedef SmartProjectionPoseFactor<POSE, LANDMARK, CALIBRATION> This;
/// shorthand for a smart pointer to a factor
typedef boost::shared_ptr<This> shared_ptr;
/**
* Constructor
* @param rankTol tolerance used to check if point triangulation is degenerate
* @param linThreshold threshold on relative pose changes used to decide whether to relinearize (selective relinearization)
* @param manageDegeneracy is true, in presence of degenerate triangulation, the factor is converted to a rotation-only constraint,
* otherwise the factor is simply neglected
* @param enableEPI if set to true linear triangulation is refined with embedded LM iterations
* @param body_P_sensor is the transform from body to sensor frame (default identity)
*/
SmartProjectionPoseFactor(const double rankTol = 1,
const double linThreshold = -1, const bool manageDegeneracy = false,
const bool enableEPI = false, boost::optional<POSE> body_P_sensor = boost::none) :
Base(rankTol, linThreshold, manageDegeneracy, enableEPI, body_P_sensor) {}
/** Virtual destructor */
virtual ~SmartProjectionPoseFactor() {}
/**
* add a new measurement and pose key
* @param measured is the 2m dimensional location of the projection of a single landmark in the m view (the measurement)
* @param poseKey is the index corresponding to the camera observing the same landmark
* @param noise_i is the measurement noise
* @param K_i is the (known) camera calibration
*/
void add(const Point2 measured_i, const Key poseKey_i,
const SharedNoiseModel noise_i,
const boost::shared_ptr<CALIBRATION> K_i) {
Base::add(measured_i, poseKey_i, noise_i);
K_all_.push_back(K_i);
}
/**
* add a new measurements and pose keys
* Variant of the previous one in which we include a set of measurements
*/
void add(std::vector<Point2> measurements, std::vector<Key> poseKeys,
std::vector<SharedNoiseModel> noises,
std::vector<boost::shared_ptr<CALIBRATION> > Ks) {
Base::add(measurements, poseKeys, noises);
for (size_t i = 0; i < measurements.size(); i++) {
K_all_.push_back(Ks.at(i));
}
}
/**
* add a new measurements and pose keys
* Variant of the previous one in which we include a set of measurements with the same noise and calibration
*/
void add(std::vector<Point2> measurements, std::vector<Key> poseKeys,
const SharedNoiseModel noise, const boost::shared_ptr<CALIBRATION> K) {
for (size_t i = 0; i < measurements.size(); i++) {
Base::add(measurements.at(i), poseKeys.at(i), noise);
K_all_.push_back(K);
}
}
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
void print(const std::string& s = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const {
std::cout << s << "SmartProjectionPoseFactor, z = \n ";
BOOST_FOREACH(const boost::shared_ptr<CALIBRATION>& K, K_all_)
K->print("calibration = ");
Base::print("", keyFormatter);
}
/// equals
virtual bool equals(const NonlinearFactor& p, double tol = 1e-9) const {
const This *e = dynamic_cast<const This*>(&p);
return e && Base::equals(p, tol);
}
/// get the dimension of the factor
virtual size_t dim() const {
return 6 * this->keys_.size();
}
// Collect all cameras
typename Base::Cameras cameras(const Values& values) const {
typename Base::Cameras cameras;
size_t i=0;
BOOST_FOREACH(const Key& k, this->keys_) {
Pose3 pose = values.at<Pose3>(k);
typename Base::Camera camera(pose, *K_all_[i++]);
cameras.push_back(camera);
}
return cameras;
}
/**
* linearize returns a Hessian factor contraining the poses
*/
virtual boost::shared_ptr<GaussianFactor> linearize(
const Values& values) const {
return this->createHessianFactor(cameras(values));
}
/**
* error calculates the error of the factor.
*/
virtual double error(const Values& values) const {
if (this->active(values)) {
return this->totalReprojectionError(cameras(values));
} else { // else of active flag
return 0.0;
}
}
/** return the calibration object */
inline const boost::shared_ptr<CALIBRATION> calibration() const {
return K_all_;
}
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
ar & BOOST_SERIALIZATION_NVP(K_all_);
}
}; // end of class declaration
} // \ namespace gtsam

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