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release/4.3a0
Frank Dellaert 2023-01-08 21:09:31 -08:00
parent 73754f271a
commit 15802e58f9
5 changed files with 154 additions and 139 deletions
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3
gtsam/base/tests/testVerticalBlockMatrix.cpp
View File

@ -24,8 +24,7 @@
using namespace gtsam;
const std::list<size_t> L{3, 2, 1};
const std::vector<size_t> dimensions(L.begin(), L.end());
const std::vector<size_t> dimensions{3, 2, 1};
//*****************************************************************************
TEST(VerticalBlockMatrix, Constructor1) {

281
gtsam/geometry/CameraSet.h
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@ -18,12 +18,13 @@
#pragma once
#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/CalibratedCamera.h> // for Cheirality exception
#include <gtsam/base/Testable.h>
#include <gtsam/base/SymmetricBlockMatrix.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/SymmetricBlockMatrix.h>
#include <gtsam/base/Testable.h>
#include <gtsam/geometry/CalibratedCamera.h> // for Cheirality exception
#include <gtsam/geometry/Point3.h>
#include <gtsam/inference/Key.h>
#include <vector>
namespace gtsam {
@ -31,70 +32,67 @@ namespace gtsam {
/**
* @brief A set of cameras, all with their own calibration
*/
template<class CAMERA>
class CameraSet : public std::vector<CAMERA, Eigen::aligned_allocator<CAMERA> > {
template <class CAMERA>
class CameraSet : public std::vector<CAMERA, Eigen::aligned_allocator<CAMERA>> {
protected:
using Base = std::vector<CAMERA, typename Eigen::aligned_allocator<CAMERA>>;
protected:
using Base = std::vector<CAMERA, typename Eigen::aligned_allocator<CAMERA>>;
/**
* 2D measurement and noise model for each of the m views
* The order is kept the same as the keys that we use to create the factor.
*/
typedef typename CAMERA::Measurement Z;
typedef typename CAMERA::MeasurementVector ZVector;
/**
* 2D measurement and noise model for each of the m views
* The order is kept the same as the keys that we use to create the factor.
*/
typedef typename CAMERA::Measurement Z;
typedef typename CAMERA::MeasurementVector ZVector;
static const int D = traits<CAMERA>::dimension; ///< Camera dimension
static const int ZDim = traits<Z>::dimension; ///< Measurement dimension
static const int D = traits<CAMERA>::dimension; ///< Camera dimension
static const int ZDim = traits<Z>::dimension; ///< Measurement dimension
/// Make a vector of re-projection errors
static Vector ErrorVector(const ZVector& predicted, const ZVector& measured) {
// Check size
size_t m = predicted.size();
if (measured.size() != m)
throw std::runtime_error("CameraSet::errors: size mismatch");
/// Make a vector of re-projection errors
static Vector ErrorVector(const ZVector& predicted, const ZVector& measured) {
// Check size
size_t m = predicted.size();
if (measured.size() != m)
throw std::runtime_error("CameraSet::errors: size mismatch");
// Project and fill error vector
Vector b(ZDim * m);
for (size_t i = 0, row = 0; i < m; i++, row += ZDim) {
Vector bi = traits<Z>::Local(measured[i], predicted[i]);
if (ZDim == 3 && std::isnan(bi(1))) { // if it is a stereo point and the
// right pixel is missing (nan)
bi(1) = 0;
}
b.segment<ZDim>(row) = bi;
}
return b;
// Project and fill error vector
Vector b(ZDim * m);
for (size_t i = 0, row = 0; i < m; i++, row += ZDim) {
Vector bi = traits<Z>::Local(measured[i], predicted[i]);
if (ZDim == 3 && std::isnan(bi(1))) { // if it is a stereo point and the
// right pixel is missing (nan)
bi(1) = 0;
}
b.segment<ZDim>(row) = bi;
}
return b;
}
public:
using Base::Base; // Inherit the vector constructors
public:
using Base::Base; // Inherit the vector constructors
/// Destructor
virtual ~CameraSet() = default;
/// Destructor
virtual ~CameraSet() = default;
/// Definitions for blocks of F
using MatrixZD = Eigen::Matrix<double, ZDim, D>;
using FBlocks = std::vector<MatrixZD, Eigen::aligned_allocator<MatrixZD>>;
/// Definitions for blocks of F
using MatrixZD = Eigen::Matrix<double, ZDim, D>;
using FBlocks = std::vector<MatrixZD, Eigen::aligned_allocator<MatrixZD>>;
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
virtual void print(const std::string& s = "") const {
std::cout << s << "CameraSet, cameras = \n";
for (size_t k = 0; k < this->size(); ++k) this->at(k).print(s);
/**
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/
virtual void print(const std::string& s = "") const {
std::cout << s << "CameraSet, cameras = \n";
for (size_t k = 0; k < this->size(); ++k) this->at(k).print(s);
}
/// equals
bool equals(const CameraSet& p, double tol = 1e-9) const {
if (this->size() != p.size())
return false;
if (this->size() != p.size()) return false;
bool camerasAreEqual = true;
for (size_t i = 0; i < this->size(); i++) {
if (this->at(i).equals(p.at(i), tol) == false)
camerasAreEqual = false;
if (this->at(i).equals(p.at(i), tol) == false) camerasAreEqual = false;
break;
}
return camerasAreEqual;
@ -106,11 +104,10 @@ public:
* matrix this function returns the diagonal blocks.
* throws CheiralityException
*/
template<class POINT>
ZVector project2(const POINT& point, //
boost::optional<FBlocks&> Fs = boost::none, //
boost::optional<Matrix&> E = boost::none) const {
template <class POINT>
ZVector project2(const POINT& point, //
boost::optional<FBlocks&> Fs = boost::none, //
boost::optional<Matrix&> E = boost::none) const {
static const int N = FixedDimension<POINT>::value;
// Allocate result
@ -135,19 +132,19 @@ public:
}
/// Calculate vector [project2(point)-z] of re-projection errors
template<class POINT>
template <class POINT>
Vector reprojectionError(const POINT& point, const ZVector& measured,
boost::optional<FBlocks&> Fs = boost::none, //
boost::optional<Matrix&> E = boost::none) const {
boost::optional<FBlocks&> Fs = boost::none, //
boost::optional<Matrix&> E = boost::none) const {
return ErrorVector(project2(point, Fs, E), measured);
}
/**
* Do Schur complement, given Jacobian as Fs,E,P, return SymmetricBlockMatrix
* G = F' * F - F' * E * P * E' * F
* g = F' * (b - E * P * E' * b)
* Fixed size version
*/
* Do Schur complement, given Jacobian as Fs,E,P, return SymmetricBlockMatrix
* G = F' * F - F' * E * P * E' * F
* g = F' * (b - E * P * E' * b)
* Fixed size version
*/
template <int N,
int ND> // N = 2 or 3 (point dimension), ND is the camera dimension
static SymmetricBlockMatrix SchurComplement(
@ -158,38 +155,47 @@ public:
// a single point is observed in m cameras
size_t m = Fs.size();
// Create a SymmetricBlockMatrix (augmented hessian, with extra row/column with info vector)
// Create a SymmetricBlockMatrix (augmented hessian, with extra row/column
// with info vector)
size_t M1 = ND * m + 1;
std::vector<DenseIndex> dims(m + 1); // this also includes the b term
std::vector<DenseIndex> dims(m + 1); // this also includes the b term
std::fill(dims.begin(), dims.end() - 1, ND);
dims.back() = 1;
SymmetricBlockMatrix augmentedHessian(dims, Matrix::Zero(M1, M1));
// Blockwise Schur complement
for (size_t i = 0; i < m; i++) { // for each camera
for (size_t i = 0; i < m; i++) { // for each camera
const Eigen::Matrix<double, ZDim, ND>& Fi = Fs[i];
const auto FiT = Fi.transpose();
const Eigen::Matrix<double, ZDim, N> Ei_P = //
const Eigen::Matrix<double, ZDim, N> Ei_P = //
E.block(ZDim * i, 0, ZDim, N) * P;
// D = (Dx2) * ZDim
augmentedHessian.setOffDiagonalBlock(i, m, FiT * b.segment<ZDim>(ZDim * i) // F' * b
- FiT * (Ei_P * (E.transpose() * b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
augmentedHessian.setOffDiagonalBlock(
i, m,
FiT * b.segment<ZDim>(ZDim * i) // F' * b
-
FiT *
(Ei_P *
(E.transpose() *
b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
// (DxD) = (DxZDim) * ( (ZDimxD) - (ZDimx3) * (3xZDim) * (ZDimxD) )
augmentedHessian.setDiagonalBlock(i, FiT
* (Fi - Ei_P * E.block(ZDim * i, 0, ZDim, N).transpose() * Fi));
augmentedHessian.setDiagonalBlock(
i,
FiT * (Fi - Ei_P * E.block(ZDim * i, 0, ZDim, N).transpose() * Fi));
// upper triangular part of the hessian
for (size_t j = i + 1; j < m; j++) { // for each camera
for (size_t j = i + 1; j < m; j++) { // for each camera
const Eigen::Matrix<double, ZDim, ND>& Fj = Fs[j];
// (DxD) = (Dx2) * ( (2x2) * (2xD) )
augmentedHessian.setOffDiagonalBlock(i, j, -FiT
* (Ei_P * E.block(ZDim * j, 0, ZDim, N).transpose() * Fj));
augmentedHessian.setOffDiagonalBlock(
i, j,
-FiT * (Ei_P * E.block(ZDim * j, 0, ZDim, N).transpose() * Fj));
}
} // end of for over cameras
} // end of for over cameras
augmentedHessian.diagonalBlock(m)(0, 0) += b.squaredNorm();
return augmentedHessian;
@ -297,20 +303,21 @@ public:
* g = F' * (b - E * P * E' * b)
* Fixed size version
*/
template<int N> // N = 2 or 3
static SymmetricBlockMatrix SchurComplement(const FBlocks& Fs,
const Matrix& E, const Eigen::Matrix<double, N, N>& P, const Vector& b) {
return SchurComplement<N,D>(Fs, E, P, b);
template <int N> // N = 2 or 3
static SymmetricBlockMatrix SchurComplement(
const FBlocks& Fs, const Matrix& E, const Eigen::Matrix<double, N, N>& P,
const Vector& b) {
return SchurComplement<N, D>(Fs, E, P, b);
}
/// Computes Point Covariance P, with lambda parameter
template<int N> // N = 2 or 3 (point dimension)
template <int N> // N = 2 or 3 (point dimension)
static void ComputePointCovariance(Eigen::Matrix<double, N, N>& P,
const Matrix& E, double lambda, bool diagonalDamping = false) {
const Matrix& E, double lambda,
bool diagonalDamping = false) {
Matrix EtE = E.transpose() * E;
if (diagonalDamping) { // diagonal of the hessian
if (diagonalDamping) { // diagonal of the hessian
EtE.diagonal() += lambda * EtE.diagonal();
} else {
DenseIndex n = E.cols();
@ -322,7 +329,7 @@ public:
/// Computes Point Covariance P, with lambda parameter, dynamic version
static Matrix PointCov(const Matrix& E, const double lambda = 0.0,
bool diagonalDamping = false) {
bool diagonalDamping = false) {
if (E.cols() == 2) {
Matrix2 P2;
ComputePointCovariance<2>(P2, E, lambda, diagonalDamping);
@ -339,8 +346,9 @@ public:
* Dynamic version
*/
static SymmetricBlockMatrix SchurComplement(const FBlocks& Fblocks,
const Matrix& E, const Vector& b, const double lambda = 0.0,
bool diagonalDamping = false) {
const Matrix& E, const Vector& b,
const double lambda = 0.0,
bool diagonalDamping = false) {
if (E.cols() == 2) {
Matrix2 P;
ComputePointCovariance<2>(P, E, lambda, diagonalDamping);
@ -353,17 +361,17 @@ public:
}
/**
* Applies Schur complement (exploiting block structure) to get a smart factor on cameras,
* and adds the contribution of the smart factor to a pre-allocated augmented Hessian.
* Applies Schur complement (exploiting block structure) to get a smart factor
* on cameras, and adds the contribution of the smart factor to a
* pre-allocated augmented Hessian.
*/
template<int N> // N = 2 or 3 (point dimension)
static void UpdateSchurComplement(const FBlocks& Fs, const Matrix& E,
const Eigen::Matrix<double, N, N>& P, const Vector& b,
const KeyVector& allKeys, const KeyVector& keys,
/*output ->*/SymmetricBlockMatrix& augmentedHessian) {
assert(keys.size()==Fs.size());
assert(keys.size()<=allKeys.size());
template <int N> // N = 2 or 3 (point dimension)
static void UpdateSchurComplement(
const FBlocks& Fs, const Matrix& E, const Eigen::Matrix<double, N, N>& P,
const Vector& b, const KeyVector& allKeys, const KeyVector& keys,
/*output ->*/ SymmetricBlockMatrix& augmentedHessian) {
assert(keys.size() == Fs.size());
assert(keys.size() <= allKeys.size());
FastMap<Key, size_t> KeySlotMap;
for (size_t slot = 0; slot < allKeys.size(); slot++)
@ -374,39 +382,49 @@ public:
// g = F' * (b - E * P * E' * b)
// a single point is observed in m cameras
size_t m = Fs.size(); // cameras observing current point
size_t M = (augmentedHessian.rows() - 1) / D; // all cameras in the group
assert(allKeys.size()==M);
size_t m = Fs.size(); // cameras observing current point
size_t M = (augmentedHessian.rows() - 1) / D; // all cameras in the group
assert(allKeys.size() == M);
// Blockwise Schur complement
for (size_t i = 0; i < m; i++) { // for each camera in the current factor
for (size_t i = 0; i < m; i++) { // for each camera in the current factor
const MatrixZD& Fi = Fs[i];
const auto FiT = Fi.transpose();
const Eigen::Matrix<double, 2, N> Ei_P = E.template block<ZDim, N>(
ZDim * i, 0) * P;
const Eigen::Matrix<double, 2, N> Ei_P =
E.template block<ZDim, N>(ZDim * i, 0) * P;
// D = (DxZDim) * (ZDim)
// allKeys are the list of all camera keys in the group, e.g, (1,3,4,5,7)
// we should map those to a slot in the local (grouped) hessian (0,1,2,3,4)
// Key cameraKey_i = this->keys_[i];
// we should map those to a slot in the local (grouped) hessian
// (0,1,2,3,4) Key cameraKey_i = this->keys_[i];
DenseIndex aug_i = KeySlotMap.at(keys[i]);
// information vector - store previous vector
// vectorBlock = augmentedHessian(aug_i, aug_m).knownOffDiagonal();
// add contribution of current factor
augmentedHessian.updateOffDiagonalBlock(aug_i, M,
FiT * b.segment<ZDim>(ZDim * i) // F' * b
- FiT * (Ei_P * (E.transpose() * b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
augmentedHessian.updateOffDiagonalBlock(
aug_i, M,
FiT * b.segment<ZDim>(ZDim * i) // F' * b
-
FiT *
(Ei_P *
(E.transpose() *
b))); // D = (DxZDim) * (ZDimx3) * (N*ZDimm) * (ZDimm x 1)
// (DxD) += (DxZDim) * ( (ZDimxD) - (ZDimx3) * (3xZDim) * (ZDimxD) )
// add contribution of current factor
// TODO(gareth): Eigen doesn't let us pass the expression. Call eval() for now...
augmentedHessian.updateDiagonalBlock(aug_i,
((FiT * (Fi - Ei_P * E.template block<ZDim, N>(ZDim * i, 0).transpose() * Fi))).eval());
// TODO(gareth): Eigen doesn't let us pass the expression. Call eval() for
// now...
augmentedHessian.updateDiagonalBlock(
aug_i,
((FiT *
(Fi -
Ei_P * E.template block<ZDim, N>(ZDim * i, 0).transpose() * Fi)))
.eval());
// upper triangular part of the hessian
for (size_t j = i + 1; j < m; j++) { // for each camera
for (size_t j = i + 1; j < m; j++) { // for each camera
const MatrixZD& Fj = Fs[j];
DenseIndex aug_j = KeySlotMap.at(keys[j]);
@ -415,39 +433,38 @@ public:
// off diagonal block - store previous block
// matrixBlock = augmentedHessian(aug_i, aug_j).knownOffDiagonal();
// add contribution of current factor
augmentedHessian.updateOffDiagonalBlock(aug_i, aug_j,
-FiT * (Ei_P * E.template block<ZDim, N>(ZDim * j, 0).transpose() * Fj));
augmentedHessian.updateOffDiagonalBlock(
aug_i, aug_j,
-FiT * (Ei_P * E.template block<ZDim, N>(ZDim * j, 0).transpose() *
Fj));
}
} // end of for over cameras
} // end of for over cameras
augmentedHessian.diagonalBlock(M)(0, 0) += b.squaredNorm();
}
private:
private:
/// Serialization function
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int /*version*/) {
ar & (*this);
template <class ARCHIVE>
void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
ar&(*this);
}
public:
public:
GTSAM_MAKE_ALIGNED_OPERATOR_NEW
};
template<class CAMERA>
template <class CAMERA>
const int CameraSet<CAMERA>::D;
template<class CAMERA>
template <class CAMERA>
const int CameraSet<CAMERA>::ZDim;
template<class CAMERA>
struct traits<CameraSet<CAMERA> > : public Testable<CameraSet<CAMERA> > {
};
template <class CAMERA>
struct traits<CameraSet<CAMERA>> : public Testable<CameraSet<CAMERA>> {};
template<class CAMERA>
struct traits<const CameraSet<CAMERA> > : public Testable<CameraSet<CAMERA> > {
};
template <class CAMERA>
struct traits<const CameraSet<CAMERA>> : public Testable<CameraSet<CAMERA>> {};
} // \ namespace gtsam
} // namespace gtsam

4
gtsam/inference/tests/testOrdering.cpp
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@ -44,7 +44,7 @@ TEST(Ordering, constrained_ordering) {
// unconstrained version
{
Ordering actual = Ordering::Colamd(symbolicGraph);
Ordering expected = Ordering({0, 1, 2, 3, 4, 5});
Ordering expected{0, 1, 2, 3, 4, 5};
EXPECT(assert_equal(expected, actual));
}
@ -102,7 +102,7 @@ TEST(Ordering, grouped_constrained_ordering) {
constraints[5] = 2;
Ordering actual = Ordering::ColamdConstrained(symbolicGraph, constraints);
Ordering expected = {0, 1, 3, 2, 4, 5};
Ordering expected{0, 1, 3, 2, 4, 5};
EXPECT(assert_equal(expected, actual));
}

2
gtsam/linear/tests/testGaussianFactorGraph.cpp
View File

@ -222,7 +222,7 @@ TEST(GaussianFactorGraph, gradient) {
VectorValues solution = fg.optimize();
VectorValues actual2 = fg.gradient(solution);
EXPECT(assert_equal(VectorValues::Zero(solution), actual2));
}
}
/* ************************************************************************* */
TEST(GaussianFactorGraph, transposeMultiplication) {

3
gtsam/symbolic/tests/testSymbolicEliminationTree.cpp
View File

@ -96,8 +96,7 @@ TEST(EliminationTree, Create) {
EliminationTreeTester::buildHardcodedTree(simpleTestGraph1);
// Build from factor graph
Ordering order;
order += 0, 1, 2, 3, 4;
const Ordering order{0, 1, 2, 3, 4};
SymbolicEliminationTree actual(simpleTestGraph1, order);
CHECK(assert_equal(expected, actual));