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

Smore more refactoring to RegularImplicit..

release/4.3a0
dellaert 2015-03-02 08:52:35 -08:00
parent 05fcbe6556
commit 11a6ba412c
1 changed files with 7 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
gtsam/slam/SmartFactorBase.h
View File

@ -337,14 +337,14 @@ public:
double f = computeJacobians(Fblocks, E, b, cameras, point);
Matrix3 P = PointCov(E, lambda, diagonalDamping);
// we create directly a SymmetricBlockMatrix
// Create a SymmetricBlockMatrix
size_t M1 = Dim * m + 1;
std::vector<DenseIndex> dims(m + 1); // this also includes the b term
std::fill(dims.begin(), dims.end() - 1, Dim);
dims.back() = 1;
SymmetricBlockMatrix augmentedHessian(dims, Matrix::Zero(M1, M1));
// build augmented hessian
SymmetricBlockMatrix augmentedHessian(dims, Matrix::Zero(M1, M1));
sparseSchurComplement(Fblocks, E, P, b, augmentedHessian);
augmentedHessian(m, m)(0, 0) = f;
@ -352,21 +352,6 @@ public:
augmentedHessian);
}
/**
* 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.
*/
void updateSparseSchurComplement(const std::vector<KeyMatrix2D>& Fblocks,
const Matrix& E, const Matrix3& P /*Point Covariance*/, const Vector& b,
const double f, const FastVector<Key>& allKeys,
/*output ->*/SymmetricBlockMatrix& augmentedHessian) const {
FastMap<Key, size_t> KeySlotMap;
for (size_t slot = 0; slot < allKeys.size(); slot++)
KeySlotMap.insert(std::make_pair(allKeys[slot], slot));
RegularImplicitSchurFactor<Dim>::updateSparseSchurComplement(Fblocks, E, P,
b, f, this->keys_, KeySlotMap, augmentedHessian);
}
/**
* Add the contribution of the smart factor to a pre-allocated Hessian,
* using sparse linear algebra. More efficient than the creation of the
@ -376,13 +361,13 @@ public:
const double lambda, bool diagonalDamping,
SymmetricBlockMatrix& augmentedHessian,
const FastVector<Key> allKeys) const {
std::vector<KeyMatrix2D> Fblocks;
Matrix E;
Vector b;
std::vector<KeyMatrix2D> Fblocks;
double f = computeJacobians(Fblocks, E, b, cameras, point);
Matrix3 P = PointCov(E, lambda, diagonalDamping);
updateSparseSchurComplement(Fblocks, E, P, b, f, allKeys, augmentedHessian); // augmentedHessian.matrix().block<Dim,Dim> (i,j) = ...
RegularImplicitSchurFactor<Dim, ZDim>::updateSparseSchurComplement(Fblocks,
E, P, b, f, allKeys, keys_, augmentedHessian);
}
/// Whiten the Jacobians computed by computeJacobians using noiseModel_
@ -400,9 +385,9 @@ public:
boost::shared_ptr<RegularImplicitSchurFactor<Dim, ZDim> > //
createRegularImplicitSchurFactor(const Cameras& cameras, const Point3& point,
double lambda = 0.0, bool diagonalDamping = false) const {
std::vector<KeyMatrix2D> F;
Matrix E;
Vector b;
std::vector<KeyMatrix2D> F;
computeJacobians(F, E, b, cameras, point);
whitenJacobians(F, E, b);
Matrix3 P = PointCov(E, lambda, diagonalDamping);
@ -416,12 +401,11 @@ public:
boost::shared_ptr<JacobianFactorQ<Dim, ZDim> > createJacobianQFactor(
const Cameras& cameras, const Point3& point, double lambda = 0.0,
bool diagonalDamping = false) const {
std::vector<KeyMatrix2D> F;
Matrix E;
Vector b;
std::vector<KeyMatrix2D> F;
computeJacobians(F, E, b, cameras, point);
const size_t M = b.size();
std::cout << M << std::endl;
Matrix3 P = PointCov(E, lambda, diagonalDamping);
SharedIsotropic n = noiseModel::Isotropic::Sigma(M, noiseModel_->sigma());
return boost::make_shared<JacobianFactorQ<Dim, ZDim> >(F, E, P, b, n);