15 class GaussianBayesNet;
20 template<
size_t D,
size_t ZDim>
39 for (
size_t k = 0; k <
FBlocks.size(); ++k) {
41 gfg.
add(pointKey, E.block<ZDim, 3>(ZDim * k, 0), key,
FBlocks[k],
42 b.segment < ZDim > (ZDim * k),
model);
47 boost::shared_ptr<GaussianBayesNet> bn;
50 variables.push_back(pointKey);
noiseModel::Diagonal::shared_ptr model
boost::shared_ptr< This > shared_ptr
shared_ptr to this class
RegularJacobianFactor< D > Base
const vector< Matrix26, Eigen::aligned_allocator< Matrix26 > > FBlocks
const gtsam::Key pointKey
JacobianFactorQR(const KeyVector &keys, const std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > &FBlocks, const Matrix &E, const Matrix3 &P, const Vector &b, const SharedDiagonal &model=SharedDiagonal())
void add(const GaussianFactor &factor)
FastVector< Key > KeyVector
Define collection type once and for all - also used in wrappers.
STL compatible allocator to use with types requiring a non standrad alignment.
std::pair< boost::shared_ptr< BayesNetType >, boost::shared_ptr< FactorGraphType > > eliminatePartialSequential(const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
JacobianFactor class with fixed sized blcoks.
Linear Factor Graph where all factors are Gaussians.
noiseModel::Diagonal::shared_ptr SharedDiagonal
const KeyVector & keys() const
Access the factor's involved variable keys.
Eigen::Matrix< double, ZDim, D > MatrixZD
The matrix class, also used for vectors and row-vectors.
std::uint64_t Key
Integer nonlinear key type.
friend GTSAM_EXPORT std::pair< boost::shared_ptr< GaussianConditional >, shared_ptr > EliminateQR(const GaussianFactorGraph &factors, const Ordering &keys)