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15 class GaussianBayesNet;
20 template<
size_t D,
size_t ZDim>
39 for (
size_t k = 0; k <
FBlocks.size(); ++k) {
Linear Factor Graph where all factors are Gaussians.
const gtsam::Key pointKey
FastVector< Key > KeyVector
Define collection type once and for all - also used in wrappers.
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())
STL compatible allocator to use with types requiring a non standrad alignment.
std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > FBlocks
friend GTSAM_EXPORT std::pair< std::shared_ptr< GaussianConditional >, shared_ptr > EliminateQR(const GaussianFactorGraph &factors, const Ordering &keys)
std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > > eliminatePartialSequential(const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const
JacobianFactor class with fixed sized blcoks.
noiseModel::Diagonal::shared_ptr SharedDiagonal
static const int ZDim
Measurement dimension (Point2)
RegularJacobianFactor< D > Base
Eigen::Matrix< double, ZDim, D > MatrixZD
noiseModel::Diagonal::shared_ptr model
const gtsam::Symbol key('X', 0)
const KeyVector & keys() const
Access the factor's involved variable keys.
The matrix class, also used for vectors and row-vectors.
std::uint64_t Key
Integer nonlinear key type.
void add(const GaussianFactor &factor)
gtsam
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autogenerated on Thu Dec 19 2024 04:01:23