Modules

Here is a list of all modules:

[detail level 12]

▼Solving of sparse linear systems with least-squares	Densely partially eliminate with Cholesky factorization
Solving by multifrontal variable elimination (QR and Cholesky)	An EliminatableClusterTree, i.e., a set of variable clusters with factors, arranged in a tree, with the additional property that it represents the clique tree associated with a Bayes net
Solving by sequential variable elimination (QR and Cholesky)
Base	FastMap is a thin wrapper around std::map that uses the boost fast_pool_allocator instead of the default STL allocator
Geometry	Common base class for all calibration models
Navigation	Base class for prior on attitude Example:
SLAM	PreintegratedCombinedMeasurements integrates the IMU measurements (rotation rates and accelerations) and the corresponding covariance matrix
Nonlinear	Custom factor that takes a std::function as the error
ISAM2	Implementation of the full ISAM2 algorithm for incremental nonlinear optimization
SAM	Binary factor for a bearing measurement Works for any two types A1,A2 for which the functor Bearing<A1,A2>() is defined
SFM	The MFAS class to solve a Minimum feedback arc set (MFAS) problem