[mrpt-graphslam]

Detailed Description

GraphSLAM algorithms.

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Library `mrpt-graphslam`

This C++ library is part of MRPT and can be installed in Debian-based systems with:

    sudo apt install libmrpt-graphslam-dev

See: Using MRPT from your CMake project

Basic graph-SLAM algorithms: See the namespace mrpt::graphslam.

For an introduction to graph-slam maps refer to the summary in https://www.mrpt.org/Graph-SLAM_maps

Classes
struct	mrpt::graphslam::apps::TRegistrationDeciderProps
	Properties struct for the Registration Decider classes. More...
struct	mrpt::graphslam::apps::TOptimizerProps
	Properties struct for the Optimizer classes. More...
struct	mrpt::graphslam::apps::TUserOptionsChecker< GRAPH_t >
	Class containing the declarations of supplementary methods that can be used in application-related code. More...
class	mrpt::graphslam::CGraphSlamEngine< GRAPH_T >
	Main file for the GraphSlamEngine. More...
class	mrpt::graphslam::deciders::CICPCriteriaERD< GRAPH_T >
	ICP-based Edge Registration. More...
class	mrpt::graphslam::deciders::CLoopCloserERD< GRAPH_T >
	Edge Registration Decider scheme specialized in Loop Closing. More...
class	mrpt::graphslam::optimizers::CLevMarqGSO< GRAPH_T >
	Levenberg-Marquardt non-linear graph slam optimization scheme. More...
class	mrpt::graphslam::deciders::CEdgeRegistrationDecider< GRAPH_T >
	Interface for implementing edge registration classes. More...
class	mrpt::graphslam::optimizers::CGraphSlamOptimizer< GRAPH_t >
	Interface for implementing graphSLAM optimizer classes. More...
class	mrpt::graphslam::deciders::CIncrementalNodeRegistrationDecider< GRAPH_T >
	Incremental Node registration decider. More...
class	mrpt::graphslam::deciders::CNodeRegistrationDecider< GRAPH_T >
	Interface for implementing node registration classes. More...
class	mrpt::graphslam::CRegistrationDeciderOrOptimizer< GRAPH_T >
	Interface for implementing node/edge registration deciders or optimizer classes. More...
class	mrpt::graphslam::detail::CEdgeCounter
	Generic class for tracking the total number of edges for different tpes of edges and for storing visualization-related information for each type. More...
class	mrpt::graphslam::deciders::CRangeScanOps< GRAPH_T >
	Class for keeping together all the RangeScanner-related functions. More...
class	mrpt::graphslam::CWindowManager
	Class acts as a container for storing pointers to mrpt::gui::CDisplayWindow3D, mrpt::graphslam::CWindowObserver instances. More...
class	mrpt::graphslam::CWindowObserver
	Monitor events in the visualization window. More...
struct	mrpt::graphslam::TSlidingWindow
	Class to monitor the evolution of a statistical quantity. More...
struct	mrpt::graphslam::TUncertaintyPath< GRAPH_T >
	Holds the data of an information path. More...
class	mrpt::graphslam::deciders::CEmptyNRD< GRAPH_T >
	Empty Node Registration Decider. More...
class	mrpt::graphslam::deciders::CFixedIntervalsNRD< GRAPH_T >
	Fixed Intervals Odometry-based Node Registration. More...
class	mrpt::graphslam::deciders::CICPCriteriaNRD< GRAPH_T >
	ICP-based Fixed Intervals Node Registration. More...
struct	mrpt::graphslam::graphslam_traits< GRAPH_T >
	Auxiliary traits template for use among graph-slam problems to make life easier with these complicated, long data type names. More...
struct	mrpt::graphslam::TResultInfoSpaLevMarq
	Output information for mrpt::graphslam::optimize_graph_spa_levmarq() More...

Namespaces
	mrpt::graphslam
	SLAM methods related to graphs of pose constraints.

Functions
template<class GRAPH_T , class FEEDBACK_CALLABLE = typename graphslam_traits<GRAPH_T>::TFunctorFeedback>
void	mrpt::graphslam::optimize_graph_spa_levmarq (GRAPH_T &graph, TResultInfoSpaLevMarq &out_info, const std::set< mrpt::graphs::TNodeID > *in_nodes_to_optimize=nullptr, const mrpt::system::TParametersDouble &extra_params=mrpt::system::TParametersDouble(), FEEDBACK_CALLABLE functor_feedback=FEEDBACK_CALLABLE())
	Optimize a graph of pose constraints using the Sparse Pose Adjustment (SPA) sparse representation and a Levenberg-Marquardt optimizer. More...

Function Documentation

optimize_graph_spa_levmarq()

template<class GRAPH_T , class FEEDBACK_CALLABLE = typename graphslam_traits<GRAPH_T>::TFunctorFeedback>

void mrpt::graphslam::optimize_graph_spa_levmarq	(	GRAPH_T &	graph,
		TResultInfoSpaLevMarq &	out_info,
		const std::set< mrpt::graphs::TNodeID > *	in_nodes_to_optimize = nullptr,
		const mrpt::system::TParametersDouble &	extra_params = mrpt::system::TParametersDouble(),
		FEEDBACK_CALLABLE	functor_feedback = FEEDBACK_CALLABLE()
	)

Optimize a graph of pose constraints using the Sparse Pose Adjustment (SPA) sparse representation and a Levenberg-Marquardt optimizer.

This method works for all types of graphs derived from CNetworkOfPoses (see its reference mrpt::graphs::CNetworkOfPoses for the list). The input data are all the pose constraints in graph (graph.edges), and the gross first estimations of the "global" pose coordinates (in graph.nodes).

Note that these first coordinates can be obtained with mrpt::graphs::CNetworkOfPoses::dijkstra_nodes_estimate().

The method implemented in this file is based on this work:

• "Efficient Sparse Pose Adjustment for 2D Mapping", Kurt Konolige et al., 2010. , but generalized for not only 2D but 2D and 3D poses, and using on-manifold optimization.

Parameters

[in,out]	graph	The input edges and output poses.
[out]	out_info	Some basic output information on the process.
[in]	nodes_to_optimize	The list of nodes whose global poses are to be optimized. If nullptr (default), all the node IDs are optimized (but that marked as root in the graph).
[in]	extra_params	Optional parameters, see below.
[in]	functor_feedback	Optional: a pointer to a user function can be set here to be called on each LM loop iteration (eg to refresh the current state and error, refresh a GUI, etc.)

List of optional parameters by name in "extra_params":

• "verbose": (default=0) If !=0, produce verbose ouput.
• "max_iterations": (default=100) Maximum number of Lev-Marq. iterations.
• "initial_lambda": (default=0) <=0 means auto guess, otherwise, initial lambda value for the lev-marq algorithm.
• "tau": (default=1e-6) Initial tau value for the lev-marq algorithm.
• "e1": (default=1e-6) Lev-marq algorithm iteration stopping criterion #1: |gradient| < e1
• "e2": (default=1e-6) Lev-marq algorithm iteration stopping criterion #2: |delta_incr| < e2*(x_norm+e2)

Note

The following graph types are supported: mrpt::graphs::CNetworkOfPoses2D, mrpt::graphs::CNetworkOfPoses3D, mrpt::graphs::CNetworkOfPoses2DInf, mrpt::graphs::CNetworkOfPoses3DInf

Template Parameters

GRAPH_T

Normally a mrpt::graphs::CNetworkOfPoses<EDGE_TYPE,MAPS_IMPLEMENTATION>. Users won't have to write this template argument by hand, since the compiler will auto-fit it depending on the type of the graph object.

See also

The example "graph_slam_demo"

Note

Implementation can be found in file levmarq_impl.h

Definition at line 79 of file levmarq.h.

References __CURRENT_FUNCTION_NAME__, ASSERTDEB_, ASSERTDEB_ABOVE_, ASSERTDEB_EQUAL_, ASSERTMSG_, mrpt::containers::begin(), mrpt::math::CSparseMatrix::compressFromTriplet(), mrpt::system::CTimeLogger::enter(), mrpt::graphslam::TResultInfoSpaLevMarq::final_total_sq_error, mrpt::containers::find_in_vector(), mrpt::format(), mrpt::math::CSparseMatrix::insert_entry_fast(), mrpt::math::CSparseMatrix::insert_submatrix(), mrpt::keep_max(), mrpt::system::CTimeLogger::leave(), MRPT_END, MRPT_START, mrpt::math::norm(), mrpt::math::norm_inf(), mrpt::graphslam::TResultInfoSpaLevMarq::num_iters, mrpt::math::MatrixVectorBase< Scalar, Derived >::setZero(), mrpt::math::CVectorDynamic< T >::size(), mrpt::square(), mrpt::math::UNINITIALIZED_MATRIX, and verbose.

Referenced by mrpt::graphslam::optimizers::CLevMarqGSO< GRAPH_T >::_optimizeGraph(), mrpt::hmtslam::CHierarchicalMapMHPartition::computeGloballyConsistentNodeCoordinates(), GraphTester< my_graph_t >::test_optimize_compare_known_solution(), and GraphTester< my_graph_t >::test_ring_path().

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