class mrpt::graphs::ScalarFactorGraph

Overview

Sparse solver for GMRF (Gaussian Markov Random Fields) graphical models.

The design of this class is optimized for large problems (e.g. >1e3 nodes, >1e4 constrainst) by leaving to the user/caller the responsibility of allocating all “nodes” and constraints. This class can be seen as an intermediary solution between current methods in mrpt::graphslam and the well-known G2O library:

Assumptions/limitations:

  • Linear error functions (for now).

  • Scalar (1-dim) error functions.

  • Gaussian factors.

  • Solver: Eigen SparseQR.

Usage:

  • Call initialize() to set the number of nodes.

  • Call addConstraints() to insert constraints. This may be called more than once.

  • Call updateEstimation() to run one step of the linear SparseQR solver.

#include <mrpt/graphs/ScalarFactorGraph.h>

class ScalarFactorGraph: public mrpt::system::COutputLogger
{
public:
    // structs

    struct BinaryFactorVirtualBase;
    struct FactorBase;
    struct UnaryFactorVirtualBase;

    // construction

    ScalarFactorGraph();

    // methods

    void clear();
    void initialize(size_t nodeCount);
    void addConstraint(const UnaryFactorVirtualBase& listOfConstraints);
    void addConstraint(const BinaryFactorVirtualBase& listOfConstraints);
    bool eraseConstraint(const FactorBase& c);
    void clearAllConstraintsByType_Unary();
    void clearAllConstraintsByType_Binary();

    void updateEstimation(
        mrpt::math::CVectorDouble& solved_x_inc,
        mrpt::math::CVectorDouble* solved_variances = nullptr
        );

    bool isProfilerEnabled() const;
    void enableProfiler(bool enable = true);
};

Inherited Members

public:
    // structs

    struct TMsg;

    // fields

    bool logging_enable_console_output {true};
    bool logging_enable_keep_record {false};

    // methods

    void logStr(const VerbosityLevel level, std::string_view msg_str) const;
    void logFmt(const VerbosityLevel level, const char* fmt, ...) const;
    void void logCond(const VerbosityLevel level, bool cond, const std::string& msg_str) const;
    void setLoggerName(const std::string& name);
    std::string getLoggerName() const;
    void setVerbosityLevel(const VerbosityLevel level);
    void setVerbosityLevelForCallbacks(const VerbosityLevel level);
    void setMinLoggingLevel(const VerbosityLevel level);
    VerbosityLevel getMinLoggingLevel() const;
    VerbosityLevel getMinLoggingLevelForCallbacks() const;
    bool isLoggingLevelVisible(VerbosityLevel level) const;
    void getLogAsString(std::string& log_contents) const;
    std::string getLogAsString() const;
    void writeLogToFile(const std::optional<std::string>& fname_in = std::nullopt) const;
    void dumpLogToConsole() const;
    std::string getLoggerLastMsg() const;
    void getLoggerLastMsg(std::string& msg_str) const;
    void loggerReset();
    void logRegisterCallback(output_logger_callback_t userFunc);
    bool logDeregisterCallback(output_logger_callback_t userFunc);
    COutputLogger& operator = (const COutputLogger&);
    COutputLogger& operator = (COutputLogger&&);
    static std::array<mrpt::system::ConsoleForegroundColor, NUMBER_OF_VERBOSITY_LEVELS>& logging_levels_to_colors();
    static const std::array<const char*, NUMBER_OF_VERBOSITY_LEVELS>& logging_levels_to_names();

Methods

void clear()

Reset state: remove all constraints and nodes.

void initialize(size_t nodeCount)

Initialize the GMRF internal state and copy the prior factors.

void addConstraint(const UnaryFactorVirtualBase& listOfConstraints)

Insert constraints into the GMRF problem.

Parameters:

listOfConstraints

List of user-implemented constraints. A pointer to the passed object is kept, but memory ownship REMAINS being responsibility of the caller. This is done such that arrays/vectors of constraints can be more efficiently allocated if their type is known at build time.

 
bool eraseConstraint(const FactorBase& c)

Removes a constraint.

Return true if found and deleted correctly.