struct mrpt::slam::CRangeBearingKFSLAM::TOptions¶

The options for the algorithm.

#include <mrpt/slam/CRangeBearingKFSLAM.h>

struct TOptions: public mrpt::config::CLoadableOptions
{
    //
fields

    mrpt::math::CVectorFloat stds_Q_no_odo;
    float std_sensor_range {0.01f};
    float std_sensor_yaw;
    float std_sensor_pitch;
    float std_odo_z_additional {0};
    bool doPartitioningExperiment {false};
    float quantiles_3D_representation {3};
    int partitioningMethod {0};
    TDataAssociationMethod data_assoc_method {assocNN};
    TDataAssociationMetric data_assoc_metric {metricMaha};
    double data_assoc_IC_chi2_thres {0.99};
    TDataAssociationMetric data_assoc_IC_metric {metricMaha};
    double data_assoc_IC_ml_threshold {0.0};
    bool create_simplemap {false};
    bool force_ignore_odometry {false};

    //
methods

    virtual void loadFromConfigFile(const mrpt::config::CConfigFileBase& source, const std::string& section);
    virtual void dumpToTextStream(std::ostream& out) const;
};

Inherited Members¶

public:
    //
methods

    virtual void loadFromConfigFile(const mrpt::config::CConfigFileBase& source, const std::string& section) = 0;
    void loadFromConfigFileName(const std::string& config_file, const std::string& section);
    virtual void saveToConfigFile(mrpt::config::CConfigFileBase& target, const std::string& section) const;
    void saveToConfigFileName(const std::string& config_file, const std::string& section) const;
    void dumpToConsole() const;
    virtual void dumpToTextStream(std::ostream& out) const;

Fields¶

mrpt::math::CVectorFloat stds_Q_no_odo

A 7-length vector with the std.

deviation of the transition model in (x,y,z, qr,qx,qy,qz) used only when there is no odometry (if there is odo, its uncertainty values will be used instead); x y z: In meters.

float std_sensor_range {0.01f}

The std.

deviation of the sensor (for the matrix R in the kalman filters), in meters and radians.

float std_odo_z_additional {0}

Additional std.

dev. to sum to the motion model in the z axis (useful when there is only 2D odometry and we want to put things hard to the algorithm) (default=0)

bool doPartitioningExperiment {false}

If set to true (default=false), map will be partitioned using the method stated by partitioningMethod.

float quantiles_3D_representation {3}

Default = 3.

int partitioningMethod {0}

Applicable only if “doPartitioningExperiment=true”.

0: Automatically detect partition through graph-cut. N>=1: Cut every “N” observations.

double data_assoc_IC_chi2_thres {0.99}

Threshold in [0,1] for the chi2square test for individual compatibility between predictions and observations (default: 0.99)

TDataAssociationMetric data_assoc_IC_metric {metricMaha}

Whether to use mahalanobis (->chi2 criterion) vs.

Matching likelihood.

double data_assoc_IC_ml_threshold {0.0}

Only if data_assoc_IC_metric==ML, the log-ML threshold (Default=0.0)

bool create_simplemap {false}

Whether to fill m_SFs (default=false)

bool force_ignore_odometry {false}

Whether to ignore the input odometry and behave as if there was no odometry at all (default: false)

Methods¶

virtual void loadFromConfigFile(const mrpt::config::CConfigFileBase& source, const std::string& section)

This method load the options from a “.ini”-like file or memory-stored string list.

Only those parameters found in the given “section” and having the same name that the variable are loaded. Those not found in the file will stay with their previous values (usually the default values loaded at initialization). An example of an “.ini” file:

[section]
resolution    = 0.10   // blah blah...
modeSelection = 1      // 0=blah, 1=blah,...