class mrpt::maps::CMetricMap

Python API: mrpt.maps.CMetricMap

Overview

Declares a virtual base class for all metric maps storage classes.

In this class virtual methods are provided to allow the insertion of any type of “CObservation” objects into the metric map, thus updating the map (doesn’t matter if it is a 2D/3D grid, a point map, etc.).

Observations don’t include any information about the robot pose, just the raw observation and information about the sensor pose relative to the robot mobile base coordinates origin.

Note that all metric maps implement this mrpt::system::CObservable interface, emitting the following events:

• mrpt::obs::mrptEventMetricMapClear: Upon call of the clear() method.

mrpt::obs::mrptEventMetricMapInsert: Upon insertion of an observation that effectively modifies the map (e.g. inserting an image into a grid map will NOT raise an event, inserting a laser scan will).

To check what observations are supported by each metric map, see Maps and observations compatibility matrices.

All derived class must implement a static class factory <metric_map_class>::MapDefinition() that builds a default TMetricMapInitializer

See also:

CObservation, CSensoryFrame, CMultiMetricMap

#include <mrpt/maps/CMetricMap.h>

class CMetricMap:
    public mrpt::serialization::CSerializable,
    public mrpt::system::CObservable,
    public mrpt::Stringifyable,
    public mrpt::viz::Visualizable
{
public:
    // typedefs

    typedef std::shared_ptr<CMetricMap> Ptr;
    typedef std::shared_ptr<const CMetricMap> ConstPtr;

    // fields

    TMapGenericParams genericMapParams;

    // construction

    CMetricMap();

    // methods

    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    void clear();
    virtual bool isEmpty() const = 0;
    virtual auto boundingBox() const;
    void loadFromSimpleMap(const mrpt::maps::CSimpleMap& Map);
    bool insertObservation(const mrpt::obs::CObservation& obs, const std::optional<const mrpt::poses::CPose3D>& robotPose = std::nullopt);
    bool insertObservationPtr(const mrpt::obs::CObservation::Ptr& obs, const std::optional<const mrpt::poses::CPose3D>& robotPose = std::nullopt);
    double computeObservationLikelihood(const mrpt::obs::CObservation& obs, const mrpt::poses::CPose3D& takenFrom) const;
    virtual bool canComputeObservationLikelihood(const mrpt::obs::CObservation& obs) const;
    double computeObservationsLikelihood(const mrpt::obs::CSensoryFrame& sf, const mrpt::poses::CPose3D& takenFrom);
    bool canComputeObservationsLikelihood(const mrpt::obs::CSensoryFrame& sf) const;

    virtual void determineMatching2D(
        const mrpt::maps::CMetricMap* otherMap,
        const mrpt::poses::CPose2D& otherMapPose,
        mrpt::tfest::TMatchingPairList& correspondences,
        const TMatchingParams& params,
        TMatchingExtraResults& extraResults
        ) const;

    virtual void determineMatching3D(
        const mrpt::maps::CMetricMap* otherMap,
        const mrpt::poses::CPose3D& otherMapPose,
        mrpt::tfest::TMatchingPairList& correspondences,
        const TMatchingParams& params,
        TMatchingExtraResults& extraResults
        ) const;

    virtual float compute3DMatchingRatio(
        const mrpt::maps::CMetricMap* otherMap,
        const mrpt::poses::CPose3D& otherMapPose,
        const TMatchingRatioParams& params
        ) const;

    virtual void saveMetricMapRepresentationToFile(const std::string& filNamePrefix) const = 0;
    virtual void auxParticleFilterCleanUp();
    virtual float squareDistanceToClosestCorrespondence(float x0, float y0) const;
};

// direct descendants

template <class octree_t, class octree_node_t>
class COctoMapBase;

template <typename node_t>
class CVoxelMapBase;

class CBeaconMap;
class CHeightGridMap2D;
class CLandmarksMap;
class CMultiMetricMap;
class COccupancyGridMap2D;
class COccupancyGridMap3D;
class CPointsMap;
class CRandomFieldGridMap2D;
class CReflectivityGridMap2D;
class TestMetricMap;

Inherited Members

public:
    // typedefs

    typedef std::shared_ptr<CObject> Ptr;
    typedef std::shared_ptr<const CObject> ConstPtr;
    typedef std::unique_ptr<CObject> UniquePtr;
    typedef std::unique_ptr<const CObject> ConstUniquePtr;
    typedef std::shared_ptr<CSerializable> Ptr;
    typedef std::shared_ptr<const CSerializable> ConstPtr;

    // methods

    mrpt::rtti::CObject::Ptr duplicateGetSmartPtr() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    virtual CObject* clone() const = 0;
    virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const;
    static const mrpt::rtti::TRuntimeClassId& GetRuntimeClassIdStatic();
    virtual std::string asString() const = 0;
    Visualizable& operator = (const Visualizable&);
    Visualizable& operator = (Visualizable&&);
    virtual void getVisualizationInto(mrpt::viz::CSetOfObjects& o) const = 0;
    std::shared_ptr<mrpt::viz::CSetOfObjects> getVisualization() const;

Fields

TMapGenericParams genericMapParams

Common params to all maps.

Construction

CMetricMap()

Constructor.

Methods

virtual const mrpt::rtti::TRuntimeClassId* GetRuntimeClass() const

Returns information about the class of an object in runtime.

void clear()

Erase all the contents of the map.

virtual bool isEmpty() const = 0

Returns true if the map is empty/no observation has been inserted.

virtual auto boundingBox() const

Returns the bounding box of the metric map, or (0,0,0)-(0,0,0) (the default value of mrpt::math::TBoundingBoxf() if not implemented in the derived class or the map is empty.

void loadFromSimpleMap(const mrpt::maps::CSimpleMap& Map)

Load the map contents from a CSimpleMap object, erasing all previous content of the map.

This is done invoking insertObservation() for each observation at the mean 3D robot pose of each pose-observations pair in the CSimpleMap object.

Parameters:

std::exception

Some internal steps in invoked methods can raise exceptions on invalid parameters, etc…

See also:

insertObservation, CSimpleMap

bool insertObservation(
    const mrpt::obs::CObservation& obs,
    const std::optional<const mrpt::poses::CPose3D>& robotPose = std::nullopt
    )

Insert the observation information into this map.

This method must be implemented in derived classes. See: Maps and observations compatibility matrices

Parameters:

obs	The observation
robotPose	The 3D pose of the robot mobile base in the map reference system, or NULL (default) if you want to use the origin.

See also:

CObservation::insertObservationInto

bool insertObservationPtr(
    const mrpt::obs::CObservation::Ptr& obs,
    const std::optional<const mrpt::poses::CPose3D>& robotPose = std::nullopt
    )

A wrapper for smart pointers, just calls the non-smart pointer version.

See: Maps and observations compatibility matrices

double computeObservationLikelihood(const mrpt::obs::CObservation& obs, const mrpt::poses::CPose3D& takenFrom) const

Computes the log-likelihood of a given observation given an arbitrary robot 3D pose.

See: Maps and observations compatibility matrices

Parameters:

takenFrom	The robot’s pose the observation is supposed to be taken from.
obs	The observation.

Returns:

This method returns a log-likelihood.

See also:

Used in particle filter algorithms, see: CMultiMetricMapPDF::update

virtual bool canComputeObservationLikelihood(const mrpt::obs::CObservation& obs) const

Returns true if this map is able to compute a sensible likelihood function for this observation (i.e.

an occupancy grid map cannot with an image). See: Maps and observations compatibility matrices

Parameters:

obs	The observation.

See also:

computeObservationLikelihood, genericMapParams.enableObservationLikelihood

double computeObservationsLikelihood(const mrpt::obs::CSensoryFrame& sf, const mrpt::poses::CPose3D& takenFrom)

Returns the sum of the log-likelihoods of each individual observation within a mrpt::obs::CSensoryFrame.

See: Maps and observations compatibility matrices

Parameters:

takenFrom	The robot’s pose the observation is supposed to be taken from.
sf	The set of observations in a CSensoryFrame.

Returns:

This method returns a log-likelihood.

See also:

canComputeObservationsLikelihood

bool canComputeObservationsLikelihood(const mrpt::obs::CSensoryFrame& sf) const

Returns true if this map is able to compute a sensible likelihood function for this observation (i.e.

an occupancy grid map cannot with an image). See: Maps and observations compatibility matrices

Parameters:

sf	The observations.

See also:

canComputeObservationLikelihood

virtual void determineMatching2D(
    const mrpt::maps::CMetricMap* otherMap,
    const mrpt::poses::CPose2D& otherMapPose,
    mrpt::tfest::TMatchingPairList& correspondences,
    const TMatchingParams& params,
    TMatchingExtraResults& extraResults
    ) const

Computes the matching between this and another 2D point map, which includes finding:

• The set of points pairs in each map

• The mean squared distance between corresponding pairs.

The algorithm is:

• For each point in “otherMap”:

  • Transform the point according to otherMapPose

  • Search with a KD-TREE the closest correspondences in “this” map.

  • Add to the set of candidate matchings, if it passes all the thresholds in params.

This method is the most time critical one into ICP-like algorithms.

Parameters:

otherMap	[IN] The other map to compute the matching with.
otherMapPose	[IN] The pose of the other map as seen from “this”.
params	[IN] Parameters for the determination of pairings.
correspondences	[OUT] The detected matchings pairs.
extraResults	[OUT] Other results.

See also:

compute3DMatchingRatio

virtual void determineMatching3D(
    const mrpt::maps::CMetricMap* otherMap,
    const mrpt::poses::CPose3D& otherMapPose,
    mrpt::tfest::TMatchingPairList& correspondences,
    const TMatchingParams& params,
    TMatchingExtraResults& extraResults
    ) const

Computes the matchings between this and another 3D points map - method used in 3D-ICP.

This method finds the set of point pairs in each map.

The method is the most time critical one into ICP-like algorithms.

The algorithm is:

• For each point in “otherMap”:

  • Transform the point according to otherMapPose

  • Search with a KD-TREE the closest correspondences in “this” map.

  • Add to the set of candidate matchings, if it passes all the thresholds in params.

Parameters:

otherMap	[IN] The other map to compute the matching with.
otherMapPose	[IN] The pose of the other map as seen from “this”.
params	[IN] Parameters for the determination of pairings.
correspondences	[OUT] The detected matchings pairs.
extraResults	[OUT] Other results.

See also:

compute3DMatchingRatio

virtual float compute3DMatchingRatio(
    const mrpt::maps::CMetricMap* otherMap,
    const mrpt::poses::CPose3D& otherMapPose,
    const TMatchingRatioParams& params
    ) const

Computes the ratio in [0,1] of correspondences between “this” and the “otherMap” map, whose 6D pose relative to “this” is “otherMapPose” In the case of a multi-metric map, this returns the average between the maps.

This method always return 0 for grid maps.

Parameters:

otherMap	[IN] The other map to compute the matching with.
otherMapPose	[IN] The 6D pose of the other map as seen from “this”.
params	[IN] Matching parameters

Returns:

The matching ratio [0,1]

See also:

determineMatching2D

virtual void saveMetricMapRepresentationToFile(const std::string& filNamePrefix) const = 0

This virtual method saves the map to a file “filNamePrefix”+< some_file_extension >, as an image or in any other applicable way (Notice that other methods to save the map may be implemented in classes implementing this virtual interface).

virtual void auxParticleFilterCleanUp()

This method is called at the end of each “prediction-update-map insertion” cycle within “mrpt::slam::CMetricMapBuilderRBPF::processActionObservation”.

This method should normally do nothing, but in some cases can be used to free auxiliary cached variables.

virtual float squareDistanceToClosestCorrespondence(float x0, float y0) const

Returns the square distance from the 2D point (x0,y0) to the closest correspondence in the map.