Class CMultiMetricMapPDF

Nested Relationships

Nested Types

Inheritance Relationships

Base Types

  • public mrpt::serialization::CSerializable

  • public mrpt::bayes::CParticleFilterData< CRBPFParticleData >

  • public mrpt::bayes::CParticleFilterDataImpl< CMultiMetricMapPDF, mrpt::bayes::CParticleFilterData< CRBPFParticleData >::CParticleList >

  • public mrpt::slam::PF_implementation< CRBPFParticleData, CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER > (Template Class PF_implementation)

Class Documentation

class CMultiMetricMapPDF : public mrpt::serialization::CSerializable, public mrpt::bayes::CParticleFilterData<CRBPFParticleData>, public mrpt::bayes::CParticleFilterDataImpl<CMultiMetricMapPDF, mrpt::bayes::CParticleFilterData<CRBPFParticleData>::CParticleList>, public mrpt::slam::PF_implementation<CRBPFParticleData, CMultiMetricMapPDF, mrpt::bayes::particle_storage_mode::POINTER>

Declares a class that represents a Rao-Blackwellized set of particles for solving the SLAM problem (This class is the base of RBPF-SLAM applications). This class is used internally by the map building algorithm in “mrpt::slam::CMetricMapBuilderRBPF”

See also

mrpt::slam::CMetricMapBuilderRBPF

Virtual methods that the PF_implementations assume exist.

virtual mrpt::math::TPose3D getLastPose(size_t i, bool &pose_is_valid) const override

Return the last robot pose in the i’th particle; is_valid_pose will be false if there is no such last pose.

Throws:

std::exception – on out-of-range particle index

void PF_SLAM_implementation_custom_update_particle_with_new_pose(CParticleDataContent *particleData, const mrpt::math::TPose3D &newPose) const override
bool PF_SLAM_implementation_doWeHaveValidObservations(const CParticleList &particles, const mrpt::obs::CSensoryFrame *sf) const override
virtual bool PF_SLAM_implementation_skipRobotMovement() const override

Make a specialization if needed, eg. in the first step in SLAM.

virtual double PF_SLAM_computeObservationLikelihoodForParticle(const mrpt::bayes::CParticleFilter::TParticleFilterOptions &PF_options, size_t particleIndexForMap, const mrpt::obs::CSensoryFrame &observation, const mrpt::poses::CPose3D &x) const override

Evaluate the observation likelihood for one particle at a given location

Public Functions

CMultiMetricMapPDF() = default

Constructor

CMultiMetricMapPDF(const bayes::CParticleFilter::TParticleFilterOptions &opts, const mrpt::maps::TSetOfMetricMapInitializers &mapsInitializers, const TPredictionParams &predictionOptions)
void clear(const mrpt::poses::CPose2D &initialPose)

Clear all elements of the maps, and restore all paths to a single starting pose

void clear(const mrpt::poses::CPose3D &initialPose)

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

void clear(const mrpt::maps::CSimpleMap &prevMap, const mrpt::poses::CPose3D &currentPose)

Resets the map by loading an already-mapped map for past poses. Current robot pose should be normally set to the last keyframe in the simplemap.

void getEstimatedPosePDFAtTime(size_t timeStep, mrpt::poses::CPose3DPDFParticles &out_estimation) const

Returns the estimate of the robot pose as a particles PDF for the instant of time “timeStep”, from 0 to N-1.

See also

getEstimatedPosePDF

void getEstimatedPosePDF(mrpt::poses::CPose3DPDFParticles &out_estimation) const

Returns the current estimate of the robot pose, as a particles PDF.

See also

getEstimatedPosePDFAtTime

const CMultiMetricMap *getAveragedMetricMapEstimation()

Returns the weighted averaged map based on the current best estimation. If you need a persistent copy of this object, please use “CSerializable::duplicate” and use the copy.

See also

Almost 100% sure you would prefer the best current map, given by getCurrentMostLikelyMetricMap()

const CMultiMetricMap *getCurrentMostLikelyMetricMap() const

Returns a pointer to the current most likely map (associated to the most likely particle)

inline size_t getNumberOfObservationsInSimplemap() const

Get the number of CSensoryFrame inserted into the internal member SFs

bool insertObservation(mrpt::obs::CSensoryFrame &sf)

Insert an observation to the map, at each particle’s pose and to each particle’s metric map.

Parameters:

sf – The SF to be inserted

Returns:

true if any may was updated, false otherwise

void getPath(size_t i, std::deque<math::TPose3D> &out_path) const

Return the path (in absolute coordinate poses) for the i’th particle.

Throws:

On – index out of bounds

double getCurrentEntropyOfPaths()

Returns the current entropy of paths, computed as the average entropy of poses along the path, where entropy of each pose estimation is computed as the entropy of the gaussian approximation covariance.

double getCurrentJointEntropy()

Returns the joint entropy estimation over paths and maps, according to “Information Gain-based Exploration Using” by C. Stachniss, G. Grissetti and W.Burgard.

void updateSensoryFrameSequence()

Update the poses estimation of the member “SFs” according to the current path belief.

void saveCurrentPathEstimationToTextFile(const std::string &fil)

A logging utility: saves the current path estimation for each particle in a text file (a row per particle, each 3-column-entry is a set [x,y,phi], respectively).

Public Members

mrpt::maps::CMultiMetricMapPDF::TPredictionParams options

Protected Functions

void prediction_and_update_pfStandardProposal(const mrpt::obs::CActionCollection *action, const mrpt::obs::CSensoryFrame *observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options) override
void prediction_and_update_pfOptimalProposal(const mrpt::obs::CActionCollection *action, const mrpt::obs::CSensoryFrame *observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options) override
void prediction_and_update_pfAuxiliaryPFOptimal(const mrpt::obs::CActionCollection *action, const mrpt::obs::CSensoryFrame *observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options) override
void prediction_and_update_pfAuxiliaryPFStandard(const mrpt::obs::CActionCollection *action, const mrpt::obs::CSensoryFrame *observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options) override

Friends

friend class mrpt::slam::CMetricMapBuilderRBPF
struct TPredictionParams : public mrpt::config::CLoadableOptions

The struct for passing extra simulation parameters to the prediction/update stage when running a particle filter.

See also

prediction_and_update

Public Functions

TPredictionParams() = default

Default settings method

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

Public Members

int pfOptimalProposal_mapSelection = {0}

[pf optimal proposal only] Only for PF algorithm=2 (Exact “pfOptimalProposal”) Select the map on which to calculate the optimal proposal distribution. Values: 0: Gridmap -> Uses Scan matching-based approximation (based on Stachniss’ work) 1: Landmarks -> Uses matching to approximate optimal 2: Beacons -> Used for exact optimal proposal in RO-SLAM 3: Pointsmap -> Uses Scan matching-based approximation with a map of points (based on Stachniss’ work) Default = 0

float ICPGlobalAlign_MinQuality = {0.70f}

[prediction stage][pf optimal proposal only] If useICPGlobalAlign_withGrid=true, this is the minimum quality ratio [0,1] of the alignment such as it will be accepted. Otherwise, raw odometry is used for those bad cases (default=0.7).

mrpt::slam::TKLDParams KLD_params
mrpt::slam::CICP::TConfigParams icp_params

ICP parameters, used only when “PF_algorithm=2” in the particle filter.