Detailed Description

Declares a class that represents a Probability Density Function (PDF) over a 2D pose (x,y,phi), using a set of weighted samples.

This class also implements particle filtering for robot localization. See the MRPT application "app/pf-localization" for an example of usage.

See also:: CMonteCarloLocalization3D, CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable

#include <mrpt/slam/CMonteCarloLocalization2D.h>

Inheritance diagram for mrpt::slam::CMonteCarloLocalization2D:

[legend]

List of all members.

Public Types
enum	{ is_3D_val = 0 }
enum	{ is_PDF_val = 1 }
typedef TDATA	type_value
	The type of the state the PDF represents.
typedef T	CParticleDataContent
	This is the type inside the corresponding CParticleData class.
typedef CProbabilityParticle< T >	CParticleData
	Use this to refer to each element in the m_particles array.
typedef std::deque< CParticleData >	CParticleList
	Use this type to refer to the list of particles m_particles.
typedef double(*	TParticleProbabilityEvaluator )(const bayes::CParticleFilter::TParticleFilterOptions &PF_options, const CParticleFilterCapable obj, size_t index, const void action, const void *observation)
	A callback function type for evaluating the probability of m_particles of being selected, used in "fastDrawSample".
Public Member Functions
	CMonteCarloLocalization2D (size_t M=1)
	Constructor.
virtual	~CMonteCarloLocalization2D ()
	Destructor.
void	resetUniformFreeSpace (COccupancyGridMap2D *theMap, const double freeCellsThreshold=0.7, const int particlesCount=-1, const double x_min=-1e10f, const double x_max=1e10f, const double y_min=-1e10f, const double y_max=1e10f, const double phi_min=-M_PI, const double phi_max=M_PI)
	Reset the PDF to an uniformly distributed one, but only in the free-space of a given 2D occupancy-grid-map.
void	prediction_and_update_pfStandardProposal (const mrpt::slam::CActionCollection action, const mrpt::slam::CSensoryFrame observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Update the m_particles, predicting the posterior of robot pose and map after a movement command.
void	prediction_and_update_pfAuxiliaryPFStandard (const mrpt::slam::CActionCollection action, const mrpt::slam::CSensoryFrame observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Update the m_particles, predicting the posterior of robot pose and map after a movement command.
void	prediction_and_update_pfAuxiliaryPFOptimal (const mrpt::slam::CActionCollection action, const mrpt::slam::CSensoryFrame observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Update the m_particles, predicting the posterior of robot pose and map after a movement command.
	IMPLEMENT_PARTICLE_FILTER_CAPABLE (CPose2D)
void	clear ()
	Free all the memory associated to m_particles, and set the number of parts = 0.
void	copyFrom (const CPosePDF &o)
	Copy operator, translating if necesary (for example, between m_particles and gaussian representations)
void	resetDeterministic (const CPose2D &location, size_t particlesCount=0)
	Reset the PDF to a single point: All m_particles will be set exactly to the supplied pose.
void	resetUniform (const double &x_min, const double &x_max, const double &y_min, const double &y_max, const double &phi_min=-M_PI, const double &phi_max=M_PI, const int &particlesCount=-1)
	Reset the PDF to an uniformly distributed one, inside of the defined cube.
void	getMean (CPose2D &mean_pose) const
	Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF).
virtual void	getMean (TDATA &mean_point) const =0
	Returns the mean, or mathematical expectation of the probability density distribution (PDF).
void	getCovarianceAndMean (CMatrixDouble33 &cov, CPose2D &mean_point) const
	Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.
virtual void	getCovarianceAndMean (CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &cov, TDATA &mean_point) const =0
	Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.
CPose2D	getParticlePose (size_t i) const
	Returns the pose of the i'th particle.
void	saveToTextFile (const std::string &file) const
	Save PDF's m_particles to a text file.
size_t	size () const
	Get the m_particles count (equivalent to "particlesCount")
void	performSubstitution (std::vector< int > &indx)
	Performs the substitution for internal use of resample in particle filter algorithm, don't call it directly.
virtual void	performSubstitution (const std::vector< size_t > &indx)=0
	Performs the substitution for internal use of resample in particle filter algorithm, don't call it directly.
void	changeCoordinatesReference (const CPose3D &newReferenceBase)
	This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which "to project" the current pdf.
void	drawSingleSample (CPose2D &outPart) const
	Draws a single sample from the distribution (WARNING: weights are assumed to be normalized!)
virtual void	drawSingleSample (TDATA &outPart) const =0
	Draws a single sample from the distribution.
void	operator+= (const CPose2D &Ap)
	Appends (pose-composition) a given pose "p" to each particle.
void	append (CPosePDFParticles &o)
	Appends (add to the list) a set of m_particles to the existing ones, and then normalize weights.
void	inverse (CPosePDF &o) const
	Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF.
CPose2D	getMostLikelyParticle () const
	Returns the particle with the highest weight.
void	bayesianFusion (const CPosePDF &p1, const CPosePDF &p2, const double &minMahalanobisDistToDrop=0)
	Bayesian fusion.
double	evaluatePDF_parzen (const double &x, const double &y, const double &phi, const double &stdXY, const double &stdPhi) const
	Evaluates the PDF at a given arbitrary point as reconstructed by a Parzen window.
void	saveParzenPDFToTextFile (const char *fileName, const double &x_min, const double &x_max, const double &y_min, const double &y_max, const double &phi, const double &stepSizeXY, const double &stdXY, const double &stdPhi) const
	Save a text file (compatible with matlab) representing the 2D evaluation of the PDF as reconstructed by a Parzen window.
template<class OPENGL_SETOFOBJECTSPTR >
void	getAs3DObject (OPENGL_SETOFOBJECTSPTR &out_obj) const
	Returns a 3D representation of this PDF.
template<class OPENGL_SETOFOBJECTSPTR >
OPENGL_SETOFOBJECTSPTR	getAs3DObject () const
	Returns a 3D representation of this PDF.
mrpt::utils::CObjectPtr	duplicateGetSmartPtr () const
	Returns a copy of the object, indepently of its class, as a smart pointer (the newly created object will exist as long as any copy of this smart pointer).
CObject *	clone () const
	Cloning interface for smart pointers.
void	getCovarianceDynAndMean (CMatrixDouble &cov, TDATA &mean_point) const
	Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.
TDATA	getMeanVal () const
	Returns the mean, or mathematical expectation of the probability density distribution (PDF).
void	getCovariance (CMatrixDouble &cov) const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
void	getCovariance (CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &cov) const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN >	getCovariance () const
	Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)
virtual void	drawManySamples (size_t N, std::vector< vector_double > &outSamples) const
	Draws a number of samples from the distribution, and saves as a list of 1xSTATE_LEN vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum.
double	getCovarianceEntropy () const
	Compute the entropy of the estimated covariance matrix.
void	clearParticles ()
	Free the memory of all the particles and reset the array "m_particles" to length zero.
void	writeParticlesToStream (utils::CStream &out) const
	Dumps the sequence of particles and their weights to a stream (requires T implementing CSerializable).
void	readParticlesFromStream (utils::CStream &in)
	Reads the sequence of particles and their weights from a stream (requires T implementing CSerializable).
void	getWeights (vector_double &out_logWeights) const
	Returns a vector with the sequence of the logaritmic weights of all the samples.
void	prepareFastDrawSample (const bayes::CParticleFilter::TParticleFilterOptions &PF_options, TParticleProbabilityEvaluator partEvaluator=defaultEvaluator, const void action=NULL, const void observation=NULL) const
	Prepares data structures for calling fastDrawSample method next.
size_t	fastDrawSample (const bayes::CParticleFilter::TParticleFilterOptions &PF_options) const
	Draws a random sample from the particle filter, in such a way that each particle has a probability proportional to its weight (in the standard PF algorithm).
virtual double	getW (size_t i) const =0
	Access to i'th particle (logarithm) weight, where first one is index 0.
virtual void	setW (size_t i, double w)=0
	Modifies i'th particle (logarithm) weight, where first one is index 0.
virtual size_t	particlesCount () const =0
	Get the m_particles count.
void	prediction_and_update (const mrpt::slam::CActionCollection action, const mrpt::slam::CSensoryFrame observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Performs the prediction stage of the Particle Filter.
virtual double	normalizeWeights (double *out_max_log_w=NULL)=0
	Normalize the (logarithmic) weights, such as the maximum weight is zero.
virtual double	ESS ()=0
	Returns the normalized ESS (Estimated Sample Size), in the range [0,1].
void	performResampling (const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Performs a resample of the m_particles, using the method selected in the constructor.
bool	PF_SLAM_implementation_gatherActionsCheckBothActObs (const CActionCollection actions, const CSensoryFrame sf)
	Auxiliary method called by PF implementations: return true if we have both action & observation, otherwise, return false AND accumulate the odometry so when we have an observation we didn't lose a thing.
Virtual methods that the PF_implementations assume exist.
const TPose3D *	getLastPose (const size_t i) const
	Return a pointer to the last robot pose in the i'th particle (or NULL if it's a path and it's empty).
void	PF_SLAM_implementation_custom_update_particle_with_new_pose (CParticleDataContent *particleData, const TPose3D &newPose) const
void	PF_SLAM_implementation_replaceByNewParticleSet (CParticleList &old_particles, const std::vector< TPose3D > &newParticles, const vector_double &newParticlesWeight, const std::vector< size_t > &newParticlesDerivedFromIdx) const
double	PF_SLAM_computeObservationLikelihoodForParticle (const CParticleFilter::TParticleFilterOptions &PF_options, const size_t particleIndexForMap, const CSensoryFrame &observation, const CPose3D &x) const
	Evaluate the observation likelihood for one particle at a given location.
Virtual methods that the PF_implementations assume exist.
virtual void	PF_SLAM_implementation_custom_update_particle_with_new_pose (PARTICLE_TYPE *particleData, const TPose3D &newPose) const =0
virtual void	PF_SLAM_implementation_replaceByNewParticleSet (typename CParticleFilterData< PARTICLE_TYPE >::CParticleList &old_particles, const vector< TPose3D > &newParticles, const vector_double &newParticlesWeight, const vector< size_t > &newParticlesDerivedFromIdx) const
	This is the default algorithm to efficiently replace one old set of samples by another new set.
virtual bool	PF_SLAM_implementation_doWeHaveValidObservations (const typename CParticleFilterData< PARTICLE_TYPE >::CParticleList &particles, const CSensoryFrame *sf) const
virtual bool	PF_SLAM_implementation_skipRobotMovement () const
	Make a specialization if needed, eg.
Static Public Member Functions
static bool	is_3D ()
static bool	is_PDF ()
static double	defaultEvaluator (const bayes::CParticleFilter::TParticleFilterOptions &PF_options, const CParticleFilterCapable obj, size_t index, const void action, const void *observation)
	The default evaluator function, which simply returns the particle weight.
static void	computeResampling (CParticleFilter::TParticleResamplingAlgorithm method, const vector_double &in_logWeights, std::vector< size_t > &out_indexes)
	A static method to perform the computation of the samples resulting from resampling a given set of particles, given their logarithmic weights, and a resampling method.
static void	log2linearWeights (const vector_double &in_logWeights, vector_double &out_linWeights)
	A static method to compute the linear, normalized (the sum the unity) weights from log-weights.
Public Attributes
TMonteCarloLocalizationParams	options
	MCL parameters.
CParticleList	m_particles
	The array of particles.
Static Public Attributes
static const mrpt::utils::TRuntimeClassId	classCObject
static const size_t	state_length
	The length of the variable, for example, 3 for a 3D point, 6 for a 3D pose (x y z yaw pitch roll).
RTTI stuff
static const mrpt::utils::TRuntimeClassId	classCSerializable
Protected Member Functions
virtual void	writeToStream (mrpt::utils::CStream &out, int *getVersion) const =0
	Introduces a pure virtual method responsible for writing to a CStream.
virtual void	readFromStream (mrpt::utils::CStream &in, int version)=0
	Introduces a pure virtual method responsible for loading from a CStream This can not be used directly be users, instead use "stream >> object;" for reading it from a stream or "stream >> object_ptr;" if the class is unknown apriori.
virtual void	prediction_and_update_pfOptimalProposal (const mrpt::slam::CActionCollection action, const mrpt::slam::CSensoryFrame observation, const bayes::CParticleFilter::TParticleFilterOptions &PF_options)
	Performs the particle filter prediction/update stages for the algorithm "pfOptimalProposal" (if not implemented in heritated class, it will raise a 'non-implemented' exception).
The generic PF implementations for localization & SLAM.
void	PF_SLAM_implementation_pfAuxiliaryPFOptimal (const CActionCollection actions, const CSensoryFrame sf, const CParticleFilter::TParticleFilterOptions &PF_options, const TKLDParams &KLD_options)
	A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFOptimal" (optimal sampling with rejection sampling approximation), common to both localization and mapping.
void	PF_SLAM_implementation_pfAuxiliaryPFStandard (const CActionCollection actions, const CSensoryFrame sf, const CParticleFilter::TParticleFilterOptions &PF_options, const TKLDParams &KLD_options)
	A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFStandard" (Auxiliary particle filter with the standard proposal), common to both localization and mapping.
void	PF_SLAM_implementation_pfStandardProposal (const CActionCollection actions, const CSensoryFrame sf, const CParticleFilter::TParticleFilterOptions &PF_options, const TKLDParams &KLD_options)
	A generic implementation of the PF method "pfStandardProposal" (standard proposal distribution, that is, a simple SIS particle filter), common to both localization and mapping.
Protected Attributes
TFastDrawAuxVars	m_fastDrawAuxiliary
	Auxiliary vectors, see CParticleFilterCapable::prepareFastDrawSample for more information.
RTTI stuff
static const mrpt::utils::TRuntimeClassId	classCPosePDF
class	mrpt::utils::CStream
RTTI stuff
virtual const mrpt::utils::TRuntimeClassId *	GetRuntimeClass () const
	Returns information about the class of an object in runtime.
virtual mrpt::utils::CObject *	duplicate () const
	Returns a copy of the object, indepently of its class.
typedef CPosePDFParticlesPtr	SmartPtr
static mrpt::utils::CLASSINIT	_init_CPosePDFParticles
static mrpt::utils::TRuntimeClassId	classCPosePDFParticles
static const mrpt::utils::TRuntimeClassId *	classinfo
static const mrpt::utils::TRuntimeClassId *	_GetBaseClass ()
static mrpt::utils::CObject *	CreateObject ()
static CPosePDFParticlesPtr	Create ()
Data members and methods used by generic PF implementations
static double	PF_SLAM_particlesEvaluator_AuxPFStandard (const CParticleFilter::TParticleFilterOptions &PF_options, const CParticleFilterCapable obj, size_t index, const void action, const void *observation)
	Compute w[i]·p(z_t \| mu_t^i), with mu_t^i being the mean of the new robot pose.
static double	PF_SLAM_particlesEvaluator_AuxPFOptimal (const CParticleFilter::TParticleFilterOptions &PF_options, const CParticleFilterCapable obj, size_t index, const void action, const void *observation)
CActionRobotMovement2D	m_accumRobotMovement2D
bool	m_accumRobotMovement2DIsValid
CPose3DPDFGaussian	m_accumRobotMovement3D
bool	m_accumRobotMovement3DIsValid
CPoseRandomSampler	m_movementDrawer
	Used in al PF implementations.
vector_double	m_pfAuxiliaryPFOptimal_estimatedProb
	Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm.
vector_double	m_pfAuxiliaryPFStandard_estimatedProb
	Auxiliary variable used in the "pfAuxiliaryPFStandard" algorithm.
vector_double	m_pfAuxiliaryPFOptimal_maxLikelihood
	Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm.
std::vector< TPose3D >	m_pfAuxiliaryPFOptimal_maxLikDrawnMovement
	Auxiliary variable used in the "pfAuxiliaryPFOptimal" algorithm.
std::vector< bool >	m_pfAuxiliaryPFOptimal_maxLikMovementDrawHasBeenUsed

Member Typedef Documentation

typedef CProbabilityParticle<T> mrpt::bayes::CParticleFilterData::CParticleData [inherited]

Use this to refer to each element in the m_particles array.

Definition at line 61 of file CParticleFilterData.h.

typedef T mrpt::bayes::CParticleFilterData::CParticleDataContent [inherited]

This is the type inside the corresponding CParticleData class.

Definition at line 60 of file CParticleFilterData.h.

typedef std::deque<CParticleData> mrpt::bayes::CParticleFilterData::CParticleList [inherited]

Use this type to refer to the list of particles m_particles.

Definition at line 62 of file CParticleFilterData.h.

typedef CPosePDFParticlesPtr mrpt::poses::CPosePDFParticles::SmartPtr [inherited]

A typedef for the associated smart pointer

Definition at line 60 of file CPosePDFParticles.h.

typedef double( * mrpt::bayes::CParticleFilterCapable::TParticleProbabilityEvaluator)(const bayes::CParticleFilter::TParticleFilterOptions &PF_options, const CParticleFilterCapable *obj, size_t index, const void *action, const void *observation) [inherited]

A callback function type for evaluating the probability of m_particles of being selected, used in "fastDrawSample".

The default evaluator function "defaultEvaluator" simply returns the particle weight.

Parameters:

index	This is the index of the particle its probability is being computed.
action	The value of this is the parameter passed to "prepareFastDrawSample"
observation	The value of this is the parameter passed to "prepareFastDrawSample" The action and the observation are declared as "void*" for a greater flexibility.

See also:: prepareFastDrawSample

Definition at line 73 of file CParticleFilterCapable.h.

typedef TDATA mrpt::utils::CProbabilityDensityFunction::type_value [inherited]

The type of the state the PDF represents.

Definition at line 54 of file CProbabilityDensityFunction.h.

Member Enumeration Documentation

anonymous enum [inherited]

Enumerator:

is_3D_val

Definition at line 79 of file CPosePDF.h.

anonymous enum [inherited]

Enumerator:

is_PDF_val

Definition at line 81 of file CPosePDF.h.

Constructor & Destructor Documentation

mrpt::slam::CMonteCarloLocalization2D::CMonteCarloLocalization2D ( size_t M = 1 )

Constructor.

Parameters:

M	The number of m_particles.

virtual mrpt::slam::CMonteCarloLocalization2D::~CMonteCarloLocalization2D ( ) [virtual]

Destructor.

Member Function Documentation

static const mrpt::utils::TRuntimeClassId* mrpt::poses::CPosePDFParticles::_GetBaseClass ( ) [static, protected, inherited]

Reimplemented from mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFParticles::append ( CPosePDFParticles & o ) [inherited]

Appends (add to the list) a set of m_particles to the existing ones, and then normalize weights.

void mrpt::poses::CPosePDFParticles::bayesianFusion	(	const CPosePDF &	p1,
		const CPosePDF &	p2,
		const double &	minMahalanobisDistToDrop = `0`
	)		`[virtual, inherited]`

Bayesian fusion.

Implements mrpt::poses::CPosePDF.

void mrpt::poses::CPosePDFParticles::changeCoordinatesReference ( const CPose3D & newReferenceBase ) [virtual, inherited]

This can be used to convert a PDF from local coordinates to global, providing the point (newReferenceBase) from which "to project" the current pdf.

Result PDF substituted the currently stored one in the object.

Implements mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

void mrpt::poses::CPosePDFParticles::clear ( ) [inherited]

Free all the memory associated to m_particles, and set the number of parts = 0.

void mrpt::bayes::CParticleFilterData::clearParticles ( ) [inline, inherited]

Free the memory of all the particles and reset the array "m_particles" to length zero.

Definition at line 72 of file CParticleFilterData.h.

CObject* mrpt::utils::CObject::clone ( ) const [inline, inherited]

Cloning interface for smart pointers.

Reimplemented in mrpt::opengl::CRenderizable, and mrpt::opengl::CRenderizableDisplayList.

Definition at line 154 of file CObject.h.

static void mrpt::bayes::CParticleFilterCapable::computeResampling	(	CParticleFilter::TParticleResamplingAlgorithm	method,
		const vector_double &	in_logWeights,
		std::vector< size_t > &	out_indexes
	)		`[static, inherited]`

A static method to perform the computation of the samples resulting from resampling a given set of particles, given their logarithmic weights, and a resampling method.

It returns the sequence of indexes from the resampling. The number of output samples is the same than the input population. This generic method just computes these indexes, to actually perform a resampling in a particle filter object, call performResampling

See also:: performResampling

void mrpt::poses::CPosePDFParticles::copyFrom ( const CPosePDF & o ) [virtual, inherited]

Copy operator, translating if necesary (for example, between m_particles and gaussian representations)

Implements mrpt::poses::CPosePDF.

static CPosePDFParticlesPtr mrpt::poses::CPosePDFParticles::Create ( ) [static, inherited]

static mrpt::utils::CObject* mrpt::poses::CPosePDFParticles::CreateObject ( ) [static, inherited]

static double mrpt::bayes::CParticleFilterCapable::defaultEvaluator	(	const bayes::CParticleFilter::TParticleFilterOptions &	PF_options,
		const CParticleFilterCapable *	obj,
		size_t	index,
		const void *	action,
		const void *	observation
	)		`[inline, static, inherited]`

The default evaluator function, which simply returns the particle weight.

The action and the observation are declared as "void*" for a greater flexibility.

See also:: prepareFastDrawSample

Definition at line 84 of file CParticleFilterCapable.h.

References MRPT_UNUSED_PARAM, and mrpt::bayes::CParticleFilterCapable::getW().

virtual void mrpt::utils::CProbabilityDensityFunction::drawManySamples	(	size_t	N,
		std::vector< vector_double > &	outSamples
	)		const `[inline, virtual, inherited]`

Draws a number of samples from the distribution, and saves as a list of 1xSTATE_LEN vectors, where each row contains a (x,y,z,yaw,pitch,roll) datum.

This base method just call N times to drawSingleSample, but derived classes should implemented optimized method for each particular PDF.

Reimplemented in mrpt::poses::CPosePDFSOG, mrpt::poses::CPosePDFGaussianInf, mrpt::poses::CPosePDFGaussian, and mrpt::poses::CPosePDFGrid.

Definition at line 126 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::drawSingleSample().

virtual void mrpt::utils::CProbabilityDensityFunction::drawSingleSample ( TDATA & outPart ) const [pure virtual, inherited]

Draws a single sample from the distribution.

void mrpt::poses::CPosePDFParticles::drawSingleSample ( CPose2D & outPart ) const [inherited]

Draws a single sample from the distribution (WARNING: weights are assumed to be normalized!)

virtual mrpt::utils::CObject* mrpt::poses::CPosePDFParticles::duplicate ( ) const [virtual, inherited]

Returns a copy of the object, indepently of its class.

Implements mrpt::utils::CObject.

mrpt::utils::CObjectPtr mrpt::utils::CObject::duplicateGetSmartPtr ( ) const [inline, inherited]

Returns a copy of the object, indepently of its class, as a smart pointer (the newly created object will exist as long as any copy of this smart pointer).

Definition at line 151 of file CObject.h.

virtual double mrpt::bayes::CParticleFilterCapable::ESS ( ) [pure virtual, inherited]

Returns the normalized ESS (Estimated Sample Size), in the range [0,1].

Note that you do NOT need to normalize the weights before calling this.

double mrpt::poses::CPosePDFParticles::evaluatePDF_parzen	(	const double &	x,
		const double &	y,
		const double &	phi,
		const double &	stdXY,
		const double &	stdPhi
	)		const `[inherited]`

Evaluates the PDF at a given arbitrary point as reconstructed by a Parzen window.

See also:: saveParzenPDFToTextFile

size_t mrpt::bayes::CParticleFilterCapable::fastDrawSample ( const bayes::CParticleFilter::TParticleFilterOptions & PF_options ) const [inherited]

Draws a random sample from the particle filter, in such a way that each particle has a probability proportional to its weight (in the standard PF algorithm).

This method can be used to generate a variable number of m_particles when resampling: to vary the number of m_particles in the filter. See prepareFastDrawSample for more information, or the Particle Filter tutorial.

NOTES:

You MUST call "prepareFastDrawSample" ONCE before calling this method. That method must be called after modifying the particle filter (executing one step, resampling, etc...)
This method returns ONE index for the selected ("drawn") particle, in the range [0,M-1]
You do not need to call "normalizeWeights" before calling this.
See also:
prepareFastDrawSample

template<class OPENGL_SETOFOBJECTSPTR >

void mrpt::poses::CPosePDF::getAs3DObject ( OPENGL_SETOFOBJECTSPTR & out_obj ) const [inline, inherited]

Returns a 3D representation of this PDF.

Note:: Needs the mrpt-opengl library, and using mrpt::opengl::CSetOfObjectsPtr as template argument.

Definition at line 88 of file CPosePDF.h.

References mrpt::opengl::posePDF2opengl().

template<class OPENGL_SETOFOBJECTSPTR >

OPENGL_SETOFOBJECTSPTR mrpt::poses::CPosePDF::getAs3DObject ( ) const [inline, inherited]

Returns a 3D representation of this PDF.

Note:: Needs the mrpt-opengl library, and using mrpt::opengl::CSetOfObjectsPtr as template argument.

Definition at line 97 of file CPosePDF.h.

References mrpt::opengl::posePDF2opengl().

void mrpt::utils::CProbabilityDensityFunction::getCovariance ( CMatrixDouble & cov ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean, getCovarianceAndMean

Definition at line 89 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::getCovarianceDynAndMean().

void mrpt::utils::CProbabilityDensityFunction::getCovariance ( CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > & cov ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean, getCovarianceAndMean

Definition at line 98 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::getCovarianceAndMean().

CMatrixFixedNumeric<double,STATE_LEN,STATE_LEN> mrpt::utils::CProbabilityDensityFunction::getCovariance ( ) const [inline, inherited]

Returns the estimate of the covariance matrix (STATE_LEN x STATE_LEN covariance matrix)

See also:: getMean

Definition at line 107 of file CProbabilityDensityFunction.h.

References mrpt::math::cov(), and mrpt::utils::CProbabilityDensityFunction::getCovarianceAndMean().

virtual void mrpt::utils::CProbabilityDensityFunction::getCovarianceAndMean	(	CMatrixFixedNumeric< double, STATE_LEN, STATE_LEN > &	cov,
		TDATA &	mean_point
	)		const `[pure virtual, inherited]`

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:: getMean

void mrpt::poses::CPosePDFParticles::getCovarianceAndMean	(	CMatrixDouble33 &	cov,
		CPose2D &	mean_point
	)		const `[inherited]`

Returns an estimate of the pose covariance matrix (3x3 cov matrix) and the mean, both at once.

See also:: getMean

void mrpt::utils::CProbabilityDensityFunction::getCovarianceDynAndMean	(	CMatrixDouble &	cov,
		TDATA &	mean_point
	)		const `[inline, inherited]`

Returns an estimate of the pose covariance matrix (STATE_LENxSTATE_LEN cov matrix) and the mean, both at once.

See also:: getMean

Definition at line 69 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::getCovarianceAndMean().

double mrpt::utils::CProbabilityDensityFunction::getCovarianceEntropy ( ) const [inline, inherited]

Compute the entropy of the estimated covariance matrix.

See also:: http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Entropy

Definition at line 145 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::getCovariance(), and det().

const TPose3D* mrpt::slam::CMonteCarloLocalization2D::getLastPose ( const size_t i ) const [virtual]

Return a pointer to the last robot pose in the i'th particle (or NULL if it's a path and it's empty).

Implements mrpt::slam::PF_implementation< CPose2D, CMonteCarloLocalization2D >.

virtual void mrpt::utils::CProbabilityDensityFunction::getMean ( TDATA & mean_point ) const [pure virtual, inherited]

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

See also:: getCovarianceAndMean

void mrpt::poses::CPosePDFParticles::getMean ( CPose2D & mean_pose ) const [inherited]

Returns an estimate of the pose, (the mean, or mathematical expectation of the PDF).

See also:: getCovariance

TDATA mrpt::utils::CProbabilityDensityFunction::getMeanVal ( ) const [inline, inherited]

Returns the mean, or mathematical expectation of the probability density distribution (PDF).

See also:: getCovariance

Definition at line 79 of file CProbabilityDensityFunction.h.

References mrpt::utils::CProbabilityDensityFunction::getMean().

CPose2D mrpt::poses::CPosePDFParticles::getMostLikelyParticle ( ) const [inherited]

Returns the particle with the highest weight.

Reimplemented from mrpt::bayes::CParticleFilterData< CPose2D >.

CPose2D mrpt::poses::CPosePDFParticles::getParticlePose ( size_t i ) const [inherited]

Returns the pose of the i'th particle.

virtual const mrpt::utils::TRuntimeClassId* mrpt::poses::CPosePDFParticles::GetRuntimeClass ( ) const [virtual, inherited]

Returns information about the class of an object in runtime.

Reimplemented from mrpt::poses::CPosePDF.

virtual double mrpt::bayes::CParticleFilterCapable::getW ( size_t i ) const [pure virtual, inherited]

Access to i'th particle (logarithm) weight, where first one is index 0.

Referenced by mrpt::bayes::CParticleFilterCapable::defaultEvaluator().

void mrpt::bayes::CParticleFilterData::getWeights ( vector_double & out_logWeights ) const [inline, inherited]

Returns a vector with the sequence of the logaritmic weights of all the samples.

Definition at line 127 of file CParticleFilterData.h.

mrpt::poses::CPosePDFParticles::IMPLEMENT_PARTICLE_FILTER_CAPABLE ( CPose2D ) [inherited]

void mrpt::poses::CPosePDFParticles::inverse ( CPosePDF & o ) const [virtual, inherited]

Returns a new PDF such as: NEW_PDF = (0,0,0) - THIS_PDF.

Implements mrpt::poses::CPosePDF.

static bool mrpt::poses::CPosePDF::is_3D ( ) [inline, static, inherited]

Definition at line 80 of file CPosePDF.h.

static bool mrpt::poses::CPosePDF::is_PDF ( ) [inline, static, inherited]

Definition at line 82 of file CPosePDF.h.

static void mrpt::bayes::CParticleFilterCapable::log2linearWeights	(	const vector_double &	in_logWeights,
		vector_double &	out_linWeights
	)		`[static, inherited]`

A static method to compute the linear, normalized (the sum the unity) weights from log-weights.

See also:: performResampling

virtual double mrpt::bayes::CParticleFilterCapable::normalizeWeights ( double * out_max_log_w = NULL ) [pure virtual, inherited]

Normalize the (logarithmic) weights, such as the maximum weight is zero.

Parameters:

out_max_log_w If provided, will return with the maximum log_w before normalizing, such as new_weights = old_weights - max_log_w.

Returns:: The max/min ratio of weights ("dynamic range")

void mrpt::poses::CPosePDFParticles::operator+= ( const CPose2D & Ap ) [inherited]

Appends (pose-composition) a given pose "p" to each particle.

virtual size_t mrpt::bayes::CParticleFilterCapable::particlesCount ( ) const [pure virtual, inherited]

Get the m_particles count.

void mrpt::bayes::CParticleFilterCapable::performResampling ( const bayes::CParticleFilter::TParticleFilterOptions & PF_options ) [inherited]

Performs a resample of the m_particles, using the method selected in the constructor.

After computing the surviving samples, this method internally calls "performSubstitution" to actually perform the particle replacement. This method is called automatically by CParticleFilter::execute, andshould not be invoked manually normally. To just obtaining the sequence of resampled indexes from a sequence of weights, use "resample"

See also:: resample

void mrpt::poses::CPosePDFParticles::performSubstitution ( std::vector< int > & indx ) [inherited]

Performs the substitution for internal use of resample in particle filter algorithm, don't call it directly.

virtual void mrpt::bayes::CParticleFilterCapable::performSubstitution ( const std::vector< size_t > & indx ) [pure virtual, inherited]

Performs the substitution for internal use of resample in particle filter algorithm, don't call it directly.

Parameters:

indx	The indices of current m_particles to be saved as the new m_particles set.

double mrpt::slam::CMonteCarloLocalization2D::PF_SLAM_computeObservationLikelihoodForParticle	(	const CParticleFilter::TParticleFilterOptions &	PF_options,
		const size_t	particleIndexForMap,
		const CSensoryFrame &	observation,
		const CPose3D &	x
	)		const `[virtual]`

Evaluate the observation likelihood for one particle at a given location.

Implements mrpt::slam::PF_implementation< CPose2D, CMonteCarloLocalization2D >.

void mrpt::slam::CMonteCarloLocalization2D::PF_SLAM_implementation_custom_update_particle_with_new_pose	(	CParticleDataContent *	particleData,
		const TPose3D &	newPose
	)		const

virtual void mrpt::slam::PF_implementation::PF_SLAM_implementation_custom_update_particle_with_new_pose	(	PARTICLE_TYPE *	particleData,
		const TPose3D &	newPose
	)		const `[pure virtual, inherited]`

virtual bool mrpt::slam::PF_implementation::PF_SLAM_implementation_doWeHaveValidObservations	(	const typename CParticleFilterData< PARTICLE_TYPE >::CParticleList &	particles,
		const CSensoryFrame *	sf
	)		const `[inline, virtual, inherited]`

Definition at line 258 of file PF_implementations_data.h.

bool mrpt::slam::PF_implementation::PF_SLAM_implementation_gatherActionsCheckBothActObs	(	const CActionCollection *	actions,
		const CSensoryFrame *	sf
	)		`[inherited]`

Auxiliary method called by PF implementations: return true if we have both action & observation, otherwise, return false AND accumulate the odometry so when we have an observation we didn't lose a thing.

On return=true, the "m_movementDrawer" member is loaded and ready to draw samples of the increment of pose since last step. This method is smart enough to accumulate CActionRobotMovement2D or CActionRobotMovement3D, whatever comes in.

void mrpt::slam::PF_implementation::PF_SLAM_implementation_pfAuxiliaryPFOptimal	(	const CActionCollection *	actions,
		const CSensoryFrame *	sf,
		const CParticleFilter::TParticleFilterOptions &	PF_options,
		const TKLDParams &	KLD_options
	)		`[protected, inherited]`

A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFOptimal" (optimal sampling with rejection sampling approximation), common to both localization and mapping.

BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.

This method implements optimal sampling with a rejection sampling-based approximation of the true posterior. For details, see the papers:

J.-L. Blanco, J. González, and J.-A. Fernández-Madrigal, "An Optimal Filtering Algorithm for Non-Parametric Observation Models in Robot Localization," in Proc. IEEE International Conference on Robotics and Automation (ICRA'08), 2008, pp. 461–466.

BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.

This method implements optimal sampling with a rejection sampling-based approximation of the true posterior. For details, see the papers:

J.-L. Blanco, J. GonzÃ¡lez, and J.-A. FernÃ¡ndez-Madrigal, "An Optimal Filtering Algorithm for Non-Parametric Observation Models in Robot Localization," in Proc. IEEE International Conference on Robotics and Automation (ICRA'08), 2008, pp. 461â€“466.

void mrpt::slam::PF_implementation::PF_SLAM_implementation_pfAuxiliaryPFStandard	(	const CActionCollection *	actions,
		const CSensoryFrame *	sf,
		const CParticleFilter::TParticleFilterOptions &	PF_options,
		const TKLDParams &	KLD_options
	)		`[protected, inherited]`

A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFStandard" (Auxiliary particle filter with the standard proposal), common to both localization and mapping.

BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.

This method is described in the paper: Pitt, M.K.; Shephard, N. (1999). "Filtering Via Simulation: Auxiliary Particle Filters". Journal of the American Statistical Association 94 (446): 590–591. doi:10.2307/2670179.

BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.

This method is described in the paper: Pitt, M.K.; Shephard, N. (1999). "Filtering Via Simulation: Auxiliary Particle Filters". Journal of the American Statistical Association 94 (446): 590â€“591. doi:10.2307/2670179.

void mrpt::slam::PF_implementation::PF_SLAM_implementation_pfStandardProposal	(	const CActionCollection *	actions,
		const CSensoryFrame *	sf,
		const CParticleFilter::TParticleFilterOptions &	PF_options,
		const TKLDParams &	KLD_options
	)		`[protected, inherited]`

A generic implementation of the PF method "pfStandardProposal" (standard proposal distribution, that is, a simple SIS particle filter), common to both localization and mapping.

BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.

void mrpt::slam::CMonteCarloLocalization2D::PF_SLAM_implementation_replaceByNewParticleSet	(	CParticleList &	old_particles,
		const std::vector< TPose3D > &	newParticles,
		const vector_double &	newParticlesWeight,
		const std::vector< size_t > &	newParticlesDerivedFromIdx
	)		const

virtual void mrpt::slam::PF_implementation::PF_SLAM_implementation_replaceByNewParticleSet	(	typename CParticleFilterData< PARTICLE_TYPE >::CParticleList &	old_particles,
		const vector< TPose3D > &	newParticles,
		const vector_double &	newParticlesWeight,
		const vector< size_t > &	newParticlesDerivedFromIdx
	)		const `[inline, virtual, inherited]`

This is the default algorithm to efficiently replace one old set of samples by another new set.

The method uses pointers to make fast copies the first time each particle is duplicated, then makes real copies for the next ones.

Note that more efficient specializations might exist for specific particle data structs.

Definition at line 193 of file PF_implementations_data.h.

References mrpt::utils::delete_safe().

virtual bool mrpt::slam::PF_implementation::PF_SLAM_implementation_skipRobotMovement ( ) const [inline, virtual, inherited]

Make a specialization if needed, eg.

in the first step in SLAM.

Definition at line 266 of file PF_implementations_data.h.

static double mrpt::slam::PF_implementation::PF_SLAM_particlesEvaluator_AuxPFOptimal	(	const CParticleFilter::TParticleFilterOptions &	PF_options,
		const CParticleFilterCapable *	obj,
		size_t	index,
		const void *	action,
		const void *	observation
	)		`[static, protected, inherited]`

static double mrpt::slam::PF_implementation::PF_SLAM_particlesEvaluator_AuxPFStandard	(	const CParticleFilter::TParticleFilterOptions &	PF_options,
		const CParticleFilterCapable *	obj,
		size_t	index,
		const void *	action,
		const void *	observation
	)		`[static, protected, inherited]`

Compute w[i]·p(z_t | mu_t^i), with mu_t^i being the mean of the new robot pose.

Compute w[i]Â·p(z_t | mu_t^i), with mu_t^i being the mean of the new robot pose.

Parameters:

action	MUST be a "const CPose3D*"
observation	MUST be a "const CSensoryFrame*"

void mrpt::bayes::CParticleFilterCapable::prediction_and_update	(	const mrpt::slam::CActionCollection *	action,
		const mrpt::slam::CSensoryFrame *	observation,
		const bayes::CParticleFilter::TParticleFilterOptions &	PF_options
	)		`[inherited]`

Performs the prediction stage of the Particle Filter.

This method simply selects the appropiate protected method according to the particle filter algorithm to run.

See also:: prediction_and_update_pfStandardProposal,prediction_and_update_pfAuxiliaryPFStandard,prediction_and_update_pfOptimalProposal,prediction_and_update_pfAuxiliaryPFOptimal

void mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfAuxiliaryPFOptimal	(	const mrpt::slam::CActionCollection *	action,
		const mrpt::slam::CSensoryFrame *	observation,
		const bayes::CParticleFilter::TParticleFilterOptions &	PF_options
	)		`[virtual]`

Update the m_particles, predicting the posterior of robot pose and map after a movement command.

This method has additional configuration parameters in "options". Performs the update stage of the RBPF, using the sensed CSensoryFrame:

Parameters:

Action	This is a pointer to CActionCollection, containing the pose change the robot has been commanded.
observation	This must be a pointer to a CSensoryFrame object, with robot sensed observations.

See also:: options

Reimplemented from mrpt::bayes::CParticleFilterCapable.

void mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfAuxiliaryPFStandard	(	const mrpt::slam::CActionCollection *	action,
		const mrpt::slam::CSensoryFrame *	observation,
		const bayes::CParticleFilter::TParticleFilterOptions &	PF_options
	)		`[virtual]`

Update the m_particles, predicting the posterior of robot pose and map after a movement command.

This method has additional configuration parameters in "options". Performs the update stage of the RBPF, using the sensed CSensoryFrame:

Parameters:

Action	This is a pointer to CActionCollection, containing the pose change the robot has been commanded.
observation	This must be a pointer to a CSensoryFrame object, with robot sensed observations.

See also:: options

Reimplemented from mrpt::bayes::CParticleFilterCapable.

virtual void mrpt::bayes::CParticleFilterCapable::prediction_and_update_pfOptimalProposal	(	const mrpt::slam::CActionCollection *	action,
		const mrpt::slam::CSensoryFrame *	observation,
		const bayes::CParticleFilter::TParticleFilterOptions &	PF_options
	)		`[protected, virtual, inherited]`

Performs the particle filter prediction/update stages for the algorithm "pfOptimalProposal" (if not implemented in heritated class, it will raise a 'non-implemented' exception).

See also:: prediction_and_update

Reimplemented in mrpt::hmtslam::CLocalMetricHypothesis, and mrpt::slam::CMultiMetricMapPDF.

void mrpt::slam::CMonteCarloLocalization2D::prediction_and_update_pfStandardProposal	(	const mrpt::slam::CActionCollection *	action,
		const mrpt::slam::CSensoryFrame *	observation,
		const bayes::CParticleFilter::TParticleFilterOptions &	PF_options
	)		`[virtual]`

Update the m_particles, predicting the posterior of robot pose and map after a movement command.

This method has additional configuration parameters in "options". Performs the update stage of the RBPF, using the sensed CSensoryFrame:

Parameters:

action	This is a pointer to CActionCollection, containing the pose change the robot has been commanded.
observation	This must be a pointer to a CSensoryFrame object, with robot sensed observations.

See also:: options

Reimplemented from mrpt::bayes::CParticleFilterCapable.

void mrpt::bayes::CParticleFilterCapable::prepareFastDrawSample	(	const bayes::CParticleFilter::TParticleFilterOptions &	PF_options,
		TParticleProbabilityEvaluator	partEvaluator = `defaultEvaluator`,
		const void *	action = `NULL`,
		const void *	observation = `NULL`
	)		const `[inherited]`

Prepares data structures for calling fastDrawSample method next.

This method must be called once before using "fastDrawSample" (calling this more than once has no effect, but it takes time for nothing!) The behavior depends on the configuration of the PF (see CParticleFilter::TParticleFilterOptions):

DYNAMIC SAMPLE SIZE=NO: In this case this method fills out an internal array (m_fastDrawAuxiliary.alreadyDrawnIndexes) with the random indexes generated according to the selected resample scheme in TParticleFilterOptions. Those indexes are read sequentially by subsequent calls to fastDrawSample.
DYNAMIC SAMPLE SIZE=YES: Then:
- If TParticleFilterOptions.resamplingMethod = prMultinomial, the internal buffers will be filled out (m_fastDrawAuxiliary.CDF, CDF_indexes & PDF) and then fastDrawSample can be called an arbitrary number of times to generate random indexes.
- For the rest of resampling algorithms, an exception will be raised since they are not appropriate for a dynamic (unknown in advance) number of particles.

The function pointed by "partEvaluator" should take into account the particle filter algorithm selected in "m_PFAlgorithm". If called without arguments (defaultEvaluator), the default behavior is to draw samples with a probability proportional to their current weights. The action and the observation are declared as "void*" for a greater flexibility. For a more detailed information see the Particle Filter tutorial. Custom supplied "partEvaluator" functions must take into account the previous particle weight, i.e. multiplying the current observation likelihood by the weights.

See also:: fastDrawSample

Referenced by mrpt::slam::PF_implementation::PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal().

virtual void mrpt::utils::CSerializable::readFromStream	(	mrpt::utils::CStream &	in,
		int	version
	)		`[protected, pure virtual, inherited]`

Introduces a pure virtual method responsible for loading from a CStream This can not be used directly be users, instead use "stream >> object;" for reading it from a stream or "stream >> object_ptr;" if the class is unknown apriori.

Parameters:

in	The input binary stream where the object data must read from.
version	The version of the object stored in the stream: use this version number in your code to know how to read the incoming data.

Exceptions:

std::exception On any error, see CStream::ReadBuffer

See also:: CStream

Implemented in mrpt::math::CMatrixD, and mrpt::math::CMatrix.

void mrpt::bayes::CParticleFilterData::readParticlesFromStream ( utils::CStream & in ) [inline, inherited]

Reads the sequence of particles and their weights from a stream (requires T implementing CSerializable).

See also:: writeParticlesToStream

Definition at line 107 of file CParticleFilterData.h.

void mrpt::poses::CPosePDFParticles::resetDeterministic	(	const CPose2D &	location,
		size_t	particlesCount = `0`
	)		`[inherited]`

Reset the PDF to a single point: All m_particles will be set exactly to the supplied pose.

Parameters:

location	The location to set all the m_particles.
particlesCount	If this is set to 0 the number of m_particles remains unchanged.

See also:: resetUniform, resetUniformFreeSpace

void mrpt::poses::CPosePDFParticles::resetUniform	(	const double &	x_min,
		const double &	x_max,
		const double &	y_min,
		const double &	y_max,
		const double &	phi_min = `-M_PI`,
		const double &	phi_max = `M_PI`,
		const int &	particlesCount = `-1`
	)		`[inherited]`

Reset the PDF to an uniformly distributed one, inside of the defined cube.

If particlesCount is set to -1 the number of m_particles remains unchanged.

See also:: resetDeterministic, resetUniformFreeSpace

void mrpt::slam::CMonteCarloLocalization2D::resetUniformFreeSpace	(	COccupancyGridMap2D *	theMap,
		const double	freeCellsThreshold = `0.7`,
		const int	particlesCount = `-1`,
		const double	x_min = `-1e10f`,
		const double	x_max = `1e10f`,
		const double	y_min = `-1e10f`,
		const double	y_max = `1e10f`,
		const double	phi_min = `-M_PI`,
		const double	phi_max = `M_PI`
	)

Reset the PDF to an uniformly distributed one, but only in the free-space of a given 2D occupancy-grid-map.

Orientation is randomly generated in the whole 2*PI range.

Parameters:

theMap	The occupancy grid map
freeCellsThreshold	The minimum free-probability to consider a cell as empty (default is 0.7)
particlesCount	If set to -1 the number of m_particles remains unchanged.
x_min	The limits of the area to look for free cells.
x_max	The limits of the area to look for free cells.
y_min	The limits of the area to look for free cells.
y_max	The limits of the area to look for free cells.
phi_min	The limits of the area to look for free cells.
phi_max	The limits of the area to look for free cells.

See also:: resetDeterm32inistic

Exceptions:

std::exception On any error (no free cell found in map, map=NULL, etc...)

void mrpt::poses::CPosePDFParticles::saveParzenPDFToTextFile	(	const char *	fileName,
		const double &	x_min,
		const double &	x_max,
		const double &	y_min,
		const double &	y_max,
		const double &	phi,
		const double &	stepSizeXY,
		const double &	stdXY,
		const double &	stdPhi
	)		const `[inherited]`

Save a text file (compatible with matlab) representing the 2D evaluation of the PDF as reconstructed by a Parzen window.

See also:: evaluatePDF_parzen

void mrpt::poses::CPosePDFParticles::saveToTextFile ( const std::string & file ) const [virtual, inherited]

Save PDF's m_particles to a text file.

In each line it will go: "x y phi weight"

Implements mrpt::utils::CProbabilityDensityFunction< CPose2D, 3 >.

virtual void mrpt::bayes::CParticleFilterCapable::setW	(	size_t	i,
		double	w
	)		`[pure virtual, inherited]`

Modifies i'th particle (logarithm) weight, where first one is index 0.

size_t mrpt::poses::CPosePDFParticles::size ( ) const [inline, inherited]

Get the m_particles count (equivalent to "particlesCount")

Definition at line 132 of file CPosePDFParticles.h.

void mrpt::bayes::CParticleFilterData::writeParticlesToStream ( utils::CStream & out ) const [inline, inherited]

Dumps the sequence of particles and their weights to a stream (requires T implementing CSerializable).

See also:: readParticlesFromStream

Definition at line 93 of file CParticleFilterData.h.

virtual void mrpt::utils::CSerializable::writeToStream	(	mrpt::utils::CStream &	out,
		int *	getVersion
	)		const `[protected, pure virtual, inherited]`

Introduces a pure virtual method responsible for writing to a CStream.

This can not be used directly be users, instead use "stream << object;" for writing it to a stream.

Parameters:

out	The output binary stream where object must be dumped.
getVersion	If NULL, the object must be dumped. If not, only the version of the object dump must be returned in this pointer. This enables the versioning of objects dumping and backward compatibility with previously stored data.

Exceptions:

std::exception On any error, see CStream::WriteBuffer