class mrpt::poses::CPosePDFParticles¶
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 is also the base for the implementation of Monte-Carlo Localization (MCL), in mrpt::slam::CMonteCarloLocalization2D.
See the application “app/pf-localization” for an example of usage.
See also:
CPose2D, CPosePDF, CPoseGaussianPDF, CParticleFilterCapable
#include <mrpt/poses/CPosePDFParticles.h> class CPosePDFParticles: public mrpt::poses::CPosePDF, public mrpt::bayes::CParticleFilterData, public mrpt::bayes::CParticleFilterDataImpl { public: // fields static const particle_storage_mode PARTICLE_STORAGE; // construction CPosePDFParticles(size_t M = 1); // methods void getMean(CPose2D& mean_pose) const; }; // direct descendants class CMonteCarloLocalization2D;
Inherited Members¶
public: // typedefs typedef CProbabilityDensityFunction<TDATA, STATE_LEN> self_t; // structs struct TFastDrawAuxVars; // methods virtual void copyFrom(const CPosePDF& o) = 0; virtual void bayesianFusion(const CPosePDF& p1, const CPosePDF& p2, const double minMahalanobisDistToDrop = 0) = 0; virtual void inverse(CPosePDF& o) const = 0; virtual void changeCoordinatesReference(const CPose3D& newReferenceBase) = 0; virtual double getW(size_t i) const = 0; virtual void setW(size_t i, double w) = 0; virtual size_t particlesCount() const = 0; virtual void performSubstitution(const std::vector<size_t>& indx) = 0; virtual double normalizeWeights(double* out_max_log_w = nullptr) = 0; virtual double ESS() const = 0;