stat.h

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#ifndef STAT_H
#define STAT_H
#include "point.h"
#include <vector>
#include "gvalues.h"

namespace GMapping {

double sampleGaussian(double sigma,unsigned long int S=0);

double evalGaussian(double sigmaSquare, double delta);
double evalLogGaussian(double sigmaSquare, double delta);
int sampleUniformInt(int max);
double sampleUniformDouble(double min, double max);

struct Covariance3{
        Covariance3 operator + (const Covariance3 & cov) const;
        static Covariance3 zero;
        double xx, yy, tt, xy, xt, yt;
};

struct EigenCovariance3{
        EigenCovariance3();
        EigenCovariance3(const Covariance3& c);
        EigenCovariance3 rotate(double angle) const;
        OrientedPoint sample() const;
        double eval[3];
        double evec[3][3];
};

struct Gaussian3{
        OrientedPoint mean;
        EigenCovariance3 covariance;
        Covariance3 cov;
        double eval(const OrientedPoint& p) const;
        void computeFromSamples(const std::vector<OrientedPoint> & poses);
        void computeFromSamples(const std::vector<OrientedPoint> & poses, const std::vector<double>& weights );
};

template<typename PointIterator, typename WeightIterator>
Gaussian3 computeGaussianFromSamples(PointIterator& pointBegin, PointIterator& pointEnd, WeightIterator& weightBegin, WeightIterator& weightEnd){
        Gaussian3 gaussian;
        OrientedPoint mean=OrientedPoint(0,0,0);
        double wcum=0;
        double s=0, c=0;
        WeightIterator wt=weightBegin;
        double *w=new double();
        OrientedPoint *p=new OrientedPoint();
        for (PointIterator pt=pointBegin; pt!=pointEnd; pt++){
                *w=*wt;
                *p=*pt;
                s+=*w*sin(p->theta);
                c+=*w*cos(p->theta);
                mean.x+=*w*p->x;
                mean.y+=*w*p->y;
                wcum+=*w;
                wt++;
        }
        mean.x/=wcum;
        mean.y/=wcum;
        s/=wcum;
        c/=wcum;
        mean.theta=atan2(s,c);

        Covariance3 cov=Covariance3::zero;
        wt=weightBegin;
        for (PointIterator pt=pointBegin; pt!=pointEnd; pt++){
                *w=*wt;
                *p=*pt;
                OrientedPoint delta=(*p)-mean;
                delta.theta=atan2(sin(delta.theta),cos(delta.theta));
                cov.xx+=*w*delta.x*delta.x;
                cov.yy+=*w*delta.y*delta.y;
                cov.tt+=*w*delta.theta*delta.theta;
                cov.xy+=*w*delta.x*delta.y;
                cov.yt+=*w*delta.y*delta.theta;
                cov.xt+=*w*delta.x*delta.theta;
                wt++;
        }
        cov.xx/=wcum;
        cov.yy/=wcum;
        cov.tt/=wcum;
        cov.xy/=wcum;
        cov.yt/=wcum;
        cov.xt/=wcum;
        EigenCovariance3 ecov(cov);
        gaussian.mean=mean;
        gaussian.covariance=ecov;
        gaussian.cov=cov;
        delete w;
        delete p;
        return gaussian;
}

template<typename PointIterator>
Gaussian3 computeGaussianFromSamples(PointIterator& pointBegin, PointIterator& pointEnd){
        Gaussian3 gaussian;
        OrientedPoint mean=OrientedPoint(0,0,0);
        double wcum=1;
        double s=0, c=0;
        OrientedPoint *p=new OrientedPoint();
        for (PointIterator pt=pointBegin; pt!=pointEnd; pt++){
                *p=*pt;
                s+=sin(p->theta);
                c+=cos(p->theta);
                mean.x+=p->x;
                mean.y+=p->y;
                wcum+=1.;
        }
        mean.x/=wcum;
        mean.y/=wcum;
        s/=wcum;
        c/=wcum;
        mean.theta=atan2(s,c);

        Covariance3 cov=Covariance3::zero;
        for (PointIterator pt=pointBegin; pt!=pointEnd; pt++){
                *p=*pt;
                OrientedPoint delta=(*p)-mean;
                delta.theta=atan2(sin(delta.theta),cos(delta.theta));
                cov.xx+=delta.x*delta.x;
                cov.yy+=delta.y*delta.y;
                cov.tt+=delta.theta*delta.theta;
                cov.xy+=delta.x*delta.y;
                cov.yt+=delta.y*delta.theta;
                cov.xt+=delta.x*delta.theta;
        }
        cov.xx/=wcum;
        cov.yy/=wcum;
        cov.tt/=wcum;
        cov.xy/=wcum;
        cov.yt/=wcum;
        cov.xt/=wcum;
        EigenCovariance3 ecov(cov);
        gaussian.mean=mean;
        gaussian.covariance=ecov;
        gaussian.cov=cov;
        delete p;
        return gaussian;
}


}; //end namespace
#endif

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gmapping
Author(s): Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard; ROS wrapper by Brian Gerkey
autogenerated on Fri Jan 11 09:32:07 2013