ndt_cell.h

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#ifndef NDT_CELL_HH
#define NDT_CELL_HH

#include <spatial_index.h>
#include <cell.h>
#include<impl/EventCounterData.hpp>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <vector>
#include <cstdio>
#include <Eigen/Eigen>

#include <fstream>

#define CELL_UPDATE_MODE_COVARIANCE_INTERSECTION                0

#define CELL_UPDATE_MODE_SAMPLE_VARIANCE                                1

#define CELL_UPDATE_MODE_ERROR_REFINEMENT                               2

#define CELL_UPDATE_MODE_SAMPLE_VARIANCE_SURFACE_ESTIMATION 3

#define CELL_UPDATE_MODE_STUDENT_T 4

#define REFACTORED
//#define ICRA_2013_NDT_OM_SIMPLE_MODE 
namespace lslgeneric
{

template<typename PointT>
class NDTCell : public Cell<PointT>
{
public:
    bool hasGaussian_;  
    double cost;                                
    char isEmpty;                               
    double consistency_score;
                enum CellClass {HORIZONTAL=0, VERTICAL, INCLINED, ROUGH, UNKNOWN};
    std::vector<PointT> points_; 
                
    NDTCell()
    {
        hasGaussian_ = false;
        if(!parametersSet_)
        {
            setParameters();
        }
        N = 0;
        R = 0;
        G = 0;
        B = 0;
        occ = 0;
        emptyval = 0;
        isEmpty=0;
        emptylik = 0;
        emptydist = 0;
        max_occu_ = 1;
        consistency_score=0;
        cost=INT_MAX;
    }

    virtual ~NDTCell()
    {
        points_.clear();
    }

    NDTCell(PointT &center, double &xsize, double &ysize, double &zsize):
        Cell<PointT>(center,xsize,ysize,zsize)
    {
        hasGaussian_ = false;
        N = 0;
        R = 0;
        G = 0;
        B = 0;
        occ = 0;
        emptyval = 0;
        isEmpty = 0;
        emptylik = 0;
        emptydist = 0;
        if(!parametersSet_)
        {
            setParameters();
        }
        consistency_score=0;
        cost=INT_MAX;
    }

    NDTCell(const NDTCell& other):Cell<PointT>()
    {
        this->center_ = other.center_;
        this->xsize_ = other.xsize_;
        this->ysize_ = other.ysize_;
        this->zsize_ = other.zsize_;
        this->hasGaussian_ = other.hasGaussian_;
        this->R = other.R;
        this->G = other.G;
        this->B = other.B;
        this->N = other.N;
        this->occ = other.occ;
        this->emptyval = other.emptyval;
        this->edata = other.edata;
        this->consistency_score=other.consistency_score;
        this->isEmpty = other.isEmpty;
        if(this->hasGaussian_) { 
            this->setMean(other.getMean());
            this->setCov(other.getCov());
        }
        this->cost=other.cost;
    }

    virtual Cell<PointT>* clone() const;
    virtual Cell<PointT>* copy() const;

    inline void updateSampleVariance(const Eigen::Matrix3d &cov2,const Eigen::Vector3d &m2, unsigned int numpointsindistribution, 
                                                                                                                                                        bool updateOccupancyFlag=true,  float max_occu=1024, unsigned int maxnumpoints=1e9);

    inline void computeGaussian(int mode=CELL_UPDATE_MODE_SAMPLE_VARIANCE, unsigned int maxnumpoints=1e9, float occupancy_limit=255, Eigen::Vector3d origin = Eigen::Vector3d(0,0,0), double sensor_noise=0.1);
                
                void computeGaussianSimple();
    inline void updateColorInformation();


    void rescaleCovariance();
    bool rescaleCovariance(Eigen::Matrix3d &cov, Eigen::Matrix3d &invCov);

    //void updateObservation();
    void classify();

    void writeToVRML(FILE *fout, Eigen::Vector3d col = Eigen::Vector3d(0,0,0));
    int writeToJFF(FILE * jffout);
    int loadFromJFF(FILE * jffin);

    inline CellClass getClass() const
    {
        return cl_;
    }
    inline Eigen::Matrix3d getCov() const
    {
        return cov_;
    }
    inline Eigen::Matrix3d getInverseCov() const
    {
        return icov_;
    }
    inline Eigen::Vector3d getMean() const
    {
        return mean_;
    }
    inline Eigen::Matrix3d getEvecs() const
    {
        return evecs_;
    }
    inline Eigen::Vector3d getEvals() const
    {
        return evals_;
    }


    void setCov(const Eigen::Matrix3d &cov);

    inline void setMean(const Eigen::Vector3d &mean)
    {
        mean_ = mean;
    }
    inline void setEvals(const Eigen::Vector3d &ev)
    {
        evals_ = ev;
    }



    static void setParameters(double _EVAL_ROUGH_THR   =0.1,
                              double _EVEC_INCLINED_THR=8*M_PI/18,
                              double _EVAL_FACTOR      =100);
    double getLikelihood(const PointT &pt) const;

    virtual void addPoint(PointT &pt)
    {
        points_.push_back(pt);
    }


    virtual void addPoints(pcl::PointCloud<PointT> &pt)
    {
        points_.insert(points_.begin(),pt.points.begin(),pt.points.end());
    }

    void setRGB(float r, float g, float b)
    {
        R=r;
        G = g;
        B = b;
    }
    void getRGB(float &r, float &g, float &b)
    {
        r = R;
        g = G;
        b = B;
    }

    void updateOccupancy(float occ_val, float max_occu=255.0)
    {
        occ+=occ_val;
        if(occ>max_occu) occ = max_occu;
        if(occ<-max_occu) occ = -max_occu;
        max_occu_= max_occu;
    }

    float getOccupancy()
    {
        return occ;
    }
    float getOccupancyRescaled()
    {
        float occupancy = 1 - 1/(1+exp(occ));
        return occupancy > 1 ? 1 : (occupancy < 0 ? 0 : occupancy);
    }

    void updateEmpty(double elik, double dist)
    {
        emptyval++;
        emptylik+=elik;
        emptydist+=dist;
    }

    float getDynamicLikelihood(unsigned int N)
    {
        return edata.computeSemiStaticLikelihood(N);
    }
    void setOccupancy(float occ_)
    {
        occ = occ_;
    }
    void setEmptyval(int emptyval_)
    {
        emptyval=emptyval_;
    }
    void setEventData(TEventData _ed)
    {
        edata = _ed;
    }
    int getEmptyval()
    {
        return emptyval;
    }
    TEventData getEventData()
    {
        return edata;
    }
    void setN(int N_)
    {
        N = N_;
    }
    int getN()
    {
        return N;
    }
    inline double computeMaximumLikelihoodAlongLine(const PointT &p1,const PointT &p2, Eigen::Vector3d &out);

private:
    Eigen::Matrix3d cov_;               
    Eigen::Matrix3d icov_;  
    Eigen::Matrix3d evecs_; 
    Eigen::Vector3d mean_;  
    Eigen::Vector3d evals_; 
    CellClass cl_;
    static bool parametersSet_;                                                                                                 // ???
    static double EVAL_ROUGH_THR;               // = 0.1;                                                               // ???
    static double EVEC_INCLINED_THR;    // = cos(8*M_PI/18);//10 degree slope;  // ???
    static double EVAL_FACTOR;                                                                                                  // ???
    double d1_,d2_;
    unsigned int N;     
    int emptyval;                       
    double emptylik;
    double emptydist;
    float R,G,B;                        
    float occ;                          
    float max_occu_;
    TEventData edata;

                inline void studentT();
                
                double squareSum(const Eigen::Matrix3d &C,const Eigen::Vector3d &x){
                        double sum;
                        sum = C(0,0)*x(0)*x(0) + C(1,1)*x(1)*x(1) + C(2,2)*x(2)*x(2);
                        sum += 2.0*C(0,1)*x(0)*x(1) + 2.0*C(0,2)*x(0)*x(2) + 2.0*C(1,2)*x(1)*x(2);
                        return sum;
                }


                void writeJFFMatrix(FILE * jffout, Eigen::Matrix3d &mat);
                void writeJFFVector(FILE * jffout, Eigen::Vector3d &vec);
                void writeJFFEventData(FILE * jffout, TEventData &evdata);
                int loadJFFMatrix(FILE * jffin, Eigen::Matrix3d &mat);
                int loadJFFVector(FILE * jffin, Eigen::Vector3d &vec);
                int loadJFFEventData(FILE * jffin, TEventData &evdata);

public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW

};
};

#include<impl/ndt_cell.hpp>

#endif