motionModel.cpp

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#include <mtt/motionModel.h>

MotionModel::MotionModel()
{
        _x = Vector5d::Zero();
        _xp = Vector5d::Zero();
        _z = Vector3d::Zero();
        _P = Matrix5d::Zero();
}

void MotionModel::initEstimator(Vector5d& x,Matrix5d& P)
{       
        //Pre existing estimator
        _estimator.InitFilter(x,P);
}
                
void MotionModel::initEstimator(Vector3d& z)
{
        //New estimator
        
        Vector5d x_init;
        
        x_init(0)=z(0);//x
        x_init(1)=z(1);//y
        x_init(2)=0;//v
        x_init(3)=z(2);//t
        x_init(4)=0;//f
        
        _x=x_init;
        _xp=_x;
        _z=z;
        
        _estimator.InitFilter(x_init);
        
        kMatrix P = _estimator.calculateP();
        //Convert from ekfilter to eigen format
        _P(0,0)=P(0,0);
        _P(0,1)=P(0,1);
        _P(0,2)=P(0,2);
        _P(0,3)=P(0,3);
        _P(0,4)=P(0,4);
        
        _P(1,0)=P(1,0);
        _P(1,1)=P(1,1);
        _P(1,2)=P(1,2);
        _P(1,3)=P(1,3);
        _P(1,4)=P(1,4);
        
        _P(2,0)=P(2,0);
        _P(2,1)=P(2,1);
        _P(2,2)=P(2,2);
        _P(2,3)=P(2,3);
        _P(2,4)=P(2,4);
        
        _P(3,0)=P(3,0);
        _P(3,1)=P(3,1);
        _P(3,2)=P(3,2);
        _P(3,3)=P(3,3);
        _P(3,4)=P(3,4);
        
        _P(4,0)=P(4,0);
        _P(4,1)=P(4,1);
        _P(4,2)=P(4,2);
        _P(4,3)=P(4,3);
        _P(4,4)=P(4,4);
}

void MotionModel::stepEstimator(Vector3d z)
{
        kVector u(0);
        
        _estimator.z_measured=z;//Copy to estimator
        _z=z;
        
        _estimator.inovation_error=_estimator.z_measured-_estimator.x_predicted.head(3);
        
        //Convert from eigen format to ekfilter
        kVector z_(3);
        z_(0)=z(0);
        z_(1)=z(1);
        z_(2)=z(2);
        
        //Makes one prediction-correction step
        _estimator.step(u,z_);
        
//      cout<<"Xa: "<<_estimator.getX()<<endl;
//      cout<<"Pa: "<<_estimator.calculateP()<<endl;
        
//      cout<<"Z: "<<z<<endl;
        
        //Returns the corrected state (a posteriori state estimate)
        kVector x_(5);
        x_ = _estimator.getX();
        
        //Convert from ekfilter to eigen format
        _x(0)=x_(0);
        _x(1)=x_(1);
        _x(2)=x_(2);
        _x(3)=x_(3);
        _x(4)=x_(4);
        
//      cout<<"_X: "<<_x<<endl;
        
        //Returns the a posteriori error covariance estimate matrix
        kMatrix P=_estimator.calculateP();
        
        //Convert from ekfilter to eigen format
        _P(0,0)=P(0,0);
        _P(0,1)=P(0,1);
        _P(0,2)=P(0,2);
        _P(0,3)=P(0,3);
        _P(0,4)=P(0,4);
        
        _P(1,0)=P(1,0);
        _P(1,1)=P(1,1);
        _P(1,2)=P(1,2);
        _P(1,3)=P(1,3);
        _P(1,4)=P(1,4);
        
        _P(2,0)=P(2,0);
        _P(2,1)=P(2,1);
        _P(2,2)=P(2,2);
        _P(2,3)=P(2,3);
        _P(2,4)=P(2,4);
        
        _P(3,0)=P(3,0);
        _P(3,1)=P(3,1);
        _P(3,2)=P(3,2);
        _P(3,3)=P(3,3);
        _P(3,4)=P(3,4);

        _P(4,0)=P(4,0);
        _P(4,1)=P(4,1);
        _P(4,2)=P(4,2);
        _P(4,3)=P(4,3);
        _P(4,4)=P(4,4);
        
//      cout<<"P:"<<_P<<endl;
        
        //Returns the predicted state vector (a priori state estimate)
        kVector xp=_estimator.predict(u);
        
        //Convert from ekfilter to eigen format
        _xp(0)=xp(0);
        _xp(1)=xp(1);
        _xp(2)=xp(2);
        _xp(3)=xp(3);
        _xp(4)=xp(4);
        
//      cout<<"h123"<<endl;
//      cout<<"xP:"<<_xp<<endl;
//      assert(is_finite(_xp));
//      assert(is_finite(_P));

        _estimator.x_predicted=_xp;//Copy to estimator
}