FindHomography algorithm : some doubt

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Hi,

I have some doubt about FindHomography algorithm. I wrote a program to test it. In this program I have rotated an image look for descriptors in original image and rotated image. After matching I use findHomography to retrieve transformation and calculate square error for RANSAC, LMEDS and RHO method. I wrote algorithm for Levenberg-Marquardt algorithm (using Numerical Recipes ). I can add some noise to point location. NR algorithm is best without noise. Problem is when noise increase. NR is always best and other algorithms (RANSAC LMEDS and RHO) become completly wrong. I fit only six parameters using NR. I think it is like in findHomography.

May be something is wrong in my program. I don't think so. Everybody can check my code here. If you want to check with NR you can download full code on github

#include <opencv2/opencv.hpp> 
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>

using namespace cv;
using namespace std;

double SquareError(Mat h, vector<Point2d> src,vector<Point2d> dst)
{
    double e=0;
    for (int i = 0; i < src.size(); i++)
    {
        double x = src[i].x*h.at<double>(0,0)+src[i].y*h.at<double>(0,1)+h.at<double>(0,2);
        double y = src[i].x*h.at<double>(1,0)+src[i].y*h.at<double>(1,1)+h.at<double>(1,2);
        e += (x-dst[i].x)*(x-dst[i].x)+(y-dst[i].y)*(y-dst[i].y);
    }

return e;
}

#ifdef GITHUBCODE
vector<double> AjusteHomography(vector<Point2d> x,vector <Point2d> y,vector<double> paramIni);
#endif



vector<KeyPoint> kpts1,kpts2;
vector<vector<DMatch> > couple;
Mat m1;
float maxDist ;
float minDist;
const char* window_name = "Original";
int noiseLevel=0;
static void NoisAmpl(int, void*)
{
    vector<Point2d> src,dst;
    for (int i = 1; i < couple.size(); i++)
        if (couple[i][0].distance < (maxDist + minDist) / 10)
        {
            Point2f p(rand()/double(RAND_MAX)-0.5,rand()/double(RAND_MAX)-0.5);
            src.push_back(kpts1[couple[i][0].queryIdx].pt+noiseLevel/2.0*p);
            dst.push_back(kpts2[couple[i][0].trainIdx].pt);
        }
    cout << "# matches " << src.size() << "\n";
    Mat h=findHomography(src,dst,CV_RANSAC);

    cout<<"RANSAC ="<<h<<endl;
    cout << "Error RANSAC =" << SquareError(h,src,dst)<<endl;
    h=findHomography(src,dst,CV_LMEDS);
    cout<<"LMEDS="<<h<<endl;
    cout << "Error LMEDS =" << SquareError(h,src,dst)<<endl;
    h=findHomography(src,dst,RHO);
    cout<<"RHO="<<h<<endl;
    cout << "Error RHO =" << SquareError(h,src,dst)<<endl;
    Mat m3;
#ifdef GITHUBCODE
    vector<double> paramIni = {0,0,0,0,0,0};
    vector<double> hh=AjusteHomography(src,dst,paramIni);
    for (int i = 0; i<hh.size();i++)
        cout << hh[i] << "\t";
    cout<<endl;
    Mat hhlvb= (Mat_<double>(3,3) << hh[0],hh[1],hh[2],hh[3],hh[4],hh[5],0,0,1);
    cout << "Error NRC" << SquareError(hhlvb,src,dst)<<endl;
    hhlvb.at<double>(0,2) *=2;
    hhlvb.at<double>(1,2) *=2;
    cout<<"H "<<h<<"\n";
    warpPerspective(m1, m3, hhlvb, Size(m1.cols,m1.rows));
    imshow("m3NRC",m3);
#endif
    h.at<double>(0 ...