FindHomography

void Initializer::FindHomography(vector<bool> &vbMatchesInliers, float &score, cv::Mat &H21)
{
    // Number of putative matches
    const int N = mvMatches12.size();

    // Normalize coordinates
    vector<cv::Point2f> vPn1, vPn2;
    //防止不同分辨率、尺度和坐标原点下的影响，提高解的稳定性和精度
    //T 表示归一化的关系 P‘ = T * p
    Normalize(mvKeys1,vPn1, T1);
    Normalize(mvKeys2,vPn2, T2);
    cv::Mat T2inv = T2.inv();

    // Best Results variables
    score = 0.0;
    //N个点，都是0
    vbMatchesInliers = vector<bool>(N,false);

    // Iteration variables
    //八点法
    vector<cv::Point2f> vPn1i(8);
    vector<cv::Point2f> vPn2i(8);
    cv::Mat H21i, H12i;
    vector<bool> vbCurrentInliers(N,false);
    float currentScore;

    // Perform all RANSAC iterations and save the solution with highest score
    //mMaxIterations：随机选取的（8组点对）点对的数量
    for(int it=0; it<mMaxIterations; it++)
    {
        // Select a minimum set
        //提取出八个点在mvMatches12中对应的索引，在取出坐标
        for(size_t j=0; j<8; j++)
        {
            int idx = mvSets[it][j];

            vPn1i[j] = vPn1[mvMatches12[idx].first];
            vPn2i[j] = vPn2[mvMatches12[idx].second];
        }

        //计算本质矩阵，并且这里把归一化坐标的还原也计算在本质矩阵中
        cv::Mat Hn = ComputeH21(vPn1i,vPn2i);
        H21i = T2inv*Hn*T1;
        H12i = H21i.inv();
        //计算分数，选择最好的模型
        currentScore = CheckHomography(H21i, H12i, vbCurrentInliers, mSigma);
        //用更好的分数 替换 旧的，直到得到最好的模型
        if(currentScore>score)
        {
            H21 = H21i.clone();
            vbMatchesInliers = vbCurrentInliers;
            score = currentScore;
        }
    }
}

标签：H21i,score,FindHomography,currentScore,SLAM2,vector,mvMatches12,ORB,cv
来源： https://blog.csdn.net/weixin_51326570/article/details/114741527