ORB SLAM2学习笔记之mono_kitti（三）

一、Tracking流程

大致流程如图所示，下面对细节进行展开说明。

二、实例化mpTracker

在System构造函数中new一个Tracing对象指针mpTracker，方式如下所示：

//Initialize the Tracking thread
//(it will live in the main thread of execution, the one that called this constructor)
mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer,
                         mpMap, mpKeyFrameDatabase, strSettingsFile, mSensor);

构造对象过程中，先进行了传参，如camera焦距，畸变系数，帧率等，然后读取配置文件里的有关提取特征方面的信息，比如每一帧需要提取的特征数（包括整个图像金字塔），金字塔层间尺度，金字塔层数，FAST关键点阈值等，最重要的是最后调用了ORBextractor构造一个ORB特征提取器。

mpIniORBextractor = new ORBextractor(2*nFeatures,fScaleFactor,nLevels,fIniThFAST,fMinThFAST);

那构造特征提取器时都干了些什么呢，如下所示：

ORBextractor

第一步：算金字塔每层的尺度，然后根据尺度计算每层应该提取多少特征点，这里面涉及了一个等比数列，唤起高中的记忆，还挺有意思的。最后，保证提取总特征点数≥ nfeatures。

//参数：特征点多少，金字塔层与层之间的尺度（用于算每层提取关键点的数量），金字塔层级数量，FAST角点阈值，最小角点阈值
ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels,
         int _iniThFAST, int _minThFAST):
    nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
    iniThFAST(_iniThFAST), minThFAST(_minThFAST)
{
    mvScaleFactor.resize(nlevels);
    mvLevelSigma2.resize(nlevels);
    mvScaleFactor[0]=1.0f;
    mvLevelSigma2[0]=1.0f;
    for(int i=1; i<nlevels; i++)
    {
        mvScaleFactor[i]=mvScaleFactor[i-1]*scaleFactor;
        mvLevelSigma2[i]=mvScaleFactor[i]*mvScaleFactor[i];
    }

    mvInvScaleFactor.resize(nlevels);
    mvInvLevelSigma2.resize(nlevels);
    for(int i=0; i<nlevels; i++)
    {
        mvInvScaleFactor[i]=1.0f/mvScaleFactor[i];
        mvInvLevelSigma2[i]=1.0f/mvLevelSigma2[i];
    }

    mvImagePyramid.resize(nlevels);//金字塔有几层

    mnFeaturesPerLevel.resize(nlevels);
    float factor = 1.0f / scaleFactor;  //梯度q
    //第一层特征点的数量  nfeatures是总特征点的数量   后面的式子是等比数列求和公式   nfeatures × （1-q）/（1 - q^n）
    float nDesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));

    //依次算每层的特征点数量，加和，保证最后总的特征点数量 ≥ nfeatures
    int sumFeatures = 0;
    for( int level = 0; level < nlevels-1; level++ )
    {
        mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
        sumFeatures += mnFeaturesPerLevel[level];
        nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);

第二步：准备制作描述子，包括采用bit_pattern_31_模板，计算像素点半径什么的，有点opencv源码的知识，没太看懂，不能钻牛角尖。。。有大神看懂下面的代码能给我讲一哈吗，求带求带

    /*!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!复制训练用的模板!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!*/
    const int npoints = 512;//512个点，256对点，比较完会有一个256位的描述子
    const Point* pattern0 = (const Point*)bit_pattern_31_;//??? bit_pattern_31_可能的意思是描述子的计算区域直径是 31 的模式

    //std::copy(start, end, std::back_inserter(container)); 从 start 到 end 的迭代器复制完放入 container 的后面
    std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

    //This is for orientation
    // pre-compute the end of a row in a circular patch
    umax.resize(HALF_PATCH_SIZE + 1);

    int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);  //cvFloor含义是取不大于参数的最大整数值
    int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);  //cvCeil含义是取不小于参数的最小整数值

    //?????????????????????????????????????????????????????????????????????????????????????????????????
    const double hp2 = HALF_PATCH_SIZE*HALF_PATCH_SIZE;  //半径的平方
    for (v = 0; v <= vmax; ++v)
        umax[v] = cvRound(sqrt(hp2 - v * v));

    // Make sure we are symmetric
    for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
    {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }

三、TrackMonocular

如果忽略其他线程的构造，就继续回到main函数里，看得出main函数里很重要的就是这个TrackMonocular了，函数体如下所示：

cv::Mat System::TrackMonocular(const cv::Mat &im, const double &timestamp)
{
    if(mSensor!=MONOCULAR)
    {
        cerr << "ERROR: you called TrackMonocular but input sensor was not set to Monocular." << endl;
        exit(-1);
    }

    // Check mode change
    {
        unique_lock<mutex> lock(mMutexMode);
        if(mbActivateLocalizationMode)
        {
            mpLocalMapper->RequestStop();

            // Wait until Local Mapping has effectively stopped
            while(!mpLocalMapper->isStopped())
            {
                usleep(1000);
            }

            mpTracker->InformOnlyTracking(true);
            mbActivateLocalizationMode = false;
        }
        if(mbDeactivateLocalizationMode)
        {
            mpTracker->InformOnlyTracking(false);
            mpLocalMapper->Release();
            mbDeactivateLocalizationMode = false;
        }
    }

    // Check reset
    {
    unique_lock<mutex> lock(mMutexReset);
    if(mbReset)
    {
        mpTracker->Reset();
        mbReset = false;
    }
    }

    cv::Mat Tcw = mpTracker->GrabImageMonocular(im,timestamp);

    unique_lock<mutex> lock2(mMutexState);
    mTrackingState = mpTracker->mState;
    mTrackedMapPoints = mpTracker->mCurrentFrame.mvpMapPoints;
    mTrackedKeyPointsUn = mpTracker->mCurrentFrame.mvKeysUn;

    return Tcw;
}

看得出这个函数调用完会返回一个Mat类的东西，就是Tcw位姿。函数刚开始先check了一下系统现在是不是LocalizationMode，有没有Reset，用了mutex大概是为了防止不同线程访问共享内存从而出错，并发编程这里还不是很懂，也没有什么具体资料，等学会以后会更新一下，还请大神走过路过赐教一下鸭…接下来进入GrabImageMonocular函数，计算都是在这里进行的。

GrabImageMonocular

函数需要两个参数：图片和对应的时间戳。先进行图片格式的转换，然后构造了CurrentFrame，最后进行Track。

cv::Mat Tracking::GrabImageMonocular(const cv::Mat &im, const double &timestamp)
{
    mImGray = im;

    //根据传入图片的不同情况转换图片格式
    if(mImGray.channels()==3)
    {
        if(mbRGB)
            cvtColor(mImGray,mImGray,CV_RGB2GRAY);
        else
            cvtColor(mImGray,mImGray,CV_BGR2GRAY);
    }
    else if(mImGray.channels()==4)
    {
        if(mbRGB)
            cvtColor(mImGray,mImGray,CV_RGBA2GRAY);
        else
            cvtColor(mImGray,mImGray,CV_BGRA2GRAY);
    }

    if(mState==NOT_INITIALIZED || mState==NO_IMAGES_YET) //之前Tracking构造函数的时候，已经将mState初始化为NO_IMAGES_YET
        mCurrentFrame = Frame(mImGray,timestamp,mpIniORBextractor,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);
    else
        mCurrentFrame = Frame(mImGray,timestamp,mpORBextractorLeft,mpORBVocabulary,mK,mDistCoef,mbf,mThDepth);

    Track();

    return mCurrentFrame.mTcw.clone();
}

CurrentFrame构造

构造当前帧（ CurrentFrame ）需要传入几个参数，见下面注释。函数内部干了几件事：
第一个：从ORBextractor中提取一些参数用于后面提取特征点；

Frame::Frame(const cv::Mat &imGray, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth)
    :mpORBvocabulary(voc),mpORBextractorLeft(extractor),mpORBextractorRight(static_cast<ORBextractor*>(NULL)),
     mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth)
{
    // Frame ID
    mnId=nNextId++;

    // Scale Level Info   图像金字塔参数
    mnScaleLevels = mpORBextractorLeft->GetLevels();
    mfScaleFactor = mpORBextractorLeft->GetScaleFactor();
    mfLogScaleFactor = log(mfScaleFactor);
    mvScaleFactors = mpORBextractorLeft->GetScaleFactors();
    mvInvScaleFactors = mpORBextractorLeft->GetInverseScaleFactors();
    mvLevelSigma2 = mpORBextractorLeft->GetScaleSigmaSquares();
    mvInvLevelSigma2 = mpORBextractorLeft->GetInverseScaleSigmaSquares();

第二个：调用函数ExtractORB提取ORB特征，然后对特征点去畸变；

    // ORB extraction
    ExtractORB(0,imGray);//能得到 mvKeys 与 mDescriptors，关键点和描述子

    N = mvKeys.size();

    if(mvKeys.empty())
        return;

    UndistortKeyPoints(); //关键点去畸变

    // Set no stereo information
    mvuRight = vector<float>(N,-1);
    mvDepth = vector<float>(N,-1);

    mvpMapPoints = vector<MapPoint*>(N,static_cast<MapPoint*>(NULL));
    mvbOutlier = vector<bool>(N,false);

第三个：如果是对第一帧图像，还会计算图片边界，然后把特征点分配进之前打算分的格子中

    // This is done only for the first Frame (or after a change in the calibration)
    if(mbInitialComputations)
    {
        ComputeImageBounds(imGray); // 算出 mnMinX mnMaxX mnMinY mnMaxY

        mfGridElementWidthInv=static_cast<float>(FRAME_GRID_COLS)/static_cast<float>(mnMaxX-mnMinX);
        mfGridElementHeightInv=static_cast<float>(FRAME_GRID_ROWS)/static_cast<float>(mnMaxY-mnMinY);

        fx = K.at<float>(0,0);
        fy = K.at<float>(1,1);
        cx = K.at<float>(0,2);
        cy = K.at<float>(1,2);
        invfx = 1.0f/fx;
        invfy = 1.0f/fy;

        mbInitialComputations=false;
    }

    mb = mbf/fx;

    AssignFeaturesToGrid(); //把特征加入格子中
}

ExtractORB函数的说明

函数里面调用了ORBextractor类里的operator()函数，用于提取关键点和描述子：

void Frame::ExtractORB(int flag, const cv::Mat &im)
{
    if(flag==0)
        (*mpORBextractorLeft)(im,cv::Mat(),mvKeys,mDescriptors);
    else
        (*mpORBextractorRight)(im,cv::Mat(),mvKeysRight,mDescriptorsRight);
}

operator()函数需要传入要提取的图像，内部为：

//提取关键点和描述子
void ORBextractor::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
                      OutputArray _descriptors)
{ 
    if(_image.empty())
        return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );

    // Pre-compute the scale pyramid
    ComputePyramid(image); // 计算 mvImagePyramid

    vector < vector<KeyPoint> > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints);
    //ComputeKeyPointsOld(allKeypoints);

    Mat descriptors;

    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }

    _keypoints.clear();
    _keypoints.reserve(nkeypoints);

    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image
        Mat workingMat = mvImagePyramid[level].clone();
        GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);

        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                 keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}

其中提取FAST关键点有两个很重要的函数是 ComputePyramid和 ComputeKeyPointsOctTree，（其实还有一个computeDescriptors也很重要不过没细看）前者构建了图像金字塔 mvImagePyramid 供给后面 OctTree 的计算。

ComputePyramid

这个函数有个很奇怪的地方，for循环里面有个Mat temp的初始化，也就是说每调用一次就会重新构造temp这个对象，但是这样的话后面copyMakeBorder这个函数就没什么作用了鸭…但我确实就像下面代码中的一样将temp cout 出来发现它并不都是初始化后的状态，C++博大精深…希望路过的大神能帮我解答一下…

void ORBextractor::ComputePyramid(cv::Mat image)
{

    for (int level = 0; level < nlevels; ++level)
    {
        float scale = mvInvScaleFactor[level];
        Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));//列数是宽，行数是高
        Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
        Mat temp(wholeSize, image.type()), masktemp;
        //cout<<"temp"<

        //左上角点坐标，宽，高
        mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

        // Compute the resized image
        if( level != 0 )
        {
            resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);

            //造边界
            copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101+BORDER_ISOLATED);
            
        }
        else
        {
            //造边界EDGE_THRESHOLD ，以最外面的像素为对称轴,输出为temp
            copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101);
            
        }
    }

}

ComputeKeyPointsOctTree

函数采用划分格子的形式在格子里挑选关键点，这样可能效率更高些，然后通过函数DistributeOctTree以四叉树形式将关键点分配给每个节点，然后将响应值response最大的点挑出来，这样能保证关键点分布是均匀的，然后通过computeOrientation函数利用灰度质心法IC_Angle计算关键点的方向。过程略复杂，如下面注释所示：

void ORBextractor::ComputeKeyPointsOctTree(vector<vector<KeyPoint> >& allKeypoints)
{
    allKeypoints.resize(nlevels);// 有 nlevels 层 keypoints

    // 设置格子大小
    const float W = 30;

    for (int level = 0; level < nlevels; ++level)
    {
        const int minBorderX = EDGE_THRESHOLD-3;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD+3;
        const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD+3;

        vector<cv::KeyPoint> vToDistributeKeys;
        vToDistributeKeys.reserve(nfeatures*10);

        const float width = (maxBorderX-minBorderX);
        const float height = (maxBorderY-minBorderY);

        const int nCols = width/W;                    //每一行有几个格子
        const int nRows = height/W;                   //每一列有几个格子
        const int wCell = ceil(width/nCols);          //每一行每个格子的实际宽度
        const int hCell = ceil(height/nRows);         //每一列每个格子的实际高度

        for(int i=0; i<nRows; i++)
        {
            const float iniY =minBorderY+i*hCell;     //cell的起始y值
            float maxY = iniY+hCell+6;                //cell的最大y值

            if(iniY>=maxBorderY-3)
                continue;
            if(maxY>maxBorderY)
                maxY = maxBorderY;

            for(int j=0; j<nCols; j++)
            {
                const float iniX =minBorderX+j*wCell; //cell的起始x值
                float maxX = iniX+wCell+6;            //cell的最大x值
                if(iniX>=maxBorderX-6)
                    continue;
                if(maxX>maxBorderX)
                    maxX = maxBorderX;

                vector<cv::KeyPoint> vKeysCell;
                FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                     vKeysCell,iniThFAST,true);

                // 如果检测到的fast特征为空，则降低阈值再进行检测
                if(vKeysCell.empty())
                {
                    FAST(mvImagePyramid[level].rowRange(iniY,maxY).colRange(iniX,maxX),
                         vKeysCell,minThFAST,true);
                }

                if(!vKeysCell.empty())
                {
                    for(vector<cv::KeyPoint>::iterator vit=vKeysCell.begin(); vit!=vKeysCell.end();vit++)
                    {
                        (*vit).pt.x+=j*wCell; //每个cell中特征点实际的x坐标，当然，是相对于minBorderX的
                        (*vit).pt.y+=i*hCell; //每个cell中特征点实际的y坐标，当然，是相对于minBorderY的
                        vToDistributeKeys.push_back(*vit); //装满了每一层FAST特征点
                    }
                }

            }
        }

        //把第level层的keypoints赋值。 keypoints变化，allKeypoints[level]也跟着变化  KeyPoint类里面有pt、angle等信息
        vector<KeyPoint> & keypoints = allKeypoints[level];
        keypoints.reserve(nfeatures);

        //对每个节点里面的特征进行选择最好特征，这样就对检测到的特征进行了均匀化处理
        keypoints = DistributeOctTree(vToDistributeKeys, minBorderX, maxBorderX,
                                      minBorderY, maxBorderY,mnFeaturesPerLevel[level], level);

        const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level]; //每层直径的尺度不一样

        // Add border to coordinates and scale information
        // 换算关键点真实位置（添加边界值），添加关键点的尺度信息
        const int nkps = keypoints.size();
        for(int i=0; i<nkps ; i++)
        {
            keypoints[i].pt.x+=minBorderX;
            keypoints[i].pt.y+=minBorderY;
            keypoints[i].octave=level; //金字塔第几层
            keypoints[i].size = scaledPatchSize;
        }
    }

    // compute orientations
    for (int level = 0; level < nlevels; ++level)
        computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
}

DistributeOctTree函数功能是把关键点均匀化，就不多说了，代码注释的很详细，直接贴代码了，略长但是逻辑很清晰，父节点生子节点，一生四，四生十六，每层金字塔都这么做：

/*!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!将图像划分成四叉树形式!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!*/

//参数： 第level层的所有FAST关键点组成的vector，特征检测区域上下两个边界的x与y坐标，第level层的特征点数量，第level层
vector<cv::KeyPoint> ORBextractor::DistributeOctTree(const vector<cv::KeyPoint>& vToDistributeKeys, const int &minX,
                                       const int &maxX, const int &minY, const int &maxY, const int &N, const int &level)
{
    // Compute how many initial nodes
    //根据图像宽高比例确定有几个节点，例如图像太长或太扁初始节点可能多于一个
    const int nIni = round( static_cast<float>(maxX-minX)/(maxY-minY) );

    //节点在x方向上的长度
    const float hX = static_cast<float>(maxX-minX)/nIni;

    list<ExtractorNode> lNodes;

    vector<ExtractorNode*> vpIniNodes;
    vpIniNodes.resize(nIni);

    //UL UR BL BR是节点的四个角的坐标
    for(int i=0; i<nIni; i++)
    {
        ExtractorNode ni;
        ni.UL = cv::Point2i(hX*static_cast<float>(i),0);
        ni.UR = cv::Point2i(hX*static_cast<float>(i+1),0);
        ni.BL = cv::Point2i(ni.UL.x,maxY-minY);
        ni.BR = cv::Point2i(ni.UR.x,maxY-minY);
        ni.vKeys.reserve(vToDistributeKeys.size());

        lNodes.push_back(ni);
        vpIniNodes[i] = &lNodes.back();
    }

    //Associate points to childs
    //把图片里的关键点分配到节点中去
    for(size_t i=0;i<vToDistributeKeys.size();i++)
    {
        const cv::KeyPoint &kp = vToDistributeKeys[i];
        vpIniNodes[kp.pt.x/hX]->vKeys.push_back(kp);
    }

    list<ExtractorNode>::iterator lit = lNodes.begin();

    while(lit!=lNodes.end())
    {
        if(lit->vKeys.size()==1)
        {
            lit->bNoMore=true;
            lit++;
        }
        else if(lit->vKeys.empty())
            lit = lNodes.erase(lit);
        else
            lit++;
    }

    bool bFinish = false;

    int iteration = 0;

    vector<pair<int,ExtractorNode*> > vSizeAndPointerToNode; //节点 和 它对应包含的 特征点数
    vSizeAndPointerToNode.reserve(lNodes.size()*4); //一共有几个子节点（从初始节点分叉）

    while(!bFinish)
    {
        iteration++;

        int prevSize = lNodes.size(); //老节点的数量

        lit = lNodes.begin(); //lit是节点的迭代器

        int nToExpand = 0;

        vSizeAndPointerToNode.clear();//清空

        /*感觉while执行完 要么就是不再划分新节点，lnodes里都是老节点，
                         要么就是分完新节点后老节点被一个个erase掉，新节点从前面一个个添加进来*/
        while(lit!=lNodes.end())
        {
            if(lit->bNoMore)
            {
                // If node only contains one point do not subdivide and continue
                // 如果节点中只有一个特征点就不划分了
                lit++;
                continue;
            }
            else
            {
                // If more than one point, subdivide
                ExtractorNode n1,n2,n3,n4;
                lit->DivideNode(n1,n2,n3,n4);

                // Add childs if they contain points
                if(!n1.vKeys.empty())
                {
                    lNodes.push_front(n1);     //把子节点推到lnodes前面 变成lnodes的一部分
                    if(n1.vKeys.size()>1)
                    {
                        nToExpand++; //还要再分

                        //把n1与它里面的特征点数量推进去
                        vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin(); //begin()迭代器 赋给 第一个元素的迭代器
                    }
                }
                if(!n2.vKeys.empty())
                {
                    lNodes.push_front(n2);
                    if(n2.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if(!n3.vKeys.empty())
                {
                    lNodes.push_front(n3);
                    if(n3.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }
                if(!n4.vKeys.empty())
                {
                    lNodes.push_front(n4);
                    if(n4.vKeys.size()>1)
                    {
                        nToExpand++;
                        vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front()));
                        lNodes.front().lit = lNodes.begin();
                    }
                }

                lit=lNodes.erase(lit);  //lit指向下一个元素
                continue;
            }
        }       

        // Finish if there are more nodes than required features
        // or all nodes contain just one point
        if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
        {
            bFinish = true;
        }

        //节点展开次数乘以3用于表明下一次的节点分解可能超过特征数，即为最后一次分解
        else if(((int)lNodes.size()+nToExpand*3)>N)
        {

            while(!bFinish)
            {

                prevSize = lNodes.size();

                vector<pair<int,ExtractorNode*> > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
                vSizeAndPointerToNode.clear();

                sort(vPrevSizeAndPointerToNode.begin(),vPrevSizeAndPointerToNode.end());
                for(int j=vPrevSizeAndPointerToNode.size()-1;j>=0;j--)
                {
                    ExtractorNode n1,n2,n3,n4;
                    vPrevSizeAndPointerToNode[j].second->DivideNode(n1,n2,n3,n4);

                    // Add childs if they contain points
                    if(n1.vKeys.size()>0)
                    {
                        lNodes.push_front(n1);
                        if(n1.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n2.vKeys.size()>0)
                    {
                        lNodes.push_front(n2);
                        if(n2.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n3.vKeys.size()>0)
                    {
                        lNodes.push_front(n3);
                        if(n3.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }
                    if(n4.vKeys.size()>0)
                    {
                        lNodes.push_front(n4);
                        if(n4.vKeys.size()>1)
                        {
                            vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(),&lNodes.front()));
                            lNodes.front().lit = lNodes.begin();
                        }
                    }

                    lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

                    if((int)lNodes.size()>=N)
                        break;
                }

                if((int)lNodes.size()>=N || (int)lNodes.size()==prevSize)
                    bFinish = true;

            }
        }
    }

    // Retain the best point in each node
    vector<cv::KeyPoint> vResultKeys;
    vResultKeys.reserve(nfeatures);
    for(list<ExtractorNode>::iterator lit=lNodes.begin(); lit!=lNodes.end(); lit++)
    {
        vector<cv::KeyPoint> &vNodeKeys = lit->vKeys;//vNodeKeys是一个节点中的特征点
        cv::KeyPoint* pKP = &vNodeKeys[0];//第一个特征点
        float maxResponse = pKP->response; //response代表着该关键点how good，更确切的说，是该点角点的程度。

        for(size_t k=1;k<vNodeKeys.size();k++) //选择一个响应最大的特征点
        {
            if(vNodeKeys[k].response>maxResponse)
            {
                pKP = &vNodeKeys[k];
                maxResponse = vNodeKeys[k].response;
            }
        }

        vResultKeys.push_back(*pKP); //推进去
    }

    return vResultKeys;//把最终的筛选过的每个节点中响应最好的特征点作为结果返回
}

这里面有个函数叫DivideNode，代码如下，功能就像函数名一样，父节点分成四个子节点，别忘了x和y轴的方向：

void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4)
{
    const int halfX = ceil(static_cast<float>(UR.x-UL.x)/2);
    const int halfY = ceil(static_cast<float>(BR.y-UL.y)/2);

    //Define boundaries of childs
    /*!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
     *                          -------------------------------------------------→
     *                          |                      |                     |
     *                          |                      |                     |
     *                          |          n1          |          n2         |
     *                          |                      |                     |
     *                          |                      |                     |
     *                          |----------------------|---------------------|
     *                          |                      |                     |
     *                          |                      |                     |
     *                          |          n3          |          n4         |
     *                          |                      |                     |
     *                          |                      |                     |
     *                          |---------------------------------------------
     *                          ↓
     *
     *!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!*/

    n1.UL = UL;
    n1.UR = cv::Point2i(UL.x+halfX,UL.y);
    n1.BL = cv::Point2i(UL.x,UL.y+halfY);
    n1.BR = cv::Point2i(UL.x+halfX,UL.y+halfY);
    n1.vKeys.reserve(vKeys.size());

    n2.UL = n1.UR;
    n2.UR = UR;
    n2.BL = n1.BR;
    n2.BR = cv::Point2i(UR.x,UL.y+halfY);
    n2.vKeys.reserve(vKeys.size());

    n3.UL = n1.BL;
    n3.UR = n1.BR;
    n3.BL = BL;
    n3.BR = cv::Point2i(n1.BR.x,BL.y);
    n3.vKeys.reserve(vKeys.size());

    n4.UL = n3.UR;
    n4.UR = n2.BR;
    n4.BL = n3.BR;
    n4.BR = BR;
    n4.vKeys.reserve(vKeys.size());

    //Associate points to childs
    for(size_t i=0;i<vKeys.size();i++)
    {
        const cv::KeyPoint &kp = vKeys[i];
        if(kp.pt.x<n1.UR.x)
        {
            if(kp.pt.y<n1.BR.y)
                n1.vKeys.push_back(kp);
            else
                n3.vKeys.push_back(kp);
        }
        else if(kp.pt.y<n1.BR.y)
            n2.vKeys.push_back(kp);
        else
            n4.vKeys.push_back(kp);
    }

    if(n1.vKeys.size()==1)
        n1.bNoMore = true;
    if(n2.vKeys.size()==1)
        n2.bNoMore = true;
    if(n3.vKeys.size()==1)
        n3.bNoMore = true;
    if(n4.vKeys.size()==1)
        n4.bNoMore = true;

}

CurrentFrame构造流程图

Track()函数还没有看，看完下篇博客总结，最后看一下构造当前帧的流程图：