ORBSLAM2源码学习（6） ORBmatcher类

还是先上代码再放总结

#include "ORBmatcher.h"
#include<limits.h>
#include<opencv2/core/core.hpp>
#include<opencv2/features2d/features2d.hpp>
#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h"
#include<stdint.h>

using namespace std;

namespace ORB_SLAM2
{

const int ORBmatcher::TH_HIGH = 100;
const int ORBmatcher::TH_LOW = 50;
const int ORBmatcher::HISTO_LENGTH = 30;

ORBmatcher::ORBmatcher(float nnratio, bool checkOri): mfNNratio(nnratio), mbCheckOrientation(checkOri)
{
}

// 通过投影，对Local MapPoint进行跟踪
// 将Local MapPoint投影到当前帧中, 由此增加当前帧的MapPoints 
int ORBmatcher::SearchByProjection(Frame &F, const vector<MapPoint*> &vpMapPoints, const float th)
{
    int nmatches=0;

    const bool bFactor = th!=1.0;

    for(size_t iMP=0; iMP<vpMapPoints.size(); iMP++)
    {
        MapPoint* pMP = vpMapPoints[iMP];

        if(!pMP->mbTrackInView)
            continue;

        if(pMP->isBad())
            continue;
            
        // 通过距离预测的金字塔层数
        const int &nPredictedLevel = pMP->mnTrackScaleLevel;

        // The size of the window will depend on the viewing direction
        // 搜索范围, 若当前视角和平均视角夹角接近时, r取一个较小的值
        float r = RadiusByViewingCos(pMP->mTrackViewCos);
        
        if(bFactor)
            r*=th;

        // 通过投影点投影到当前帧,以及搜索窗口和预测的尺度进行搜索, 找出附近的点
        const vector<size_t> vIndices =
                F.GetFeaturesInArea(pMP->mTrackProjX,pMP->mTrackProjY,r*F.mvScaleFactors[nPredictedLevel],nPredictedLevel-1,nPredictedLevel);

        if(vIndices.empty())
            continue;

        const cv::Mat MPdescriptor = pMP->GetDescriptor();

        int bestDist=256;
        int bestLevel= -1;
        int bestDist2=256;
        int bestLevel2 = -1;
        int bestIdx =-1 ;

        // Get best and second matches with near keypoints
		// 遍历在设置范围内找到的点
        for(vector<size_t>::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
        {
            const size_t idx = *vit;

            // 如果Frame中的该点已经有对应的MapPoint了,则退出该次循环
            if(F.mvpMapPoints[idx])
                if(F.mvpMapPoints[idx]->Observations()>0)
                    continue;

            if(F.mvuRight[idx]>0)
            {
                const float er = fabs(pMP->mTrackProjXR-F.mvuRight[idx]);
                if(er>r*F.mvScaleFactors[nPredictedLevel])
                    continue;
            }

            const cv::Mat &d = F.mDescriptors.row(idx);

            const int dist = DescriptorDistance(MPdescriptor,d);
            
            // 根据描述子寻找描述子距离最小和次小的特征点
            if(dist<bestDist)
            {
                bestDist2=bestDist;
                bestDist=dist;
                bestLevel2 = bestLevel;
                bestLevel = F.mvKeysUn[idx].octave;
                bestIdx=idx;
            }
            else if(dist<bestDist2)
            {
                bestLevel2 = F.mvKeysUn[idx].octave;
                bestDist2=dist;
            }
        }

        // Apply ratio to second match (only if best and second are in the same scale level)
        if(bestDist<=TH_HIGH)
        {
            if(bestLevel==bestLevel2 && bestDist>mfNNratio*bestDist2)
                continue;

            F.mvpMapPoints[bestIdx]=pMP; // 为Frame中的点增加对应的MapPoint
            nmatches++;
        }
    }

    return nmatches;
}

// 视角和平均观测视角越小，搜索范围越小
float ORBmatcher::RadiusByViewingCos(const float &viewCos)
{
    if(viewCos>0.998)
        return 2.5;
    else
        return 4.0;
}
// 判断点到极线的距离是否合适
// 计算kp2特征点到极线的距离：
// 极线l：ax + by + c = 0
// (u,v)到l的距离为： |au+bv+c| / sqrt(a^2+b^2)
bool ORBmatcher::CheckDistEpipolarLine(const cv::KeyPoint &kp1,const cv::KeyPoint &kp2,const cv::Mat &F12,const KeyFrame* pKF2)
{
    // Epipolar line in second image l = x1'F12 = [a b c]
    // 求出kp1在pKF2上对应的极线
    const float a = kp1.pt.x*F12.at<float>(0,0)+kp1.pt.y*F12.at<float>(1,0)+F12.at<float>(2,0);
    const float b = kp1.pt.x*F12.at<float>(0,1)+kp1.pt.y*F12.at<float>(1,1)+F12.at<float>(2,1);
    const float c = kp1.pt.x*F12.at<float>(0,2)+kp1.pt.y*F12.at<float>(1,2)+F12.at<float>(2,2);

    const float num = a*kp2.pt.x+b*kp2.pt.y+c;

    const float den = a*a+b*b;

    if(den==0)
        return false;

    const float dsqr = num*num/den;

    return dsqr<3.84*pKF2->mvLevelSigma2[kp2.octave];
}

// 通过bow对关键帧(pKF)和普通帧(F)中的特征点进行快速匹配
// 对属于同一node的特征点通过描述子距离进行匹配 
// 根据匹配，用pKF中特征点对应的MapPoint更新F中特征点对应的MapPoints 
// 通过距离阈值、比例阈值和角度投票进行剔除误匹配
// 注意是对关键帧的特征点进行匹配
int ORBmatcher::SearchByBoW(KeyFrame* pKF,Frame &F, vector<MapPoint*> &vpMapPointMatches)
{
    const vector<MapPoint*> vpMapPointsKF = pKF->GetMapPointMatches();

    vpMapPointMatches = vector<MapPoint*>(F.N,static_cast<MapPoint*>(NULL));

    const DBoW2::FeatureVector &vFeatVecKF = pKF->mFeatVec;

    int nmatches=0;

    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);
    const float factor = HISTO_LENGTH/360.0f;

    // We perform the matching over ORB that belong to the same vocabulary node (at a certain level)
    DBoW2::FeatureVector::const_iterator KFit = vFeatVecKF.begin();
    DBoW2::FeatureVector::const_iterator Fit = F.mFeatVec.begin();
    DBoW2::FeatureVector::const_iterator KFend = vFeatVecKF.end();
    DBoW2::FeatureVector::const_iterator Fend = F.mFeatVec.end();

    while(KFit != KFend && Fit != Fend)
    {
        if(KFit->first == Fit->first) 
        {
            const vector<unsigned int> vIndicesKF = KFit->second;	// KF中的点
            const vector<unsigned int> vIndicesF = Fit->second;		// F中的点

            // 遍历KF中属于该node的特征点
            for(size_t iKF=0; iKF<vIndicesKF.size(); iKF++)
            {
                const unsigned int realIdxKF = vIndicesKF[iKF];

                MapPoint* pMP = vpMapPointsKF[realIdxKF]; // 取出KF中该特征对应的MapPoint

                if(!pMP)
                    continue;

                if(pMP->isBad())
                    continue;

                const cv::Mat &dKF= pKF->mDescriptors.row(realIdxKF); // 取出KF中该特征对应的描述子

                int bestDist1=256; 	// 最好的距离
                int bestIdxF =-1 ;
                int bestDist2=256; 	// 倒数第二好距离

                // 遍历F中属于该node的特征点，找到了最佳匹配点
                for(size_t iF=0; iF<vIndicesF.size(); iF++)
                {
                    const unsigned int realIdxF = vIndicesF[iF];

                    if(vpMapPointMatches[realIdxF])	// 表明这个点已经被匹配过了，不再匹配
                        continue;

                    const cv::Mat &dF = F.mDescriptors.row(realIdxF); // 取出F中该特征对应的描述子

                    const int dist =  DescriptorDistance(dKF,dF); // 求描述子的距离

                    if(dist<bestDist1)
                    {
                        bestDist2=bestDist1;
                        bestDist1=dist;
                        bestIdxF=realIdxF;
                    }
                    else if(dist<bestDist2)
                    {
                        bestDist2=dist;
                    }
                }

                // 根据阈值 和 角度剔除误匹配
                if(bestDist1<=TH_LOW) 
                {
                    // 最佳匹配比次佳匹配明显要好
                    if(static_cast<float>(bestDist1)<mfNNratio*static_cast<float>(bestDist2))
                    {
                        // 更新特征点的MapPoint
                        vpMapPointMatches[bestIdxF]=pMP;

                        const cv::KeyPoint &kp = pKF->mvKeysUn[realIdxKF];

                        if(mbCheckOrientation)
                        {
                            // 所有的特征点的角度变化应该是一致的，通过直方图统计得到最准确的角度变化值
                            float rot = kp.angle-F.mvKeys[bestIdxF].angle;// 该特征点的角度变化值
                            if(rot<0.0)
                                rot+=360.0f;
                            int bin = round(rot*factor);// 将旋转角度分配到组
                            if(bin==HISTO_LENGTH)
                                bin=0;
                            assert(bin>=0 && bin<HISTO_LENGTH);
                            rotHist[bin].push_back(bestIdxF);
                        }
                        nmatches++;
                    }
                }

            }

            KFit++;
            Fit++;
        }
        else if(KFit->first < Fit->first)
        {
            KFit = vFeatVecKF.lower_bound(Fit->first);
        }
        else
        {
            Fit = F.mFeatVec.lower_bound(KFit->first);
        }
    }

    // 根据方向剔除误匹配的点
    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        // 计算rotHist中最大的三个的index
        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            // 如果特征点的旋转角度变化量属于这三个组，则保留
            if(i==ind1 || i==ind2 || i==ind3)
                continue;

            // 将除了ind1 ind2 ind3以外的匹配点去掉
            for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
            {
                vpMapPointMatches[rotHist[i][j]]=static_cast<MapPoint*>(NULL);
                nmatches--;
            }
        }
    }

    return nmatches;
}

// 根据Sim3变换，将每个vpPoints投影到pKF上，并根据尺度确定一个搜索区域，
// 根据该MapPoint的描述子与该区域内的特征点进行匹配
int ORBmatcher::SearchByProjection(KeyFrame* pKF, cv::Mat Scw, const vector<MapPoint*> &vpPoints, vector<MapPoint*> &vpMatched, int th)
{
    // Get Calibration Parameters for later projection
    const float &fx = pKF->fx;
    const float &fy = pKF->fy;
    const float &cx = pKF->cx;
    const float &cy = pKF->cy;

    // Decompose Scw
    cv::Mat sRcw = Scw.rowRange(0,3).colRange(0,3);
    const float scw = sqrt(sRcw.row(0).dot(sRcw.row(0)));	// 计算得到尺度s
    cv::Mat Rcw = sRcw/scw;
    cv::Mat tcw = Scw.rowRange(0,3).col(3)/scw;
    cv::Mat Ow = -Rcw.t()*tcw;

    // Set of MapPoints already found in the KeyFrame
    set<MapPoint*> spAlreadyFound(vpMatched.begin(), vpMatched.end());
    spAlreadyFound.erase(static_cast<MapPoint*>(NULL));

    int nmatches=0;

    // For each Candidate MapPoint Project and Match
    // 遍历所有的MapPoints
    for(int iMP=0, iendMP=vpPoints.size(); iMP<iendMP; iMP++)
    {
        MapPoint* pMP = vpPoints[iMP];

        // Discard Bad MapPoints and already found
        // 丢弃坏的MapPoints和已经匹配上的MapPoints
        if(pMP->isBad() || spAlreadyFound.count(pMP))
            continue;

        // Get 3D Coords.
        cv::Mat p3Dw = pMP->GetWorldPos();

        // Transform into Camera Coords.
        cv::Mat p3Dc = Rcw*p3Dw+tcw;

        // Depth must be positive
        if(p3Dc.at<float>(2)<0.0)
            continue;

        // Project into Image
		// 将地图点投影到图像中（像素）
        const float invz = 1/p3Dc.at<float>(2);
        const float x = p3Dc.at<float>(0)*invz;
        const float y = p3Dc.at<float>(1)*invz;

        const float u = fx*x+cx;
        const float v = fy*y+cy;

        // Point must be inside the image
		// 检查是否在图像内
        if(!pKF->IsInImage(u,v))
            continue;

        // Depth must be inside the scale invariance region of the point
        // 判断距离是否在尺度协方差范围内
        const float maxDistance = pMP->GetMaxDistanceInvariance();
        const float minDistance = pMP->GetMinDistanceInvariance();
        cv::Mat PO = p3Dw-Ow;
        const float dist = cv::norm(PO);

        if(dist<minDistance || dist>maxDistance)
            continue;

        // Viewing angle must be less than 60 deg
		// 检查视角
        cv::Mat Pn = pMP->GetNormal();

        if(PO.dot(Pn)<0.5*dist)
            continue;

		// 预测尺度
        int nPredictedLevel = pMP->PredictScale(dist,pKF);

        // Search in a radius
        // 根据尺度确定搜索半径
        const float radius = th*pKF->mvScaleFactors[nPredictedLevel];

		// 得到搜索范围内的特征点
        const vector<size_t> vIndices = pKF->GetFeaturesInArea(u,v,radius);

		// 没有点则跳过本次循环
        if(vIndices.empty())
            continue;

        // Match to the most similar keypoint in the radius
		// 描述子
        const cv::Mat dMP = pMP->GetDescriptor();

        int bestDist = 256;
        int bestIdx = -1;
        // 遍历搜索区域内所有特征点，与该MapPoint的描述子进行匹配
        for(vector<size_t>::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
        {
            const size_t idx = *vit;
            if(vpMatched[idx])
                continue;

            const int &kpLevel= pKF->mvKeysUn[idx].octave;

			// 尺度相差不能太大
            if(kpLevel<nPredictedLevel-1 || kpLevel>nPredictedLevel)
                continue;

            const cv::Mat &dKF = pKF->mDescriptors.row(idx);

			// 计算描述子距离
            const int dist = DescriptorDistance(dMP,dKF);

			// 记录
            if(dist<bestDist)
            {
                bestDist = dist;
                bestIdx = idx;
            }
        }

        // 该MapPoint与bestIdx对应的特征点匹配成功
        if(bestDist<=TH_LOW)
        {
            vpMatched[bestIdx]=pMP;
            nmatches++;
        }
    }

    return nmatches;
}

// 用于初始化时的搜索匹配
int ORBmatcher::SearchForInitialization(Frame &F1, Frame &F2, vector<cv::Point2f> &vbPrevMatched, vector<int> &vnMatches12, int windowSize)
{
    int nmatches=0;
    vnMatches12 = vector<int>(F1.mvKeysUn.size(),-1);

    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);
    const float factor = HISTO_LENGTH/360.0f;

    vector<int> vMatchedDistance(F2.mvKeysUn.size(),INT_MAX);
    vector<int> vnMatches21(F2.mvKeysUn.size(),-1);

    for(size_t i1=0, iend1=F1.mvKeysUn.size(); i1<iend1; i1++)
    {
        cv::KeyPoint kp1 = F1.mvKeysUn[i1];
        int level1 = kp1.octave;
		// 初始化时只匹配金字塔底层的特征点？
        if(level1>0)
            continue;

		// 在另一帧中设置搜索范围并获取范围内的特征点
        vector<size_t> vIndices2 = F2.GetFeaturesInArea(vbPrevMatched[i1].x,vbPrevMatched[i1].y, windowSize,level1,level1);

        if(vIndices2.empty())
            continue;
		// 取出第一针中点的描述子
        cv::Mat d1 = F1.mDescriptors.row(i1);

        int bestDist = INT_MAX;
        int bestDist2 = INT_MAX;
        int bestIdx2 = -1;

		// 遍历第二帧中候选特征点，提取描述子计算距离，记录最好的和次好的距离和索引
        for(vector<size_t>::iterator vit=vIndices2.begin(); vit!=vIndices2.end(); vit++)
        {
            size_t i2 = *vit;

            cv::Mat d2 = F2.mDescriptors.row(i2);

            int dist = DescriptorDistance(d1,d2);

            if(vMatchedDistance[i2]<=dist)
                continue;

            if(dist<bestDist)
            {
                bestDist2=bestDist;
                bestDist=dist;
                bestIdx2=i2;
            }
            else if(dist<bestDist2)
            {
                bestDist2=dist;
            }
        }

		// 同上面的函数
        if(bestDist<=TH_LOW)
        {
            if(bestDist<(float)bestDist2*mfNNratio)
            {
                if(vnMatches21[bestIdx2]>=0)
                {
                    vnMatches12[vnMatches21[bestIdx2]]=-1;
                    nmatches--;
                }
                vnMatches12[i1]=bestIdx2;
                vnMatches21[bestIdx2]=i1;
                vMatchedDistance[bestIdx2]=bestDist;
                nmatches++;

                if(mbCheckOrientation)
                {
                    float rot = F1.mvKeysUn[i1].angle-F2.mvKeysUn[bestIdx2].angle;
                    if(rot<0.0)
                        rot+=360.0f;
                    int bin = round(rot*factor);
                    if(bin==HISTO_LENGTH)
                        bin=0;
                    assert(bin>=0 && bin<HISTO_LENGTH);
                    rotHist[bin].push_back(i1);
                }
            }
        }

    }

    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            if(i==ind1 || i==ind2 || i==ind3)
                continue;
            for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
            {
                int idx1 = rotHist[i][j];
                if(vnMatches12[idx1]>=0)
                {
                    vnMatches12[idx1]=-1;
                    nmatches--;
                }
            }
        }

    }

    //Update prev matched
    for(size_t i1=0, iend1=vnMatches12.size(); i1<iend1; i1++)
        if(vnMatches12[i1]>=0)
            vbPrevMatched[i1]=F2.mvKeysUn[vnMatches12[i1]].pt;

    return nmatches;
}

// 通过词包，对关键帧的特征点进行跟踪，该函数用于闭环检测时两个关键帧间的特征点匹配
// 是关键帧之间的匹配
int ORBmatcher::SearchByBoW(KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint *> &vpMatches12)
{
	// 分别获取特征点、特征点的词向量、对应的地图点、描述子
    const vector<cv::KeyPoint> &vKeysUn1 = pKF1->mvKeysUn;
    const DBoW2::FeatureVector &vFeatVec1 = pKF1->mFeatVec;
    const vector<MapPoint*> vpMapPoints1 = pKF1->GetMapPointMatches();
    const cv::Mat &Descriptors1 = pKF1->mDescriptors;

    const vector<cv::KeyPoint> &vKeysUn2 = pKF2->mvKeysUn;
    const DBoW2::FeatureVector &vFeatVec2 = pKF2->mFeatVec;
    const vector<MapPoint*> vpMapPoints2 = pKF2->GetMapPointMatches();
    const cv::Mat &Descriptors2 = pKF2->mDescriptors;

    vpMatches12 = vector<MapPoint*>(vpMapPoints1.size(),static_cast<MapPoint*>(NULL));
    vector<bool> vbMatched2(vpMapPoints2.size(),false);

    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);

    const float factor = HISTO_LENGTH/360.0f;

    int nmatches = 0;

    DBoW2::FeatureVector::const_iterator f1it = vFeatVec1.begin();
    DBoW2::FeatureVector::const_iterator f2it = vFeatVec2.begin();
    DBoW2::FeatureVector::const_iterator f1end = vFeatVec1.end();
    DBoW2::FeatureVector::const_iterator f2end = vFeatVec2.end();

    while(f1it != f1end && f2it != f2end)
    {
        if(f1it->first == f2it->first)		// 分别取出属于同一node的ORB特征点
        {
            // 遍历KF1中属于该node的特征点
            for(size_t i1=0, iend1=f1it->second.size(); i1<iend1; i1++)
            {
                const size_t idx1 = f1it->second[i1];

                MapPoint* pMP1 = vpMapPoints1[idx1];
                if(!pMP1)
                    continue;
                if(pMP1->isBad())
                    continue;

                const cv::Mat &d1 = Descriptors1.row(idx1);

                int bestDist1=256;
                int bestIdx2 =-1 ;
                int bestDist2=256;

                // 遍历KF2中属于该node的特征点，找到最佳匹配点
                for(size_t i2=0, iend2=f2it->second.size(); i2<iend2; i2++)
                {
                    const size_t idx2 = f2it->second[i2];

                    MapPoint* pMP2 = vpMapPoints2[idx2];

                    if(vbMatched2[idx2] || !pMP2)
                        continue;

                    if(pMP2->isBad())
                        continue;

                    const cv::Mat &d2 = Descriptors2.row(idx2);

                    int dist = DescriptorDistance(d1,d2);

                    if(dist<bestDist1)
                    {
                        bestDist2=bestDist1;
                        bestDist1=dist;
                        bestIdx2=idx2;
                    }
                    else if(dist<bestDist2)
                    {
                        bestDist2=dist;
                    }
                }

                // 根据阈值 和 角度剔除误匹配
                if(bestDist1<TH_LOW)
                {
                    if(static_cast<float>(bestDist1)<mfNNratio*static_cast<float>(bestDist2))
                    {
                        vpMatches12[idx1]=vpMapPoints2[bestIdx2];
                        vbMatched2[bestIdx2]=true;

                        if(mbCheckOrientation)
                        {
                            float rot = vKeysUn1[idx1].angle-vKeysUn2[bestIdx2].angle;
                            if(rot<0.0)
                                rot+=360.0f;
                            int bin = round(rot*factor);
                            if(bin==HISTO_LENGTH)
                                bin=0;
                            assert(bin>=0 && bin<HISTO_LENGTH);
                            rotHist[bin].push_back(idx1);
                        }
                        nmatches++;
                    }
                }
            }

            f1it++;
            f2it++;
        }
        else if(f1it->first < f2it->first)
        {
            f1it = vFeatVec1.lower_bound(f2it->first);
        }
        else
        {
            f2it = vFeatVec2.lower_bound(f1it->first);
        }
    }

    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            if(i==ind1 || i==ind2 || i==ind3)
                continue;
            for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
            {
                vpMatches12[rotHist[i][j]]=static_cast<MapPoint*>(NULL);
                nmatches--;
            }
        }
    }

    return nmatches;
}

// 利用基本矩阵F12，在两个关键帧之间未匹配的特征点中产生新的3d点
int ORBmatcher::SearchForTriangulation(KeyFrame *pKF1, KeyFrame *pKF2, cv::Mat F12,
                                       vector<pair<size_t, size_t> > &vMatchedPairs, const bool bOnlyStereo)
{
    const DBoW2::FeatureVector &vFeatVec1 = pKF1->mFeatVec;
    const DBoW2::FeatureVector &vFeatVec2 = pKF2->mFeatVec;

    // Compute epipole in second image
    // 计算KF1的相机中心在KF2图像平面的坐标，即极点坐标
    cv::Mat Cw = pKF1->GetCameraCenter(); 
    cv::Mat R2w = pKF2->GetRotation();    
    cv::Mat t2w = pKF2->GetTranslation(); 
    cv::Mat C2 = R2w*Cw+t2w; 
    const float invz = 1.0f/C2.at<float>(2);
    // 得到KF1的相机光心在KF2中的坐标
    const float ex =pKF2->fx*C2.at<float>(0)*invz+pKF2->cx;
    const float ey =pKF2->fy*C2.at<float>(1)*invz+pKF2->cy;

    // Find matches between not tracked keypoints
    // Matching speed-up by ORB Vocabulary
    // Compare only ORB that share the same node

    int nmatches=0;
    vector<bool> vbMatched2(pKF2->N,false);
    vector<int> vMatches12(pKF1->N,-1);

    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);

    const float factor = HISTO_LENGTH/360.0f;

    // We perform the matching over ORB that belong to the same vocabulary node (at a certain level)
    DBoW2::FeatureVector::const_iterator f1it = vFeatVec1.begin();
    DBoW2::FeatureVector::const_iterator f2it = vFeatVec2.begin();
    DBoW2::FeatureVector::const_iterator f1end = vFeatVec1.end();
    DBoW2::FeatureVector::const_iterator f2end = vFeatVec2.end();

    // 遍历pKF1和pKF2中的点
    while(f1it!=f1end && f2it!=f2end)
    {
        // 如果f1it和f2it属于同一个node节点
        if(f1it->first == f2it->first)
        {
            // 遍历该node节点下的所有特征点
            for(size_t i1=0, iend1=f1it->second.size(); i1<iend1; i1++)
            {
                // 获取pKF1中属于该node节点的特征点索引
                const size_t idx1 = f1it->second[i1];
                
                // 通过特征点索引idx1在pKF1中取出对应的MapPoint
                MapPoint* pMP1 = pKF1->GetMapPoint(idx1);
                
                // If there is already a MapPoint skip
                if(pMP1)
                    continue;

                const bool bStereo1 = pKF1->mvuRight[idx1]>=0;

                if(bOnlyStereo)
                    if(!bStereo1)
                        continue;
                
                // 通过特征点索引idx1在pKF1中取出对应的特征点
                const cv::KeyPoint &kp1 = pKF1->mvKeysUn[idx1];
                
                // 通过特征点索引idx1在pKF1中取出对应的特征点的描述子
                const cv::Mat &d1 = pKF1->mDescriptors.row(idx1);
                
                int bestDist = TH_LOW;
                int bestIdx2 = -1;
                
                for(size_t i2=0, iend2=f2it->second.size(); i2<iend2; i2++)
                {
                    // 获取pKF2中属于该node节点的特征点索引
                    size_t idx2 = f2it->second[i2];
                    
                    // 通过特征点索引idx2在pKF2中取出对应的MapPoint
                    MapPoint* pMP2 = pKF2->GetMapPoint(idx2);
                    
                    // If we have already matched or there is a MapPoint skip
                    // 如果pKF2当前特征点索引idx2已经被匹配过或者对应的3d点非空
                    // 那么这个索引idx2就不被考虑
                    if(vbMatched2[idx2] || pMP2)
                        continue;

                    const bool bStereo2 = pKF2->mvuRight[idx2]>=0;

                    if(bOnlyStereo)
                        if(!bStereo2)
                            continue;
                    
                    // 通过特征点索引idx2在pKF2中取出对应的特征点的描述子
                    const cv::Mat &d2 = pKF2->mDescriptors.row(idx2);
                    
                    // 计算idx1与idx2在两个关键帧中对应特征点的描述子距离
                    const int dist = DescriptorDistance(d1,d2);
                    
                    if(dist>TH_LOW || dist>bestDist)
                        continue;

                    // 通过特征点索引idx2在pKF2中取出对应的特征点
                    const cv::KeyPoint &kp2 = pKF2->mvKeysUn[idx2];

                    if(!bStereo1 && !bStereo2)
                    {
                        const float distex = ex-kp2.pt.x;
                        const float distey = ey-kp2.pt.y;
                        if(distex*distex+distey*distey<100*pKF2->mvScaleFactors[kp2.octave])
                            continue;
                    }

                    // 计算特征点kp2到kp1极线的距离是否小于阈值
                    if(CheckDistEpipolarLine(kp1,kp2,F12,pKF2))
                    {
                        bestIdx2 = idx2;
                        bestDist = dist;
                    }
                }
                
                if(bestIdx2>=0)
                {
                    const cv::KeyPoint &kp2 = pKF2->mvKeysUn[bestIdx2];
                    vMatches12[idx1]=bestIdx2;
                    vbMatched2[bestIdx2]=true;
                    nmatches++;

                    if(mbCheckOrientation)
                    {
                        float rot = kp1.angle-kp2.angle;
                        if(rot<0.0)
                            rot+=360.0f;
                        int bin = round(rot*factor);
                        if(bin==HISTO_LENGTH)
                            bin=0;
                        assert(bin>=0 && bin<HISTO_LENGTH);
                        rotHist[bin].push_back(idx1);
                    }
                }
            }

            f1it++;
            f2it++;
        }
        else if(f1it->first < f2it->first)
        {
            f1it = vFeatVec1.lower_bound(f2it->first);
        }
        else
        {
            f2it = vFeatVec2.lower_bound(f1it->first);
        }
    }

    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            if(i==ind1 || i==ind2 || i==ind3)
                continue;
            for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
            {
                vMatches12[rotHist[i][j]]=-1;
                nmatches--;
            }
        }

    }

    vMatchedPairs.clear();
    vMatchedPairs.reserve(nmatches);

    for(size_t i=0, iend=vMatches12.size(); i<iend; i++)
    {
        if(vMatches12[i]<0)
            continue;
        vMatchedPairs.push_back(make_pair(i,vMatches12[i]));
    }

    return nmatches;
}

// 将MapPoints投影到关键帧pKF中，并判断是否有重复的MapPoints
int ORBmatcher::Fuse(KeyFrame *pKF, const vector<MapPoint *> &vpMapPoints, const float th)
{
    cv::Mat Rcw = pKF->GetRotation();
    cv::Mat tcw = pKF->GetTranslation();

    const float &fx = pKF->fx;
    const float &fy = pKF->fy;
    const float &cx = pKF->cx;
    const float &cy = pKF->cy;
    const float &bf = pKF->mbf;

    cv::Mat Ow = pKF->GetCameraCenter();

    int nFused=0;

    const int nMPs = vpMapPoints.size();

    // 遍历所有的MapPoints
    for(int i=0; i<nMPs; i++)
    {
        MapPoint* pMP = vpMapPoints[i];

        if(!pMP)
            continue;

        if(pMP->isBad() || pMP->IsInKeyFrame(pKF))
            continue;

		// 地图点转换至相机坐标系
        cv::Mat p3Dw = pMP->GetWorldPos();
        cv::Mat p3Dc = Rcw*p3Dw + tcw;

        // Depth must be positive
		// 检查深度
        if(p3Dc.at<float>(2)<0.0f)
            continue;

        const float invz = 1/p3Dc.at<float>(2);
        const float x = p3Dc.at<float>(0)*invz;
        const float y = p3Dc.at<float>(1)*invz;

        const float u = fx*x+cx;
        const float v = fy*y+cy;	// 得到MapPoint在图像上的投影坐标

        // Point must be inside the image
        if(!pKF->IsInImage(u,v))
            continue;

        const float ur = u-bf*invz;

        const float maxDistance = pMP->GetMaxDistanceInvariance();
        const float minDistance = pMP->GetMinDistanceInvariance();
        cv::Mat PO = p3Dw-Ow;
        const float dist3D = cv::norm(PO);

        // Depth must be inside the scale pyramid of the image
		// 尺度必须合适
        if(dist3D<minDistance || dist3D>maxDistance )
            continue;

        // Viewing angle must be less than 60 deg
		// 检查视角
        cv::Mat Pn = pMP->GetNormal();

        if(PO.dot(Pn)<0.5*dist3D)
            continue;

		// 预测尺度
        int nPredictedLevel = pMP->PredictScale(dist3D,pKF);

        // Search in a radius
		// 根据MapPoint的尺度，从而确定搜索范围
        const float radius = th*pKF->mvScaleFactors[nPredictedLevel];

		// 获得范围内的点
        const vector<size_t> vIndices = pKF->GetFeaturesInArea(u,v,radius);

        if(vIndices.empty())
            continue;

        // Match to the most similar keypoint in the radius

        const cv::Mat dMP = pMP->GetDescriptor();

        int bestDist = 256;
        int bestIdx = -1;
		// 遍历这些点
        for(vector<size_t>::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
        {
            const size_t idx = *vit;

            const cv::KeyPoint &kp = pKF->mvKeysUn[idx];

            const int &kpLevel= kp.octave;

            if(kpLevel<nPredictedLevel-1 || kpLevel>nPredictedLevel)
                continue;

            // 计算MapPoint投影的坐标与这个区域特征点的距离，如果偏差很大，直接跳过特征点匹配
            if(pKF->mvuRight[idx]>=0)
            {
                // Check reprojection error in stereo
                const float &kpx = kp.pt.x;
                const float &kpy = kp.pt.y;
                const float &kpr = pKF->mvuRight[idx];
                const float ex = u-kpx;
                const float ey = v-kpy;
                const float er = ur-kpr;
                const float e2 = ex*ex+ey*ey+er*er;

                if(e2*pKF->mvInvLevelSigma2[kpLevel]>7.8)
                    continue;
            }
            else
            {
                const float &kpx = kp.pt.x;
                const float &kpy = kp.pt.y;
                const float ex = u-kpx;
                const float ey = v-kpy;
                const float e2 = ex*ex+ey*ey;

                if(e2*pKF->mvInvLevelSigma2[kpLevel]>5.99)
                    continue;
            }

            const cv::Mat &dKF = pKF->mDescriptors.row(idx);

            const int dist = DescriptorDistance(dMP,dKF);

            if(dist<bestDist)// 找MapPoint在该区域最佳匹配的特征点
            {
                bestDist = dist;
                bestIdx = idx;
            }
        }

        // If there is already a MapPoint replace otherwise add new measurement
        if(bestDist<=TH_LOW)	// 找到了MapPoint在该区域最佳匹配的特征点
        {
            MapPoint* pMPinKF = pKF->GetMapPoint(bestIdx);
            if(pMPinKF)		// 如果这个点有对应的MapPoint
            {
                if(!pMPinKF->isBad())	// 如果这个MapPoint不是bad，选择被观测次数多的点
                {
                    if(pMPinKF->Observations()>pMP->Observations())
                        pMP->Replace(pMPinKF);
                    else
                        pMPinKF->Replace(pMP);
                }
            }
            else	// 如果这个点没有对应的MapPoint则添加
            {
                pMP->AddObservation(pKF,bestIdx);
                pKF->AddMapPoint(pMP,bestIdx);
            }
            nFused++;
        }
    }

    return nFused;
}

// 投影MapPoints到KeyFrame中，并判断是否有重复的MapPoints
// Scw为世界坐标系到pKF相机坐标系的Sim3变换，用于将世界坐标系下的vpPoints变换到相机坐标系
int ORBmatcher::Fuse(KeyFrame *pKF, cv::Mat Scw, const vector<MapPoint *> &vpPoints, float th, vector<MapPoint *> &vpReplacePoint)
{
    // Get Calibration Parameters for later projection
    const float &fx = pKF->fx;
    const float &fy = pKF->fy;
    const float &cx = pKF->cx;
    const float &cy = pKF->cy;

    // Decompose Scw
    // 将Sim3转化为SE3并分解
    cv::Mat sRcw = Scw.rowRange(0,3).colRange(0,3);
    const float scw = sqrt(sRcw.row(0).dot(sRcw.row(0)));// 计算得到尺度s
    cv::Mat Rcw = sRcw/scw;
    cv::Mat tcw = Scw.rowRange(0,3).col(3)/scw;
    cv::Mat Ow = -Rcw.t()*tcw;

    // Set of MapPoints already found in the KeyFrame
    const set<MapPoint*> spAlreadyFound = pKF->GetMapPoints();

    int nFused=0;

    const int nPoints = vpPoints.size();

    // For each candidate MapPoint project and match
    // 遍历所有的MapPoints
    for(int iMP=0; iMP<nPoints; iMP++)
    {
        MapPoint* pMP = vpPoints[iMP];

        // Discard Bad MapPoints and already found
        if(pMP->isBad() || spAlreadyFound.count(pMP))
            continue;

        // Get 3D Coords.
        cv::Mat p3Dw = pMP->GetWorldPos();

        // Transform into Camera Coords.
        cv::Mat p3Dc = Rcw*p3Dw+tcw;

        // Depth must be positive
        if(p3Dc.at<float>(2)<0.0f)
            continue;

        // Project into Image
        const float invz = 1.0/p3Dc.at<float>(2);
        const float x = p3Dc.at<float>(0)*invz;
        const float y = p3Dc.at<float>(1)*invz;

        const float u = fx*x+cx;
        const float v = fy*y+cy;// 得到MapPoint在图像上的投影坐标

        // Point must be inside the image
        if(!pKF->IsInImage(u,v))
            continue;

        // Depth must be inside the scale pyramid of the image
        const float maxDistance = pMP->GetMaxDistanceInvariance();
        const float minDistance = pMP->GetMinDistanceInvariance();
        cv::Mat PO = p3Dw-Ow;
        const float dist3D = cv::norm(PO);

        if(dist3D<minDistance || dist3D>maxDistance)
            continue;

        // Viewing angle must be less than 60 deg
        cv::Mat Pn = pMP->GetNormal();

        if(PO.dot(Pn)<0.5*dist3D)
            continue;

        // Compute predicted scale level
        const int nPredictedLevel = pMP->PredictScale(dist3D,pKF);

        // Search in a radius
        // 计算搜索范围
        const float radius = th*pKF->mvScaleFactors[nPredictedLevel];

        // pKF在该区域内的特征点
        const vector<size_t> vIndices = pKF->GetFeaturesInArea(u,v,radius);

        if(vIndices.empty())
            continue;

        // Match to the most similar keypoint in the radius

        const cv::Mat dMP = pMP->GetDescriptor();

        int bestDist = INT_MAX;
        int bestIdx = -1;
        for(vector<size_t>::const_iterator vit=vIndices.begin(); vit!=vIndices.end(); vit++)
        {
            const size_t idx = *vit;
            const int &kpLevel = pKF->mvKeysUn[idx].octave;

            if(kpLevel<nPredictedLevel-1 || kpLevel>nPredictedLevel)
                continue;

            const cv::Mat &dKF = pKF->mDescriptors.row(idx);

            int dist = DescriptorDistance(dMP,dKF);

            if(dist<bestDist)
            {
                bestDist = dist;
                bestIdx = idx;
            }
        }

        // If there is already a MapPoint replace otherwise add new measurement
        if(bestDist<=TH_LOW)
        {
            MapPoint* pMPinKF = pKF->GetMapPoint(bestIdx);
            // 如果这个MapPoint已经存在，则替换，
            // 先记录下来，之后调用Replace函数来替换
            if(pMPinKF)
            {
                if(!pMPinKF->isBad())
                    vpReplacePoint[iMP] = pMPinKF;
            }
            else// 如果这个MapPoint不存在，直接添加
            {
                pMP->AddObservation(pKF,bestIdx);
                pKF->AddMapPoint(pMP,bestIdx);
            }
            nFused++;
        }
    }

    return nFused;
}

// 通过Sim3变换，确定pKF1的特征点在pKF2中的大致区域，同理，确定pKF2的特征点在pKF1中的大致区域
// 在该区域内通过描述子进行匹配捕获pKF1和pKF2之前漏匹配的特征点，更新vpMatches12
int ORBmatcher::SearchBySim3(KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint*> &vpMatches12,
                             const float &s12, const cv::Mat &R12, const cv::Mat &t12, const float th)
{
    const float &fx = pKF1->fx;
    const float &fy = pKF1->fy;
    const float &cx = pKF1->cx;
    const float &cy = pKF1->cy;

    // Camera 1 from world
    // 从world到camera的变换
    cv::Mat R1w = pKF1->GetRotation();
    cv::Mat t1w = pKF1->GetTranslation();

    //Camera 2 from world
    cv::Mat R2w = pKF2->GetRotation();
    cv::Mat t2w = pKF2->GetTranslation();

    //Transformation between cameras
    cv::Mat sR12 = s12*R12;
    cv::Mat sR21 = (1.0/s12)*R12.t();
    cv::Mat t21 = -sR21*t12;

    const vector<MapPoint*> vpMapPoints1 = pKF1->GetMapPointMatches();
    const int N1 = vpMapPoints1.size();

    const vector<MapPoint*> vpMapPoints2 = pKF2->GetMapPointMatches();
    const int N2 = vpMapPoints2.size();

    vector<bool> vbAlreadyMatched1(N1,false);
    vector<bool> vbAlreadyMatched2(N2,false);

    // 用vpMatches12更新vbAlreadyMatched1和vbAlreadyMatched2
    for(int i=0; i<N1; i++)
    {
        MapPoint* pMP = vpMatches12[i];
        if(pMP)
        {
            vbAlreadyMatched1[i]=true;// 该特征点已经判断过
            int idx2 = pMP->GetIndexInKeyFrame(pKF2);
            if(idx2>=0 && idx2<N2)
                vbAlreadyMatched2[idx2]=true;// 该特征点在pKF1中有匹配
        }
    }

    vector<int> vnMatch1(N1,-1);
    vector<int> vnMatch2(N2,-1);

    // Transform from KF1 to KF2 and search
    // 通过Sim变换，确定pKF1的特征点在pKF2中的大致区域，
    // 在该区域内通过描述子进行匹配pKF1和pKF2之前漏匹配的特征点，更新vpMatches12
    for(int i1=0; i1<N1; i1++)
    {
        MapPoint* pMP = vpMapPoints1[i1];

        if(!pMP || vbAlreadyMatched1[i1])// 该特征点已经有匹配点了，直接跳过
            continue;

        if(pMP->isBad())
            continue;

        cv::Mat p3Dw = pMP->GetWorldPos();
        cv::Mat p3Dc1 = R1w*p3Dw + t1w;		// 把pKF1系下的MapPoint从world坐标系变换到camera1坐标系
        cv::Mat p3Dc2 = sR21*p3Dc1 + t21;	// 再通过Sim3将该MapPoint从camera1变换到camera2坐标系

        // Depth must be positive
        if(p3Dc2.at<float>(2)<0.0)
            continue;

        // 投影到camera2图像平面
        const float invz = 1.0/p3Dc2.at<float>(2);
        const float x = p3Dc2.at<float>(0)*invz;
        const float y = p3Dc2.at<float>(1)*invz;

        const float u = fx*x+cx;
        const float v = fy*y+cy;

        // Point must be inside the image
        if(!pKF2->IsInImage(u,v))
            continue;

        const float maxDistance = pMP->GetMaxDistanceInvariance();
        const float minDistance = pMP->GetMinDistanceInvariance();
        const float dist3D = cv::norm(p3Dc2);

        // Depth must be inside the scale invariance region
        if(dist3D<minDistance || dist3D>maxDistance )
            continue;

        // Compute predicted octave
        // 预测该MapPoint对应的尺度
        const int nPredictedLevel = pMP->PredictScale(dist3D,pKF2);

        // Search in a radius
        // 计算搜索半径
        const float radius = th*pKF2->mvScaleFactors[nPredictedLevel];

        // 取出该区域内的所有特征点
        const vector<size_t> vIndices = pKF2->GetFeaturesInArea(u,v,radius);

        if(vIndices.empty())
            continue;

        // Match to the most similar keypoint in the radius
        const cv::Mat dMP = pMP->GetDescriptor();

        int bestDist = INT_MAX;
        int bestIdx = -1;
        // 遍历搜索区域内的所有特征点，与pMP进行描述子匹配
        for(vector<size_t>::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
        {
            const size_t idx = *vit;

            const cv::KeyPoint &kp = pKF2->mvKeysUn[idx];

            if(kp.octave<nPredictedLevel-1 || kp.octave>nPredictedLevel)
                continue;

            const cv::Mat &dKF = pKF2->mDescriptors.row(idx);

            const int dist = DescriptorDistance(dMP,dKF);

            if(dist<bestDist)
            {
                bestDist = dist;
                bestIdx = idx;
            }
        }

        if(bestDist<=TH_HIGH)
        {
            vnMatch1[i1]=bestIdx;
        }
    }

    // Transform from KF2 to KF1 and search
    // 通过Sim变换，确定pKF2的特征点在pKF1中的大致区域，
    // 在该区域内通过描述子进行匹配捕获pKF1和pKF2之前漏匹配的特征点，更新vpMatches12
	// 算法过程同上一段
    for(int i2=0; i2<N2; i2++)
    {
        MapPoint* pMP = vpMapPoints2[i2];

        if(!pMP || vbAlreadyMatched2[i2])
            continue;

        if(pMP->isBad())
            continue;

        cv::Mat p3Dw = pMP->GetWorldPos();
        cv::Mat p3Dc2 = R2w*p3Dw + t2w;
        cv::Mat p3Dc1 = sR12*p3Dc2 + t12;

        // Depth must be positive
        if(p3Dc1.at<float>(2)<0.0)
            continue;

        const float invz = 1.0/p3Dc1.at<float>(2);
        const float x = p3Dc1.at<float>(0)*invz;
        const float y = p3Dc1.at<float>(1)*invz;

        const float u = fx*x+cx;
        const float v = fy*y+cy;

        // Point must be inside the image
        if(!pKF1->IsInImage(u,v))
            continue;

        const float maxDistance = pMP->GetMaxDistanceInvariance();
        const float minDistance = pMP->GetMinDistanceInvariance();
        const float dist3D = cv::norm(p3Dc1);

        // Depth must be inside the scale pyramid of the image
        if(dist3D<minDistance || dist3D>maxDistance)
            continue;

        // Compute predicted octave
        const int nPredictedLevel = pMP->PredictScale(dist3D,pKF1);

        // Search in a radius of 2.5*sigma(ScaleLevel)
        const float radius = th*pKF1->mvScaleFactors[nPredictedLevel];

        const vector<size_t> vIndices = pKF1->GetFeaturesInArea(u,v,radius);

        if(vIndices.empty())
            continue;

        // Match to the most similar keypoint in the radius
        const cv::Mat dMP = pMP->GetDescriptor();

        int bestDist = INT_MAX;
        int bestIdx = -1;
        for(vector<size_t>::const_iterator vit=vIndices.begin(), vend=vIndices.end(); vit!=vend; vit++)
        {
            const size_t idx = *vit;

            const cv::KeyPoint &kp = pKF1->mvKeysUn[idx];

            if(kp.octave<nPredictedLevel-1 || kp.octave>nPredictedLevel)
                continue;

            const cv::Mat &dKF = pKF1->mDescriptors.row(idx);

            const int dist = DescriptorDistance(dMP,dKF);

            if(dist<bestDist)
            {
                bestDist = dist;
                bestIdx = idx;
            }
        }

        if(bestDist<=TH_HIGH)
        {
            vnMatch2[i2]=bestIdx;
        }
    }

    // Check agreement
    int nFound = 0;

    for(int i1=0; i1<N1; i1++)
    {
        int idx2 = vnMatch1[i1];

        if(idx2>=0)
        {
            int idx1 = vnMatch2[idx2];
            if(idx1==i1)	// KF1中的某个点和KF2中的某个点之间互相都能匹配上
            {
                vpMatches12[i1] = vpMapPoints2[idx2];
                nFound++;
            }
        }
    }

    return nFound;
}

// 通过投影，对上一帧的特征点进行跟踪
int ORBmatcher::SearchByProjection(Frame &CurrentFrame, const Frame &LastFrame, const float th, const bool bMono)
{
    int nmatches = 0;

    // Rotation Histogram (to check rotation consistency)
    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);
    const float factor = HISTO_LENGTH/360.0f;

    const cv::Mat Rcw = CurrentFrame.mTcw.rowRange(0,3).colRange(0,3);
    const cv::Mat tcw = CurrentFrame.mTcw.rowRange(0,3).col(3);

    const cv::Mat twc = -Rcw.t()*tcw; // twc(w)

    const cv::Mat Rlw = LastFrame.mTcw.rowRange(0,3).colRange(0,3);
    const cv::Mat tlw = LastFrame.mTcw.rowRange(0,3).col(3); // tlw(l)

    // vector from LastFrame to CurrentFrame expressed in LastFrame
    const cv::Mat tlc = Rlw*twc+tlw; // Rlw*twc(w) = twc(l), twc(l) + tlw(l) = tlc(l)

    const bool bForward = tlc.at<float>(2)>CurrentFrame.mb && !bMono; 
    const bool bBackward = -tlc.at<float>(2)>CurrentFrame.mb && !bMono; 

    for(int i=0; i<LastFrame.N; i++)
    {
        MapPoint* pMP = LastFrame.mvpMapPoints[i];

        if(pMP)
        {
            if(!LastFrame.mvbOutlier[i])
            {
                // 对上一帧有效的MapPoints进行跟踪
                // Project
                cv::Mat x3Dw = pMP->GetWorldPos();
                cv::Mat x3Dc = Rcw*x3Dw+tcw;

				// 投影至当前帧
                const float xc = x3Dc.at<float>(0);
                const float yc = x3Dc.at<float>(1);
                const float invzc = 1.0/x3Dc.at<float>(2);

                if(invzc<0)
                    continue;

                float u = CurrentFrame.fx*xc*invzc+CurrentFrame.cx;
                float v = CurrentFrame.fy*yc*invzc+CurrentFrame.cy;

                if(u<CurrentFrame.mnMinX || u>CurrentFrame.mnMaxX)
                    continue;
                if(v<CurrentFrame.mnMinY || v>CurrentFrame.mnMaxY)
                    continue;

                int nLastOctave = LastFrame.mvKeys[i].octave;

                // Search in a window. Size depends on scale
                float radius = th*CurrentFrame.mvScaleFactors[nLastOctave]; // 尺度越大，搜索范围越大

                vector<size_t> vIndices2;

                if(bForward) 	
                    vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, nLastOctave);
                else if(bBackward) 
                    vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, 0, nLastOctave);
                else 
                    vIndices2 = CurrentFrame.GetFeaturesInArea(u,v, radius, nLastOctave-1, nLastOctave+1);

                if(vIndices2.empty())
                    continue;

                const cv::Mat dMP = pMP->GetDescriptor();

                int bestDist = 256;
                int bestIdx2 = -1;

                // 遍历满足条件的特征点
                for(vector<size_t>::const_iterator vit=vIndices2.begin(), vend=vIndices2.end(); vit!=vend; vit++)
                {
                    // 如果该特征点已经有对应的MapPoint了,则退出该次循环
                    const size_t i2 = *vit;
                    if(CurrentFrame.mvpMapPoints[i2])
                        if(CurrentFrame.mvpMapPoints[i2]->Observations()>0)
                            continue;

                    if(CurrentFrame.mvuRight[i2]>0)
                    {
                        // 双目和rgbd的情况，需要保证右图的点也在搜索半径以内
                        const float ur = u - CurrentFrame.mbf*invzc;
                        const float er = fabs(ur - CurrentFrame.mvuRight[i2]);
                        if(er>radius)
                            continue;
                    }

                    const cv::Mat &d = CurrentFrame.mDescriptors.row(i2);

                    const int dist = DescriptorDistance(dMP,d);

                    if(dist<bestDist)
                    {
                        bestDist=dist;
                        bestIdx2=i2;
                    }
                }

                if(bestDist<=TH_HIGH)
                {
                    CurrentFrame.mvpMapPoints[bestIdx2]=pMP; // 为当前帧添加MapPoint
                    nmatches++;

                    if(mbCheckOrientation)
                    {
                        float rot = LastFrame.mvKeysUn[i].angle-CurrentFrame.mvKeysUn[bestIdx2].angle;
                        if(rot<0.0)
                            rot+=360.0f;
                        int bin = round(rot*factor);
                        if(bin==HISTO_LENGTH)
                            bin=0;
                        assert(bin>=0 && bin<HISTO_LENGTH);
                        rotHist[bin].push_back(bestIdx2);
                    }
                }
            }
        }
    }

    //Apply rotation consistency
    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            if(i!=ind1 && i!=ind2 && i!=ind3)
            {
                for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
                {
                    CurrentFrame.mvpMapPoints[rotHist[i][j]]=static_cast<MapPoint*>(NULL);
                    nmatches--;
                }
            }
        }
    }

    return nmatches;
}
// 将KF中的特征点投影到当前帧中进行匹配，增加当前帧的地图点，即跟踪kF
int ORBmatcher::SearchByProjection(Frame &CurrentFrame, KeyFrame *pKF, const set<MapPoint*> &sAlreadyFound, const float th , const int ORBdist)
{
    int nmatches = 0;

    const cv::Mat Rcw = CurrentFrame.mTcw.rowRange(0,3).colRange(0,3);
    const cv::Mat tcw = CurrentFrame.mTcw.rowRange(0,3).col(3);
    const cv::Mat Ow = -Rcw.t()*tcw;

    // Rotation Histogram (to check rotation consistency)
    vector<int> rotHist[HISTO_LENGTH];
    for(int i=0;i<HISTO_LENGTH;i++)
        rotHist[i].reserve(500);
    const float factor = HISTO_LENGTH/360.0f;

    const vector<MapPoint*> vpMPs = pKF->GetMapPointMatches();

    for(size_t i=0, iend=vpMPs.size(); i<iend; i++)
    {
        MapPoint* pMP = vpMPs[i];

        if(pMP)
        {
            if(!pMP->isBad() && !sAlreadyFound.count(pMP))
            {
                //Project
                cv::Mat x3Dw = pMP->GetWorldPos();
                cv::Mat x3Dc = Rcw*x3Dw+tcw;

                const float xc = x3Dc.at<float>(0);
                const float yc = x3Dc.at<float>(1);
                const float invzc = 1.0/x3Dc.at<float>(2);

                const float u = CurrentFrame.fx*xc*invzc+CurrentFrame.cx;
                const float v = CurrentFrame.fy*yc*invzc+CurrentFrame.cy;

                if(u<CurrentFrame.mnMinX || u>CurrentFrame.mnMaxX)
                    continue;
                if(v<CurrentFrame.mnMinY || v>CurrentFrame.mnMaxY)
                    continue;

                // Compute predicted scale level
                cv::Mat PO = x3Dw-Ow;
                float dist3D = cv::norm(PO);

                const float maxDistance = pMP->GetMaxDistanceInvariance();
                const float minDistance = pMP->GetMinDistanceInvariance();

                // Depth must be inside the scale pyramid of the image
                if(dist3D<minDistance || dist3D>maxDistance)
                    continue;

                int nPredictedLevel = pMP->PredictScale(dist3D,&CurrentFrame);

                // Search in a window
                const float radius = th*CurrentFrame.mvScaleFactors[nPredictedLevel];

                const vector<size_t> vIndices2 = CurrentFrame.GetFeaturesInArea(u, v, radius, nPredictedLevel-1, nPredictedLevel+1);

                if(vIndices2.empty())
                    continue;

                const cv::Mat dMP = pMP->GetDescriptor();

                int bestDist = 256;
                int bestIdx2 = -1;

                for(vector<size_t>::const_iterator vit=vIndices2.begin(); vit!=vIndices2.end(); vit++)
                {
                    const size_t i2 = *vit;
                    if(CurrentFrame.mvpMapPoints[i2])
                        continue;

                    const cv::Mat &d = CurrentFrame.mDescriptors.row(i2);

                    const int dist = DescriptorDistance(dMP,d);

                    if(dist<bestDist)
                    {
                        bestDist=dist;
                        bestIdx2=i2;
                    }
                }

                if(bestDist<=ORBdist)
                {
                    CurrentFrame.mvpMapPoints[bestIdx2]=pMP;
                    nmatches++;

                    if(mbCheckOrientation)
                    {
                        float rot = pKF->mvKeysUn[i].angle-CurrentFrame.mvKeysUn[bestIdx2].angle;
                        if(rot<0.0)
                            rot+=360.0f;
                        int bin = round(rot*factor);
                        if(bin==HISTO_LENGTH)
                            bin=0;
                        assert(bin>=0 && bin<HISTO_LENGTH);
                        rotHist[bin].push_back(bestIdx2);
                    }
                }

            }
        }
    }

    if(mbCheckOrientation)
    {
        int ind1=-1;
        int ind2=-1;
        int ind3=-1;

        ComputeThreeMaxima(rotHist,HISTO_LENGTH,ind1,ind2,ind3);

        for(int i=0; i<HISTO_LENGTH; i++)
        {
            if(i!=ind1 && i!=ind2 && i!=ind3)
            {
                for(size_t j=0, jend=rotHist[i].size(); j<jend; j++)
                {
                    CurrentFrame.mvpMapPoints[rotHist[i][j]]=NULL;
                    nmatches--;
                }
            }
        }
    }

    return nmatches;
}

// 取出最大的三个index
void ORBmatcher::ComputeThreeMaxima(vector<int>* histo, const int L, int &ind1, int &ind2, int &ind3)
{
    int max1=0;
    int max2=0;
    int max3=0;

    for(int i=0; i<L; i++)
    {
        const int s = histo[i].size();
        if(s>max1)
        {
            max3=max2;
            max2=max1;
            max1=s;
            ind3=ind2;
            ind2=ind1;
            ind1=i;
        }
        else if(s>max2)
        {
            max3=max2;
            max2=s;
            ind3=ind2;
            ind2=i;
        }
        else if(s>max3)
        {
            max3=s;
            ind3=i;
        }
    }

    if(max2<0.1f*(float)max1)
    {
        ind2=-1;
        ind3=-1;
    }
    else if(max3<0.1f*(float)max1)
    {
        ind3=-1;
    }
}

int ORBmatcher::DescriptorDistance(const cv::Mat &a, const cv::Mat &b)
{
    const int *pa = a.ptr<int32_t>();
    const int *pb = b.ptr<int32_t>();

    int dist=0;

    for(int i=0; i<8; i++, pa++, pb++)
    {
        unsigned  int v = *pa ^ *pb;
        v = v - ((v >> 1) & 0x55555555);
        v = (v & 0x33333333) + ((v >> 2) & 0x33333333);
        dist += (((v + (v >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24;
    }

    return dist;
}

} //namespace ORB_SLAM

代码有点长。。。

总结下里面几个重要的函数，其实很多步骤大体相同

• int ORBmatcher::SearchByProjection(Frame &F, const vector<MapPoint*> &vpMapPoints, const float th);  通过投影对local map中的点进行跟踪

1. 首先判断点是否需要投影及是否是坏点
2. 由距离预测金字塔层数
3. 由视角计算搜索半径，当前视角越接*均视角，则搜索半径越小
4. 将地图点投影至当前帧，根据2、3步的值找到一个范围内的所有特征点
5. 遍历范围内的点：计算点和地图点描述子的距离，距离最好距离和次好距离及对应索引

• 若最好距离满足阈值且和次好距离有明显区别，则为当前帧添加地图点int ORBmatcher::SearchByBoW(KeyFrame* pKF,Frame &F, vector<MapPoint*> &vpMapPointMatches); 通过BoW对关键帧和普通帧中的点进行匹配

1. 只对属于同一节点的特征点进行匹配
2. 对属于同一节点的关键帧和普通帧的特征点遍历计算描述子距离，记录最好距离和次好距离
3. 若最好距离满足阈值且明显优于次好距离，则更新特征点对应的匹配点（地图点）
4. 上一步得到一对匹配的点，再计算两个点的角度变化值，分配到容器中，即统计所有匹配后的点的角度变化值，不同角度值分配到不同组中
5. 计算所有角度变化组中数量最大的三个组，若前面匹配好的点的角度变化在这三个组中则保留，否则剔除，即认为特征点的角度变化应该是一致的

• int ORBmatcher::SearchByProjection(KeyFrame* pKF, cv::Mat Scw, const vector<MapPoint*> &vpPoints, vector<MapPoint*> &vpMatched, int th); 根据相似变换将给定地图点投影到关键帧上进行匹配

1. 分解相似变换矩阵计算尺度和旋转、平移
2. 对于每一个地图点，投影到像素坐标上，检查：是否是坏点、是否已匹配、深度是否为正、是否在图像内、距离是否合适、视角大小
3. 距离预测尺度à确定搜索半径à得到一个范围内的特征点
4. 遍历这些特征点，计算其和地图点的描述子距离，记录相关信息
5. 最好距离满足阈值，则添加匹配

• int ORBmatcher::SearchForInitialization(Frame &F1, Frame &F2, vector<cv::Point2f> &vbPrevMatched, vector<int> &vnMatches12, int windowSize);  初始化时的搜索匹配，两个普通帧间的匹配

1. 在第二帧中设置搜索范围，获取范围内的特征点
2. 遍历范围内的点，计算描述子距离，记录相关信息
3. 根据最好距离和次好距离以及阈值的关系，记录匹配，记录特征点角度变化
4. 根据角度变化剔除不好的匹配

• int ORBmatcher::SearchByBoW(KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint *> &vpMatches12); 通过BoW对两个关键帧之间的特征点进行匹配

1. 都是对四叉树中属于相同节点的特征点才考虑匹配，首先对KF1中某个节点下的特征点，遍历KF2中对应节点的所有特征点，计算描述子距离并记录信息
2. 根据距离和阈值关系添加匹配，记录角度变化
3. 根据角度变化剔除误匹配  

•  int ORBmatcher::SearchForTriangulation(KeyFrame *pKF1, KeyFrame *pKF2, cv::Mat F12,vector<pair<size_t, size_t> > &vMatchedPairs, const bool bOnlyStereo);  利用基本矩阵，在两个关键帧中产生新的3D点

1. 同样是对同一节点中的特征点进行遍历，跳过已经有对应地图点的点，因为这个函数的目的是为了三角化进行的搜索匹配
2. 计算描述子距离，检查特征点距离极线的距离，满足要求的记录信息
3. 进行角度变化的统计和检查

• int ORBmatcher::Fuse(KeyFrame *pKF, const vector<MapPoint *> &vpMapPoints, const float th); 将地图点投影到关键帧，判断是否有重复的地图点

1. 遍历地图点，将其转换至相机坐标系检查深度，投影并检查是否在图像内，检查尺度和视角范围是否合适
2. 根据距离预测尺度从而确定搜索半径，获得范围内的特征点
3. 遍历这些点，计算其和地图点投影后的坐标误差，误差太大的跳过，之后计算描述子距离，记录信息
4. 对于最佳匹配，如果某个点已经有了地图点，则选择观测次数多的那个点，舍弃观测次数少的点，即两点融合；如果没有对应的地图点，则添加地图点，添加观测关系。

• int ORBmatcher::Fuse(KeyFrame *pKF, cv::Mat Scw, const vector<MapPoint *> &vpPoints, float th, vector<MapPoint *> &vpReplacePoint); 同上

1. 分解相似变换矩阵，得到相关量
2. 对每一个地图点投影到相机系和图像系，检查深度、视角等信息
3. 由距离预测尺度进而确定搜索半径，在指定区域内获取特征点
4. 遍历这些特征点，计算其和地图点的描述子距离，计录最好距离和索引
5. 如果索引对应的地图点不存在则新添加一个，存在的话则标记其将要被替换

• int ORBmatcher::SearchBySim3(KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint*> &vpMatches12,const float &s12, const cv::Mat &R12, const cv::Mat &t12, const float th); 根据相似变换匹配两个关键帧中的特征点，主要是匹配之前漏匹配的点

1. 这个函数将两帧互相进行了匹配，根据相似变换确定将KF1中的特征点投影到KF2中，设定搜索范围，对范围内的特征点进行匹配；同样将KF2中的点投影到KF1中进行了匹配，这两个过程相同
2. 最后有一个检查的条件，某一对匹配点在KF1至KF2的匹配及KF2至KF1的匹配都存在时才认为是有效的匹配

• int ORBmatcher::SearchByProjection(Frame &CurrentFrame, const Frame &LastFrame, const float th, const bool bMono); 通过投影对上一帧对应的地图点进行跟踪

1. 将上一帧的有效的点投影至当前帧，根据相机的前进或后退，在当前帧的不同层的金字塔图像中获取指定范围的特征点
2. 对于范围内的没有地图点的特征点，计算其和上一帧对应点的描述子距离，记录信息，并统计角度变化
3. 根据角度变化剔除误匹配

• int ORBmatcher::SearchByProjection(Frame &CurrentFrame, KeyFrame *pKF, const set<MapPoint*> &sAlreadyFound, const float th , const int ORBdist); 将KF中的特征点投影到当前帧中进行匹配，增加当前帧的地图点

1. 对于关键帧中每一个不是坏点且没有被匹配的地图点：投影至当前帧检查距离等信息
2. 在预测范围内获取当前帧的特征点，遍历这些点计算其和KF中点的描述子距离并记录信息，统计角度变化，最后根据角度剔除误匹配

上面的总结是每个函数内部大体做了什么，有些多余，因为很多函数匹配步骤大致相似，基本思路都是根据距离预测尺度，进而根据尺度确定一个合理的搜索范围，之后对这个范围内的点进行匹配。同一类的只需要仔细看一个函数即可，没必要都看一遍，重要的是搞清楚在什么情况下调用这些不同的函数。