ORB-SLAM2的源码阅读（三）：Frame类

看到网上介绍ORB-SLAM2，基本上介绍一个系统的框架，或者按照线程，Tracking,Local Mapping, Loop Closing，介绍大致流程，但是细节讲的不是很多，只能说懂个大概原理。LZ还是自己慢慢阅读吧，虽然这样的代码注释有点啰嗦，请高手勿喷^_^

在ORB-SLAM2中，头文件中作者都写的很清楚各个函数的定义，这个LZ就不在赘述了。

#ifndef FRAME_H
#define FRAME_H

#include

#include "MapPoint.h"
#include "Thirdparty/DBoW2/DBoW2/BowVector.h"
#include "Thirdparty/DBoW2/DBoW2/FeatureVector.h"
#include "ORBVocabulary.h"
#include "KeyFrame.h"
#include "ORBextractor.h"

#include 

namespace ORB_SLAM2
{
#define FRAME_GRID_ROWS 48
#define FRAME_GRID_COLS 64

class MapPoint;
class KeyFrame;

class Frame
{
public:
    Frame();

    // Copy constructor.
    Frame(const Frame &frame);

    // Constructor for stereo cameras.
    Frame(const cv::Mat &imLeft, const cv::Mat &imRight, const double &timeStamp, ORBextractor* extractorLeft, ORBextractor* extractorRight, ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth);

    // Constructor for RGB-D cameras.
    Frame(const cv::Mat &imGray, const cv::Mat &imDepth, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth);

    // Constructor for Monocular cameras.
    Frame(const cv::Mat &imGray, const double &timeStamp, ORBextractor* extractor,ORBVocabulary* voc, cv::Mat &K, cv::Mat &distCoef, const float &bf, const float &thDepth);

    // Extract ORB on the image. 0 for left image and 1 for right image.
    void ExtractORB(int flag, const cv::Mat &im);

    // Compute Bag of Words representation.
    void ComputeBoW();

    // Set the camera pose.
    void SetPose(cv::Mat Tcw);

    // Computes rotation, translation and camera center matrices from the camera pose.
    void UpdatePoseMatrices();

    // Returns the camera center.
    inline cv::Mat GetCameraCenter(){
        return mOw.clone();
    }

    // Returns inverse of rotation
    inline cv::Mat GetRotationInverse(){
        return mRwc.clone();
    }

    // Check if a MapPoint is in the frustum of the camera
    // and fill variables of the MapPoint to be used by the tracking
    bool isInFrustum(MapPoint* pMP, float viewingCosLimit);

    // Compute the cell of a keypoint (return false if outside the grid)
    bool PosInGrid(const cv::KeyPoint &kp, int &posX, int &posY);

    vector GetFeaturesInArea(const float &x, const float  &y, const float  &r, const int minLevel=-1, const int maxLevel=-1) const;

    // Search a match for each keypoint in the left image to a keypoint in the right image.
    // If there is a match, depth is computed and the right coordinate associated to the left keypoint is stored.
    void ComputeStereoMatches();

    // Associate a "right" coordinate to a keypoint if there is valid depth in the depthmap.
    void ComputeStereoFromRGBD(const cv::Mat &imDepth);

    // Backprojects a keypoint (if stereo/depth info available) into 3D world coordinates.
    cv::Mat UnprojectStereo(const int &i);

public:
    // Vocabulary used for relocalization.
    ORBVocabulary* mpORBvocabulary;

    // Feature extractor. The right is used only in the stereo case.
    ORBextractor* mpORBextractorLeft, *mpORBextractorRight;

    // Frame timestamp.
    double mTimeStamp;

    // Calibration matrix and OpenCV distortion parameters.
    cv::Mat mK;
    static float fx;
    static float fy;
    static float cx;
    static float cy;
    static float invfx;
    static float invfy;
    cv::Mat mDistCoef;

    // Stereo baseline multiplied by fx.
    float mbf;

    // Stereo baseline in meters.
    float mb;

    // Threshold close/far points. Close points are inserted from 1 view.
    // Far points are inserted as in the monocular case from 2 views.
    float mThDepth;

    // Number of KeyPoints.
    int N;

    // Vector of keypoints (original for visualization) and undistorted (actually used by the system).
    // In the stereo case, mvKeysUn is redundant as images must be rectified.
    // In the RGB-D case, RGB images can be distorted.
    std::vector mvKeys, mvKeysRight;
    std::vector mvKeysUn;

    // Corresponding stereo coordinate and depth for each keypoint.
    // "Monocular" keypoints have a negative value.
    std::vector<float> mvuRight;
    std::vector<float> mvDepth;

    // Bag of Words Vector structures.
    DBoW2::BowVector mBowVec;
    DBoW2::FeatureVector mFeatVec;

    // ORB descriptor, each row associated to a keypoint.
    cv::Mat mDescriptors, mDescriptorsRight;

    // MapPoints associated to keypoints, NULL pointer if no association.
    std::vector mvpMapPoints;

    // Flag to identify outlier associations.
    std::vector<bool> mvbOutlier;

    // Keypoints are assigned to cells in a grid to reduce matching complexity when projecting MapPoints.
    static float mfGridElementWidthInv;
    static float mfGridElementHeightInv;
    std::vector<std::size_t> mGrid[FRAME_GRID_COLS][FRAME_GRID_ROWS];

    // Camera pose.
    cv::Mat mTcw;

    // Current and Next Frame id.
    static long unsigned int nNextId;
    long unsigned int mnId;

    // Reference Keyframe.
    KeyFrame* mpReferenceKF;

    // Scale pyramid info.
    int mnScaleLevels;
    float mfScaleFactor;
    float mfLogScaleFactor;
    vector<float> mvScaleFactors;
    vector<float> mvInvScaleFactors;
    vector<float> mvLevelSigma2;
    vector<float> mvInvLevelSigma2;

    // Undistorted Image Bounds (computed once).
    static float mnMinX;
    static float mnMaxX;
    static float mnMinY;
    static float mnMaxY;

    static bool mbInitialComputations;


private:

    // Undistort keypoints given OpenCV distortion parameters.
    // Only for the RGB-D case. Stereo must be already rectified!
    // (called in the constructor).
    void UndistortKeyPoints();

    // Computes image bounds for the undistorted image (called in the constructor).
    void ComputeImageBounds(const cv::Mat &imLeft);

    // Assign keypoints to the grid for speed up feature matching (called in the constructor).
    void AssignFeaturesToGrid();

    // Rotation, translation and camera center
    cv::Mat mRcw;
    cv::Mat mtcw;
    cv::Mat mRwc;
    cv::Mat mOw; //==mtwc
};

}// namespace ORB_SLAM

#endif // FRAME_H

#include "Frame.h"
#include "Converter.h"
#include "ORBmatcher.h"
#include 

namespace ORB_SLAM2
{

long unsigned int Frame::nNextId=0;
bool Frame::mbInitialComputations=true;
float Frame::cx, Frame::cy, Frame::fx, Frame::fy, Frame::invfx, Frame::invfy;
float Frame::mnMinX, Frame::mnMinY, Frame::mnMaxX, Frame::mnMaxY;
float Frame::mfGridElementWidthInv, Frame::mfGridElementHeightInv;

Frame::Frame()
{}

//Copy Constructor
Frame::Frame(const Frame &frame)
    :mpORBvocabulary(frame.mpORBvocabulary), mpORBextractorLeft(frame.mpORBextractorLeft), mpORBextractorRight(frame.mpORBextractorRight),
     mTimeStamp(frame.mTimeStamp), mK(frame.mK.clone()), mDistCoef(frame.mDistCoef.clone()),
     mbf(frame.mbf), mb(frame.mb), mThDepth(frame.mThDepth), N(frame.N), mvKeys(frame.mvKeys),
     mvKeysRight(frame.mvKeysRight), mvKeysUn(frame.mvKeysUn),  mvuRight(frame.mvuRight),
     mvDepth(frame.mvDepth), mBowVec(frame.mBowVec), mFeatVec(frame.mFeatVec),
     mDescriptors(frame.mDescriptors.clone()), mDescriptorsRight(frame.mDescriptorsRight.clone()),
     mvpMapPoints(frame.mvpMapPoints), mvbOutlier(frame.mvbOutlier), mnId(frame.mnId),
     mpReferenceKF(frame.mpReferenceKF), mnScaleLevels(frame.mnScaleLevels),
     mfScaleFactor(frame.mfScaleFactor), mfLogScaleFactor(frame.mfLogScaleFactor),
     mvScaleFactors(frame.mvScaleFactors), mvInvScaleFactors(frame.mvInvScaleFactors),
     mvLevelSigma2(frame.mvLevelSigma2), mvInvLevelSigma2(frame.mvInvLevelSigma2)
{
    for(int i=0;ifor(int j=0; jif(!frame.mTcw.empty())
        SetPose(frame.mTcw);
}

//双目构建的Frame对象

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

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

    // ORB extraction
    //提取特征加入双线程同步提取，0,1代表左目和右目
    //两张提取的特征点会放在不同的vector中
    //对单目和RGBD来说，右目不用，以左为准
    thread threadLeft(&Frame::ExtractORB,this,0,imLeft);
    thread threadRight(&Frame::ExtractORB,this,1,imRight);
    //该函数在线程执行完成是返回
    //在调用这个函数之后，线程对象变为不可连接的并且可以被安全地销毁
    threadLeft.join();
    threadRight.join();

    // N为特征点的数量
    N = mvKeys.size();

    //如果提取的特征点数目为0，则直接返回
    if(mvKeys.empty())
        return;

    UndistortKeyPoints();

    ComputeStereoMatches();

    //初始化地图点及其外点
    mvpMapPoints = vector(N,static_cast(NULL));    
    mvbOutlier = vector<bool>(N,false);


    // This is done only for the first Frame (or after a change in the calibration)
    if(mbInitialComputations)
    {
        ComputeImageBounds(imLeft);

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

//RGBD构建的Frame对象，基本上和双目类似,只需要恢复出右图深度为正的深度即可
Frame::Frame(const cv::Mat &imGray, const cv::Mat &imDepth, 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(NULL)),
     mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth)
{
    // Frame ID
    mnId=nNextId++;

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

    // ORB extraction
    ExtractORB(0,imGray);

    N = mvKeys.size();

    if(mvKeys.empty())
        return;

    UndistortKeyPoints();

    ComputeStereoFromRGBD(imDepth);

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

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

        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();
}

//单目构建的Frame类，和双目类似，但是不包含匹配信息
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(NULL)),
     mTimeStamp(timeStamp), mK(K.clone()),mDistCoef(distCoef.clone()), mbf(bf), mThDepth(thDepth)
{
    // Frame ID
    mnId=nNextId++;

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

    // ORB extraction
    ExtractORB(0,imGray);

    N = mvKeys.size();

    if(mvKeys.empty())
        return;

    UndistortKeyPoints();

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

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

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

        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();
}

//将关键点分布到64*48分割而成的网格中，为了加速匹配和均匀化关键点分布
void Frame::AssignFeaturesToGrid()
{
    //这里为什么要乘以0.5f？
    int nReserve = 0.5f*N/(FRAME_GRID_COLS*FRAME_GRID_ROWS);
    for(unsigned int i=0; ifor (unsigned int j=0; jfor(int i=0;iconst cv::KeyPoint &kp = mvKeysUn[i];

        int nGridPosX, nGridPosY;
        if(PosInGrid(kp,nGridPosX,nGridPosY))
            mGrid[nGridPosX][nGridPosY].push_back(i);
    }
}

//对输入的图像提取0RB的特征
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);
}

void Frame::SetPose(cv::Mat Tcw)
{
    mTcw = Tcw.clone();
    UpdatePoseMatrices();
}

void Frame::UpdatePoseMatrices()
{ 
    mRcw = mTcw.rowRange(0,3).colRange(0,3);
    mRwc = mRcw.t();
    mtcw = mTcw.rowRange(0,3).col(3);
    mOw = -mRcw.t()*mtcw;
}

//设置当前帧的姿态，并更新当前帧相机在世界坐标系下的位姿，中心点位置；
// 判断一个MapPoint是否在视角范围内
bool Frame::isInFrustum(MapPoint *pMP, float viewingCosLimit)
{
    //注意这里的MapPoint是从SearchLocalPoint传递进来的，具备一定信息量
    pMP->mbTrackInView = false;

    // 3D in absolute coordinates
    cv::Mat P = pMP->GetWorldPos(); 

    // 3D in camera coordinates
    const cv::Mat Pc = mRcw*P+mtcw; //这里的R，t是经过初步优化后
    const float &PcX = Pc.at<float>(0);
    const float &PcY= Pc.at<float>(1);
    const float &PcZ = Pc.at<float>(2);

    // Check positive depth
    if(PcZ<0.0f)
        return false;

    // Project in image and check it is not outside
    const float invz = 1.0f/PcZ;
    const float u=fx*PcX*invz+cx;
    const float v=fy*PcY*invz+cy;

    if(umnMaxX)
        return false;
    if(vmnMaxY)
        return false;

    // Check distance is in the scale invariance region of the MapPoint
    // 每一个地图点都是对应于若干尺度的金字塔提取出来的，具有一定的有效深度，
    // 如果相对当前帧的深度超过此范围，返回false
    const float maxDistance = pMP->GetMaxDistanceInvariance();
    const float minDistance = pMP->GetMinDistanceInvariance();
    // 世界坐标系下，相机到3D点P的向量，向量方向由相机只想3D点P
    const cv::Mat PO = P-mOw;
    const float dist = cv::norm(PO);

    if(distmaxDistance)
        return false;

   // Check viewing angle
    // 每一个地图都有其平均视角，是从能够观测到地图点的帧位姿中计算出
    // 如果当前帧的视角和其平均视角相差太大，返回False
    cv::Mat Pn = pMP->GetNormal();//|Pn| = 1

    const float viewCos = PO.dot(Pn)/dist; // =P0.dot(Pn)/(|P0|*|Pn|);|P0| = dist

    if(viewCosreturn false;

    // Predict scale in the image
    //根据深度预测尺度，对应特征点在一层
    const int nPredictedLevel = pMP->PredictScale(dist,this); //|pn| = 1

    // Data used by the tracking
    //标记该点要被投影
    pMP->mbTrackInView = true;
    pMP->mTrackProjX = u;
    pMP->mTrackProjXR = u - mbf*invz;//该3D点投影到双目右侧相机的横坐标
    pMP->mTrackProjY = v;
    pMP->mnTrackScaleLevel= nPredictedLevel;
    pMP->mTrackViewCos = viewCos;

    return true;
}

//在某块区域内获取特征点
//minLevel和maxLevel考察特征点是从图像金字塔的哪一层提取出来的
vector Frame::GetFeaturesInArea(const float &x, const float  &y, const float  &r, const int minLevel, const int maxLevel) const
{
    vector vIndices;
    vIndices.reserve(N);

    const int nMinCellX = max(0,(int)floor((x-mnMinX-r)*mfGridElementWidthInv));
    if(nMinCellX>=FRAME_GRID_COLS)
        return vIndices;

    const int nMaxCellX = min((int)FRAME_GRID_COLS-1,(int)ceil((x-mnMinX+r)*mfGridElementWidthInv));
    if(nMaxCellX<0)
        return vIndices;

    const int nMinCellY = max(0,(int)floor((y-mnMinY-r)*mfGridElementHeightInv));
    if(nMinCellY>=FRAME_GRID_ROWS)
        return vIndices;

    const int nMaxCellY = min((int)FRAME_GRID_ROWS-1,(int)ceil((y-mnMinY+r)*mfGridElementHeightInv));
    if(nMaxCellY<0)
        return vIndices;

    const bool bCheckLevels = (minLevel>0) || (maxLevel>=0);

    for(int ix = nMinCellX; ix<=nMaxCellX; ix++)
    {
        for(int iy = nMinCellY; iy<=nMaxCellY; iy++)
        {
            const vector vCell = mGrid[ix][iy];
            if(vCell.empty())
                continue;

            for(size_t j=0, jend=vCell.size(); jconst cv::KeyPoint &kpUn = mvKeysUn[vCell[j]];
                if(bCheckLevels)
                {
                    if(kpUn.octavecontinue;
                    if(maxLevel>=0)
                        if(kpUn.octave>maxLevel)
                            continue;
                }

                const float distx = kpUn.pt.x-x;
                const float disty = kpUn.pt.y-y;

                if(fabs(distx)fabs(disty)return vIndices;
}

//判断特征点是否在区域内
bool Frame::PosInGrid(const cv::KeyPoint &kp, int &posX, int &posY)
{
    posX = round((kp.pt.x-mnMinX)*mfGridElementWidthInv);
    posY = round((kp.pt.y-mnMinY)*mfGridElementHeightInv);

    //Keypoint's coordinates are undistorted, which could cause to go out of the image
    if(posX<0 || posX>=FRAME_GRID_COLS || posY<0 || posY>=FRAME_GRID_ROWS)
        return false;

    return true;
}

//将当前帧的描述子矩阵（可以转换成向量）转换成词袋模型向量
void Frame::ComputeBoW()
{
    if(mBowVec.empty())
    {
        vector vCurrentDesc = Converter::toDescriptorVector(mDescriptors);
        mpORBvocabulary->transform(vCurrentDesc,mBowVec,mFeatVec,4);
    }
}

//通过OpenCV给定的畸变参数给定未畸变的关键点
//只针对RGBD的例子，双目的例子一定是要进行校正过的
//在构造函数中，我们可以看到该函数被调用
void Frame::UndistortKeyPoints()
{
    //判断是否给定校正参数
    if(mDistCoef.at<float>(0)==0.0)
    {
        mvKeysUn=mvKeys;
        return;
    }

    // Fill matrix with points
    cv::Mat mat(N,2,CV_32F);
    for(int i=0; ifloat>(i,0)=mvKeys[i].pt.x;
        mat.at<float>(i,1)=mvKeys[i].pt.y;
    }

    // Undistort points
    //使用OpenCV进行校正
    mat=mat.reshape(2);
    cv::undistortPoints(mat,mat,mK,mDistCoef,cv::Mat(),mK);
    mat=mat.reshape(1);

    // Fill undistorted keypoint vector
    // 重新存储已经去畸变后的特征点坐标
    mvKeysUn.resize(N);
    for(int i=0; ifloat>(i,0);
        kp.pt.y=mat.at<float>(i,1);
        mvKeysUn[i]=kp;
    }
}

//计算图像的边界
void Frame::ComputeImageBounds(const cv::Mat &imLeft)
{
    if(mDistCoef.at<float>(0)!=0.0)
    {
        cv::Mat mat(4,2,CV_32F);
        mat.at<float>(0,0)=0.0; mat.at<float>(0,1)=0.0;
        mat.at<float>(1,0)=imLeft.cols; mat.at<float>(1,1)=0.0;
        mat.at<float>(2,0)=0.0; mat.at<float>(2,1)=imLeft.rows;
        mat.at<float>(3,0)=imLeft.cols; mat.at<float>(3,1)=imLeft.rows;

        // Undistort corners
        mat=mat.reshape(2);
        cv::undistortPoints(mat,mat,mK,mDistCoef,cv::Mat(),mK);
        mat=mat.reshape(1);

        mnMinX = min(mat.at<float>(0,0),mat.at<float>(2,0));
        mnMaxX = max(mat.at<float>(1,0),mat.at<float>(3,0));
        mnMinY = min(mat.at<float>(0,1),mat.at<float>(1,1));
        mnMaxY = max(mat.at<float>(2,1),mat.at<float>(3,1));

    }
    else
    {
        mnMinX = 0.0f;
        mnMaxX = imLeft.cols;
        mnMinY = 0.0f;
        mnMaxY = imLeft.rows;
    }
}

//进行立体匹配
//找到关键点在左右两幅图之间的匹配
//如果存在匹配，计算出深度并存储于左图关键点相关的右图中关键点的坐标
void Frame::ComputeStereoMatches()
{
    //先对要存储的信息进行初始化
    mvuRight = vector<float>(N,-1.0f);
    mvDepth = vector<float>(N,-1.0f);

    //这里在matcher中设定的阈值，TH_HIGH = 100， TH_LOW = 50，这个阈值怎么来的，具体还不是很清楚
    const int thOrbDist = (ORBmatcher::TH_HIGH+ORBmatcher::TH_LOW)/2;
    //获得原始图像的行数
    const int nRows = mpORBextractorLeft->mvImagePyramid[0].rows;

    //Assign keypoints to row table
    //构造n个vector类型的vector
    vector<vector > vRowIndices(nRows,vector());

    //每行最多装200个特征点
    for(int i=0; i200);

    // 右图特征点的数量
    const int Nr = mvKeysRight.size();

    //https://www.cnblogs.com/shang-slam/p/6393419.html参考这个网址的解释
    //在匹配左右帧的特征点时，虽然已经过了极线矫正，但是不能仅仅搜索极线对应的同一行像素点
    //而应该根据右目提取特征点时的尺度（金字塔层数），确定一个极线附近的扫描范围r，
    //这个带状范围内均包含这个特征信息，这个就是r计算的算法
    for(int iR=0; iRconst cv::KeyPoint &kp = mvKeysRight[iR];
        const float &kpY = kp.pt.y;
        const float r = 2.0f*mvScaleFactors[mvKeysRight[iR].octave];
        const int maxr = ceil(kpY+r);
        const int minr = floor(kpY-r);

        for(int yi=minr;yi<=maxr;yi++)
            vRowIndices[yi].push_back(iR);
    }

    // Set limits for search
    //双目视觉中基线的长度，单位是m
    const float minZ = mb;
    const float minD = 0;
    const float maxD = mbf/minZ;

    // For each left keypoint search a match in the right image
    vectorint, int> > vDistIdx;
    vDistIdx.reserve(N);

    for(int iL=0; iLconst cv::KeyPoint &kpL = mvKeys[iL];
        const int &levelL = kpL.octave;
        const float &vL = kpL.pt.y;
        const float &uL = kpL.pt.x;

        const vector &vCandidates = vRowIndices[vL];

        if(vCandidates.empty())
            continue;

        const float minU = uL-maxD;
        const float maxU = uL-minD;

        if(maxU<0)
            continue;

        int bestDist = ORBmatcher::TH_HIGH;
        size_t bestIdxR = 0;

        const cv::Mat &dL = mDescriptors.row(iL);

        // Compare descriptor to right keypoints
        //通过描述子进行特征点匹配，得到每个特征点最佳匹配点scaleduR
        for(size_t iC=0; iCconst size_t iR = vCandidates[iC];
            const cv::KeyPoint &kpR = mvKeysRight[iR];

            if(kpR.octave1 || kpR.octave>levelL+1)
                continue;

            const float &uR = kpR.pt.x;

            if(uR>=minU && uR<=maxU)
            {
                const cv::Mat &dR = mDescriptorsRight.row(iR);
                const int dist = ORBmatcher::DescriptorDistance(dL,dR);

                if(dist// Subpixel match by correlation
        //通过SAD滑窗得到匹配修正量bestincR
        if(bestDist// coordinates in image pyramid at keypoint scale
            const float uR0 = mvKeysRight[bestIdxR].pt.x;
            const float scaleFactor = mvInvScaleFactors[kpL.octave];
            const float scaleduL = round(kpL.pt.x*scaleFactor);
            const float scaledvL = round(kpL.pt.y*scaleFactor);
            const float scaleduR0 = round(uR0*scaleFactor);

            // sliding window search
            const int w = 5;
            cv::Mat IL = mpORBextractorLeft->mvImagePyramid[kpL.octave].rowRange(scaledvL-w,scaledvL+w+1).colRange(scaleduL-w,scaleduL+w+1);
            IL.convertTo(IL,CV_32F);
            IL = IL - IL.at<float>(w,w) *cv::Mat::ones(IL.rows,IL.cols,CV_32F);

            int bestDist = INT_MAX;
            int bestincR = 0;
            const int L = 5;
            vector<float> vDists;
            vDists.resize(2*L+1);

            const float iniu = scaleduR0+L-w;
            const float endu = scaleduR0+L+w+1;
            if(iniu<0 || endu >= mpORBextractorRight->mvImagePyramid[kpL.octave].cols)
                continue;

            for(int incR=-L; incR<=+L; incR++)
            {
                cv::Mat IR = mpORBextractorRight->mvImagePyramid[kpL.octave].rowRange(scaledvL-w,scaledvL+w+1).colRange(scaleduR0+incR-w,scaleduR0+incR+w+1);
                IR.convertTo(IR,CV_32F);
                IR = IR - IR.at<float>(w,w) *cv::Mat::ones(IR.rows,IR.cols,CV_32F);

                float dist = cv::norm(IL,IR,cv::NORM_L1);
                if(distif(bestincR==-L || bestincR==L)
                continue;

            //(bestincR,dist) (bestincR-1,dist) (bestincR+1,dist)三个点拟合出抛
            //物线，得到亚像素修正量deltaR
            // Sub-pixel match (Parabola fitting)
            const float dist1 = vDists[L+bestincR-1];
            const float dist2 = vDists[L+bestincR];
            const float dist3 = vDists[L+bestincR+1];

            const float deltaR = (dist1-dist3)/(2.0f*(dist1+dist3-2.0f*dist2));

            if(deltaR<-1 || deltaR>1)
                continue;

            // Re-scaled coordinate
            //最终匹配点位置为：scaleduR0 + bestincR + deltaR
            float bestuR = mvScaleFactors[kpL.octave]*((float)scaleduR0+(float)bestincR+deltaR);

            float disparity = (uL-bestuR);

            if(disparity>=minD && disparityif(disparity<=0)
                {
                    disparity=0.01;
                    bestuR = uL-0.01;
                }
                mvDepth[iL]=mbf/disparity;
                mvuRight[iL] = bestuR;
                vDistIdx.push_back(pair<int,int>(bestDist,iL));
            }
        }
    }

    sort(vDistIdx.begin(),vDistIdx.end());
    const float median = vDistIdx[vDistIdx.size()/2].first;
    const float thDist = 1.5f*1.4f*median;

    for(int i=vDistIdx.size()-1;i>=0;i--)
    {
        if(vDistIdx[i].firstbreak;
        else
        {
            mvuRight[vDistIdx[i].second]=-1;
            mvDepth[vDistIdx[i].second]=-1;
        }
    }
}


//RGBD计算立体匹配，只要判断下深度
void Frame::ComputeStereoFromRGBD(const cv::Mat &imDepth)
{
    mvuRight = vector<float>(N,-1);
    mvDepth = vector<float>(N,-1);

    for(int i=0; iconst cv::KeyPoint &kp = mvKeys[i];
        const cv::KeyPoint &kpU = mvKeysUn[i];

        const float &v = kp.pt.y;
        const float &u = kp.pt.x;

        const float d = imDepth.at<float>(v,u);

        if(d>0)
        {
            mvDepth[i] = d;
            mvuRight[i] = kpU.pt.x-mbf/d;
        }
    }
}

//将特征点坐标（给id即可）反投影到3D地图点（世界坐标）
//单纯的二维像素点是没办法投影到3D空间中去的，因为缺少对应的尺度，
//但是在已知深度的情况下,则可确定对应的尺度，最后获得3D中点坐标
cv::Mat Frame::UnprojectStereo(const int &i)
{
    const float z = mvDepth[i];
    if(z>0)
    {
        const float u = mvKeysUn[i].pt.x;
        const float v = mvKeysUn[i].pt.y;
        const float x = (u-cx)*z*invfx;
        const float y = (v-cy)*z*invfy;
        cv::Mat x3Dc = (cv::Mat_<float>(3,1) << x, y, z);
        return mRwc*x3Dc+mOw;
    }
    else
        return cv::Mat();
}

} //namespace ORB_SLAM

里面有些涉及到其他类就没有具体解释，还有就是LZ也不明白。。。

反正代码还能怎么着？多敲！多瞧呗！O(∩_∩)O哈哈~