/**
 * This file is part of ORB-SLAM3
 *
 * Copyright (C) 2017-2021 Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
 * Copyright (C) 2014-2016 Raúl Mur-Artal, José M.M. Montiel and Juan D. Tardós, University of Zaragoza.
 *
 * ORB-SLAM3 is free software: you can redistribute it and/or modify it under the terms of the GNU General Public
 * License as published by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * ORB-SLAM3 is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even
 * the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License along with ORB-SLAM3.
 * If not, see <http://www.gnu.org/licenses/>.
 */

#include "Optimizer.h"

#include <complex>

#include <Eigen/StdVector>
#include <Eigen/Dense>
#include <unsupported/Eigen/MatrixFunctions>

#include "Thirdparty/g2o/g2o/core/sparse_block_matrix.h"
#include "Thirdparty/g2o/g2o/core/block_solver.h"
#include "Thirdparty/g2o/g2o/core/optimization_algorithm_levenberg.h"
#include "Thirdparty/g2o/g2o/core/optimization_algorithm_gauss_newton.h"
#include "Thirdparty/g2o/g2o/solvers/linear_solver_eigen.h"
#include "Thirdparty/g2o/g2o/types/types_six_dof_expmap.h"
#include "Thirdparty/g2o/g2o/core/robust_kernel_impl.h"
#include "Thirdparty/g2o/g2o/solvers/linear_solver_dense.h"
#include "G2oTypes.h"
#include "Converter.h"

#include <mutex>

#include "OptimizableTypes.h"

namespace ORB_SLAM3
{
bool sortByVal(const pair<MapPoint *, int> &a, const pair<MapPoint *, int> &b)
{
    return (a.second < b.second);
}

/**************************************以下为单帧优化**************************************************************/

/**
 * @brief 位姿优化，纯视觉时使用。优化目标：单帧的位姿
 * @param pFrame 待优化的帧
 */
int Optimizer::PoseOptimization(Frame *pFrame)
{
    // 该优化函数主要用于Tracking线程中：运动跟踪、参考帧跟踪、地图跟踪、重定位

    // Step 1：构造g2o优化器, BlockSolver_6_3表示：位姿 _PoseDim 为6维，路标点 _LandmarkDim 是3维
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolver_6_3::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverDense<g2o::BlockSolver_6_3::PoseMatrixType>();

    g2o::BlockSolver_6_3 *solver_ptr = new g2o::BlockSolver_6_3(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    optimizer.setAlgorithm(solver);

    // 输入的帧中,有效的,参与优化过程的2D-3D点对
    int nInitialCorrespondences = 0;

    // Set Frame vertex
    // Step 2：添加顶点：待优化当前帧的Tcw
    g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
    Sophus::SE3<float> Tcw = pFrame->GetPose();
    vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));

    // 设置id，保证本次优化过程中id独立即可
    vSE3->setId(0);
    // 要优化的变量，所以不能固定
    vSE3->setFixed(false);
    // 添加节点
    optimizer.addVertex(vSE3);

    // Set MapPoint vertices
    // 当前帧的特征点数量
    const int N = pFrame->N;

    vector<ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose *> vpEdgesMono;           // 存放单目边
    vector<ORB_SLAM3::EdgeSE3ProjectXYZOnlyPoseToBody *> vpEdgesMono_FHR; // 存放另一目的边
    vector<size_t> vnIndexEdgeMono, vnIndexEdgeRight;                     // 边对应特征点的id
    vpEdgesMono.reserve(N);
    vpEdgesMono_FHR.reserve(N);
    vnIndexEdgeMono.reserve(N);
    vnIndexEdgeRight.reserve(N);

    vector<g2o::EdgeStereoSE3ProjectXYZOnlyPose *> vpEdgesStereo; // 存放双目边
    vector<size_t> vnIndexEdgeStereo;
    vpEdgesStereo.reserve(N);
    vnIndexEdgeStereo.reserve(N);

    // 自由度为2的卡方分布，显著性水平为0.05，对应的临界阈值5.991
    // 可以理解为卡方值高于5.991 95%的几率维外点
    const float deltaMono = sqrt(5.991);
    // 自由度为3的卡方分布，显著性水平为0.05，对应的临界阈值7.815
    const float deltaStereo = sqrt(7.815);

    // Step 3：添加一元边
    {
        // 锁定地图点。由于需要使用地图点来构造顶点和边,因此不希望在构造的过程中部分地图点被改写造成不一致甚至是段错误
        unique_lock<mutex> lock(MapPoint::mGlobalMutex);

        // 遍历当前帧中的所有地图点
        for (int i = 0; i < N; i++)
        {
            MapPoint *pMP = pFrame->mvpMapPoints[i];
            // 如果这个地图点还存在没有被剔除掉
            if (pMP)
            {
                // Conventional SLAM
                //  不存在相机2，则有可能为单目与双目
                if (!pFrame->mpCamera2)
                {
                    // Monocular observation
                    // 单目情况，因为双目模式下pFrame->mvuRight[i]会大于0
                    if (pFrame->mvuRight[i] < 0)
                    {
                        nInitialCorrespondences++;
                        pFrame->mvbOutlier[i] = false;

                        // 对这个地图点的观测，也就是去畸变像素坐标
                        Eigen::Matrix<double, 2, 1> obs;
                        const cv::KeyPoint &kpUn = pFrame->mvKeysUn[i];
                        obs << kpUn.pt.x, kpUn.pt.y;
                        // 新建节点,注意这个节点的只是优化位姿Pose
                        ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose *e = new ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose();

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));
                        e->setMeasurement(obs);
                        // 这个点的可信程度和特征点所在的图层有关，层数约高invSigma2越小，信息矩阵越小，表示误差越大，优化时考虑的比较少
                        const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave];
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);
                        // 在这里使用了鲁棒核函数
                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(deltaMono);

                        // 设置相机
                        e->pCamera = pFrame->mpCamera;
                        // 地图点的空间位置,作为迭代的初始值，因为是一元边，所以不以节点的形式出现
                        e->Xw = pMP->GetWorldPos().cast<double>();

                        optimizer.addEdge(e);

                        vpEdgesMono.push_back(e);
                        vnIndexEdgeMono.push_back(i);
                    }
                    else // 双目
                    {
                        nInitialCorrespondences++;
                        pFrame->mvbOutlier[i] = false;

                        // SET EDGE
                        //  观测多了一项右目的坐标
                        Eigen::Matrix<double, 3, 1> obs;
                        const cv::KeyPoint &kpUn = pFrame->mvKeysUn[i];
                        const float &kp_ur = pFrame->mvuRight[i];
                        obs << kpUn.pt.x, kpUn.pt.y, kp_ur;
                        // 新建节点,注意这里也是只优化位姿
                        g2o::EdgeStereoSE3ProjectXYZOnlyPose *e = new g2o::EdgeStereoSE3ProjectXYZOnlyPose();

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));
                        e->setMeasurement(obs);
                        // 置信程度主要是看左目特征点所在的图层
                        const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave];
                        Eigen::Matrix3d Info = Eigen::Matrix3d::Identity() * invSigma2;
                        e->setInformation(Info);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(deltaStereo);

                        // 双目下没有加入相机
                        e->fx = pFrame->fx;
                        e->fy = pFrame->fy;
                        e->cx = pFrame->cx;
                        e->cy = pFrame->cy;
                        e->bf = pFrame->mbf;
                        e->Xw = pMP->GetWorldPos().cast<double>();

                        optimizer.addEdge(e);

                        vpEdgesStereo.push_back(e);
                        vnIndexEdgeStereo.push_back(i);
                    }
                }
                // SLAM with respect a rigid body
                else  // 两个相机
                {
                    nInitialCorrespondences++;

                    cv::KeyPoint kpUn;
                    // 总数N = 相机1所有特征点数量+相机2所有特征点数量
                    if (i < pFrame->Nleft)
                    { // Left camera observation
                        kpUn = pFrame->mvKeys[i];

                        pFrame->mvbOutlier[i] = false;

                        Eigen::Matrix<double, 2, 1> obs;
                        obs << kpUn.pt.x, kpUn.pt.y;

                        ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose *e = new ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose();

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));
                        e->setMeasurement(obs);
                        const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave];
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(deltaMono);

                        e->pCamera = pFrame->mpCamera;
                        e->Xw = pMP->GetWorldPos().cast<double>();

                        optimizer.addEdge(e);

                        vpEdgesMono.push_back(e);
                        vnIndexEdgeMono.push_back(i);
                    }
                    else
                    { // Right camera observation
                        kpUn = pFrame->mvKeysRight[i - pFrame->Nleft];

                        Eigen::Matrix<double, 2, 1> obs;
                        obs << kpUn.pt.x, kpUn.pt.y;

                        pFrame->mvbOutlier[i] = false;

                        ORB_SLAM3::EdgeSE3ProjectXYZOnlyPoseToBody *e = new ORB_SLAM3::EdgeSE3ProjectXYZOnlyPoseToBody();

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));
                        e->setMeasurement(obs);
                        const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave];
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(deltaMono);

                        e->pCamera = pFrame->mpCamera2;
                        e->Xw = pMP->GetWorldPos().cast<double>();

                        e->mTrl = g2o::SE3Quat(
                            pFrame->GetRelativePoseTrl().unit_quaternion().cast<double>(), pFrame->GetRelativePoseTrl().translation().cast<double>());

                        optimizer.addEdge(e);

                        vpEdgesMono_FHR.push_back(e);
                        vnIndexEdgeRight.push_back(i);
                    }
                }
            }
        }
    }
    // 如果没有足够的匹配点,那么就只好放弃了
    // cout << "PO: vnIndexEdgeMono.size() = " << vnIndexEdgeMono.size() << "   vnIndexEdgeRight.size() = " << vnIndexEdgeRight.size() << endl;
    if (nInitialCorrespondences < 3)
        return 0;

    // We perform 4 optimizations, after each optimization we classify observation as inlier/outlier
    // At the next optimization, outliers are not included, but at the end they can be classified as inliers again.
    // Step 4：开始优化，总共优化四次，每次优化迭代10次,每次优化后，将观测分为outlier和inlier，outlier不参与下次优化
    // 由于每次优化后是对所有的观测进行outlier和inlier判别，因此之前被判别为outlier有可能变成inlier，反之亦然
    // 基于卡方检验计算出的阈值（假设测量有一个像素的偏差）
    const float chi2Mono[4] = {5.991, 5.991, 5.991, 5.991};   // 单目
    const float chi2Stereo[4] = {7.815, 7.815, 7.815, 7.815}; // 双目
    const int its[4] = {10, 10, 10, 10};                      // 四次迭代，每次迭代的次数

    // bad 的地图点个数
    int nBad = 0;
    // 一共进行四次优化，每次会剔除外点
    for (size_t it = 0; it < 4; it++)
    {
        Tcw = pFrame->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        // 其实就是初始化优化器,这里的参数0就算是不填写,默认也是0,也就是只对level为0的边进行优化
        optimizer.initializeOptimization(0);
        // 开始优化，优化10次
        optimizer.optimize(its[it]);

        nBad = 0;
        // 优化结束,开始遍历参与优化的每一条误差边(单目或双相机的相机1)
        for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
        {
            ORB_SLAM3::EdgeSE3ProjectXYZOnlyPose *e = vpEdgesMono[i];

            const size_t idx = vnIndexEdgeMono[i];

            // 如果这条误差边是来自于outlier，也就是上一次优化去掉的
            // 重新计算，因为上一次外点这一次可能成为内点
            if (pFrame->mvbOutlier[idx])
            {
                e->computeError();
            }

            // 就是error*\Omega*error,表征了这个点的误差大小(考虑置信度以后)
            const float chi2 = e->chi2();

            if (chi2 > chi2Mono[it])
            {
                pFrame->mvbOutlier[idx] = true;
                e->setLevel(1); // 设置为outlier , level 1 对应为外点,上面的过程中我们设置其为不优化
                nBad++;
            }
            else
            {
                pFrame->mvbOutlier[idx] = false;
                e->setLevel(0); // 设置为inlier, level 0 对应为内点,上面的过程中我们就是要优化这些关系
            }

            if (it == 2)
                e->setRobustKernel(0); // 除了前两次优化需要RobustKernel以外, 其余的优化都不需要 -- 因为重投影的误差已经有明显的下降了
        }
        // 对于相机2
        for (size_t i = 0, iend = vpEdgesMono_FHR.size(); i < iend; i++)
        {
            ORB_SLAM3::EdgeSE3ProjectXYZOnlyPoseToBody *e = vpEdgesMono_FHR[i];

            const size_t idx = vnIndexEdgeRight[i];

            if (pFrame->mvbOutlier[idx])
            {
                e->computeError();
            }

            const float chi2 = e->chi2();

            if (chi2 > chi2Mono[it])
            {
                pFrame->mvbOutlier[idx] = true;
                e->setLevel(1);
                nBad++;
            }
            else
            {
                pFrame->mvbOutlier[idx] = false;
                e->setLevel(0);
            }

            if (it == 2)
                e->setRobustKernel(0);
        }
        // 对于双目
        for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
        {
            g2o::EdgeStereoSE3ProjectXYZOnlyPose *e = vpEdgesStereo[i];

            const size_t idx = vnIndexEdgeStereo[i];

            if (pFrame->mvbOutlier[idx])
            {
                e->computeError();
            }

            const float chi2 = e->chi2();

            if (chi2 > chi2Stereo[it])
            {
                pFrame->mvbOutlier[idx] = true;
                e->setLevel(1);
                nBad++;
            }
            else
            {
                e->setLevel(0);
                pFrame->mvbOutlier[idx] = false;
            }

            if (it == 2)
                e->setRobustKernel(0);
        }

        if (optimizer.edges().size() < 10)
            break;
    }

    // Recover optimized pose and return number of inliers
    // Step 5 得到优化后的当前帧的位姿
    g2o::VertexSE3Expmap *vSE3_recov = static_cast<g2o::VertexSE3Expmap *>(optimizer.vertex(0));
    g2o::SE3Quat SE3quat_recov = vSE3_recov->estimate();
    Sophus::SE3<float> pose(
        SE3quat_recov.rotation().cast<float>(),
        SE3quat_recov.translation().cast<float>());
    pFrame->SetPose(pose);

    // 并且返回内点数目
    return nInitialCorrespondences - nBad;
}

/**
 * @brief 使用上一关键帧+当前帧的视觉信息和IMU信息，优化当前帧位姿
 *
 * 可分为以下几个步骤：
 * // Step 1：创建g2o优化器，初始化顶点和边
 * // Step 2：启动多轮优化，剔除外点
 * // Step 3：更新当前帧位姿、速度、IMU偏置
 * // Step 4：记录当前帧的优化状态，包括参数信息和对应的海森矩阵
 *
 * @param[in] pFrame 当前帧，也是待优化的帧
 * @param[in] bRecInit 调用这个函数的位置并没有传这个参数，因此它的值默认为false
 * @return int 返回优化后的内点数
 */
int Optimizer::PoseInertialOptimizationLastKeyFrame(Frame *pFrame, bool bRecInit)
{
    // 1. 创建g2o优化器，初始化顶点和边
    // 构建一个稀疏求解器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    // 使用dense的求解器，（常见非dense求解器有cholmod线性求解器和shur补线性求解器）
    linearSolver = new g2o::LinearSolverDense<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);
    // 使用高斯牛顿求解器
    g2o::OptimizationAlgorithmGaussNewton *solver = new g2o::OptimizationAlgorithmGaussNewton(solver_ptr);
    optimizer.setVerbose(false);
    optimizer.setAlgorithm(solver);

    // 当前帧单（左）目地图点数目
    int nInitialMonoCorrespondences = 0;
    int nInitialStereoCorrespondences = 0;
    //上面两项的和，即参与优化的地图点总数目
    int nInitialCorrespondences = 0;

    // Set Frame vertex
    // 2. 确定节点
    // 当前帧的位姿，旋转+平移，6-dim
    VertexPose *VP = new VertexPose(pFrame);
    VP->setId(0);
    VP->setFixed(false);
    optimizer.addVertex(VP);
    // 当前帧的速度，3-dim
    VertexVelocity *VV = new VertexVelocity(pFrame);
    VV->setId(1);
    VV->setFixed(false);
    optimizer.addVertex(VV);
    // 当前帧的陀螺仪偏置，3-dim
    VertexGyroBias *VG = new VertexGyroBias(pFrame);
    VG->setId(2);
    VG->setFixed(false);
    optimizer.addVertex(VG);
    // 当前帧的加速度偏置，3-dim
    VertexAccBias *VA = new VertexAccBias(pFrame);
    VA->setId(3);
    VA->setFixed(false);
    optimizer.addVertex(VA);
    // setFixed(false)这个设置使以上四个顶点（15个参数）在优化时更新

    // Set MapPoint vertices
    // 当前帧的特征点总数
    const int N = pFrame->N;
    // 当前帧左目的特征点总数
    const int Nleft = pFrame->Nleft;
    // 当前帧是否存在右目（即是否为双目）
    const bool bRight = (Nleft != -1);

    vector<EdgeMonoOnlyPose *> vpEdgesMono;
    vector<EdgeStereoOnlyPose *> vpEdgesStereo;
    vector<size_t> vnIndexEdgeMono;
    vector<size_t> vnIndexEdgeStereo;
    vpEdgesMono.reserve(N);
    vpEdgesStereo.reserve(N);
    vnIndexEdgeMono.reserve(N);
    vnIndexEdgeStereo.reserve(N);

    // 自由度为2的卡方分布，显著性水平为0.05，对应的临界阈值5.991
    const float thHuberMono = sqrt(5.991);
    // 自由度为3的卡方分布，显著性水平为0.05，对应的临界阈值7.815
    const float thHuberStereo = sqrt(7.815);

    {
        // 锁定地图点。由于需要使用地图点来构造顶点和边,因此不希望在构造的过程中部分地图点被改写造成不一致甚至是段错误
        // 3. 投影边
        unique_lock<mutex> lock(MapPoint::mGlobalMutex);

        for (int i = 0; i < N; i++)
        {
            MapPoint *pMP = pFrame->mvpMapPoints[i];
            if (pMP)
            {
                cv::KeyPoint kpUn;

                // Left monocular observation
                // 这里说的Left monocular包含两种情况：1.单目情况 2.双目情况下的左目
                if ((!bRight && pFrame->mvuRight[i] < 0) || i < Nleft)
                {
                    // 如果是双目情况下的左目
                    if (i < Nleft) // pair left-right
                        //使用未畸变校正的特征点
                        kpUn = pFrame->mvKeys[i];
                    // 如果是单目
                    else
                        // 使用畸变校正过的特征点
                        kpUn = pFrame->mvKeysUn[i];

                    // 单目地图点计数增加
                    nInitialMonoCorrespondences++;
                    // 当前地图点默认设置为不是外点
                    pFrame->mvbOutlier[i] = false;

                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    // 第一种边(视觉重投影约束)：地图点投影到该帧图像的坐标与特征点坐标偏差尽可能小
                    EdgeMonoOnlyPose *e = new EdgeMonoOnlyPose(pMP->GetWorldPos(), 0);

                    // 将位姿作为第一个顶点
                    e->setVertex(0, VP);

                    // 设置观测值，即去畸变后的像素坐标
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    // 获取不确定度，这里调用uncertainty2返回固定值1.0
                    // ?这里的1.0是作为缺省值的意思吗？是否可以根据对视觉信息的信任度动态修改这个值，比如标定的误差？
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs);

                    // invSigma2 = (Inverse(协方差矩阵))^2，表明该约束在各个维度上的可信度
                    //  图像金字塔层数越高，可信度越差
                    const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    // 设置该约束的信息矩阵
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    // 设置鲁棒核函数，避免其误差的平方项出现数值过大的增长 注：后续在优化2次后会用e->setRobustKernel(0)禁掉鲁棒核函数
                    e->setRobustKernel(rk);

                    // 重投影误差的自由度为2，设置对应的卡方阈值
                    rk->setDelta(thHuberMono);

                    // 将第一种边加入优化器
                    optimizer.addEdge(e);

                    // 将第一种边加入vpEdgesMono
                    vpEdgesMono.push_back(e);
                    // 将对应的特征点索引加入vnIndexEdgeMono
                    vnIndexEdgeMono.push_back(i);
                }
                // Stereo observation
                else if (!bRight)
                {
                    nInitialStereoCorrespondences++;
                    pFrame->mvbOutlier[i] = false;

                    kpUn = pFrame->mvKeysUn[i];
                    const float kp_ur = pFrame->mvuRight[i];
                    Eigen::Matrix<double, 3, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    EdgeStereoOnlyPose *e = new EdgeStereoOnlyPose(pMP->GetWorldPos());

                    e->setVertex(0, VP);
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs.head(2));

                    const float &invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix3d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    optimizer.addEdge(e);

                    vpEdgesStereo.push_back(e);
                    vnIndexEdgeStereo.push_back(i);
                }

                // Right monocular observation
                if (bRight && i >= Nleft)
                {
                    nInitialMonoCorrespondences++;
                    pFrame->mvbOutlier[i] = false;

                    kpUn = pFrame->mvKeysRight[i - Nleft];
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    EdgeMonoOnlyPose *e = new EdgeMonoOnlyPose(pMP->GetWorldPos(), 1);

                    e->setVertex(0, VP);
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs);

                    const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);

                    optimizer.addEdge(e);

                    vpEdgesMono.push_back(e);
                    vnIndexEdgeMono.push_back(i);
                }
            }
        }
    }
    // 统计参与优化的地图点总数目
    nInitialCorrespondences = nInitialMonoCorrespondences + nInitialStereoCorrespondences;

    // 4. 上一个关键帧节点
    // pKF为上一关键帧
    KeyFrame *pKF = pFrame->mpLastKeyFrame;

    // 上一关键帧的位姿，旋转+平移，6-dim
    VertexPose *VPk = new VertexPose(pKF);
    VPk->setId(4);
    VPk->setFixed(true);
    optimizer.addVertex(VPk);
    // 上一关键帧的速度，3-dim
    VertexVelocity *VVk = new VertexVelocity(pKF);
    VVk->setId(5);
    VVk->setFixed(true);
    optimizer.addVertex(VVk);
    // 上一关键帧的陀螺仪偏置，3-dim
    VertexGyroBias *VGk = new VertexGyroBias(pKF);
    VGk->setId(6);
    VGk->setFixed(true);
    optimizer.addVertex(VGk);
    // 上一关键帧的加速度偏置，3-dim
    VertexAccBias *VAk = new VertexAccBias(pKF);
    VAk->setId(7);
    VAk->setFixed(true);
    optimizer.addVertex(VAk);
    // setFixed(true)这个设置使以上四个顶点（15个参数）的值在优化时保持固定
    // 既然被选为关键帧，就不能太善变

    // 5. 第二种边（IMU预积分约束）：两帧之间位姿的变化量与IMU预积分的值偏差尽可能小
    EdgeInertial *ei = new EdgeInertial(pFrame->mpImuPreintegrated);

    // 将上一关键帧四个顶点（P、V、BG、BA）和当前帧两个顶点（P、V）加入第二种边
    ei->setVertex(0, VPk);
    ei->setVertex(1, VVk);
    ei->setVertex(2, VGk);
    ei->setVertex(3, VAk);
    ei->setVertex(4, VP);
    ei->setVertex(5, VV);
    // 把第二种边加入优化器
    optimizer.addEdge(ei);

    // 6. 第三种边（陀螺仪随机游走约束）：陀螺仪的随机游走值在相近帧间不会相差太多  residual=VG-VGk
    // 用大白话来讲就是用固定的VGK拽住VG，防止VG在优化中放飞自我
    EdgeGyroRW *egr = new EdgeGyroRW();

    // 将上一关键帧的BG加入第三种边
    egr->setVertex(0, VGk);
    // 将当前帧的BG加入第三种边
    egr->setVertex(1, VG);
    // C值在预积分阶段更新，range(9,12)对应陀螺仪偏置的协方差，最终cvInfoG值为inv(∑(GyroRW^2/freq))
    Eigen::Matrix3d InfoG = pFrame->mpImuPreintegrated->C.block<3, 3>(9, 9).cast<double>().inverse();
    egr->setInformation(InfoG);
    // 把第三种边加入优化器
    optimizer.addEdge(egr);

    // 7. 第四种边（加速度随机游走约束）：加速度的随机游走值在相近帧间不会相差太多  residual=VA-VAk
    // 用大白话来讲就是用固定的VAK拽住VA，防止VA在优化中放飞自我
    EdgeAccRW *ear = new EdgeAccRW();
    // 将上一关键帧的BA加入第四种边
    ear->setVertex(0, VAk);
    // 将当前帧的BA加入第四种边
    ear->setVertex(1, VA);
    // C值在预积分阶段更新，range(12,15)对应加速度偏置的协方差，最终cvInfoG值为inv(∑(AccRW^2/freq))
    Eigen::Matrix3d InfoA = pFrame->mpImuPreintegrated->C.block<3, 3>(12, 12).cast<double>().inverse();
    ear->setInformation(InfoA);
    // 把第四种边加入优化器
    optimizer.addEdge(ear);

    // 8. 启动多轮优化，剔除外点

    // We perform 4 optimizations, after each optimization we classify observation as inlier/outlier
    // At the next optimization, outliers are not included, but at the end they can be classified as inliers again.
    // 卡方检验值呈递减趋势，目的是让检验越来越苛刻
    float chi2Mono[4] = {12, 7.5, 5.991, 5.991};
    float chi2Stereo[4] = {15.6, 9.8, 7.815, 7.815};
    // 4次优化的迭代次数都为10
    int its[4] = {10, 10, 10, 10};

    // 坏点数
    int nBad = 0;
    // 单目坏点数
    int nBadMono = 0;
    // 双目坏点数
    int nBadStereo = 0;
    // 单目内点数
    int nInliersMono = 0;
    // 双目内点数
    int nInliersStereo = 0;
    // 内点数
    int nInliers = 0;
    bool bOut = false;

    // 进行4次优化
    for (size_t it = 0; it < 4; it++)
    {
        // 初始化优化器,这里的参数0代表只对level为0的边进行优化（不传参数默认也是0）
        optimizer.initializeOptimization(0);
        // 每次优化迭代十次
        optimizer.optimize(its[it]);

        // 每次优化都重新统计各类点的数目
        nBad = 0;
        nBadMono = 0;
        nBadStereo = 0;
        nInliers = 0;
        nInliersMono = 0;
        nInliersStereo = 0;

        // 使用1.5倍的chi2Mono作为“近点”的卡方检验值，意味着地图点越近，检验越宽松
        // 地图点如何定义为“近点”在下面的代码中有解释
        float chi2close = 1.5 * chi2Mono[it];

        // For monocular observations
        for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
        {
            EdgeMonoOnlyPose *e = vpEdgesMono[i];

            const size_t idx = vnIndexEdgeMono[i];

            // 如果这条误差边是来自于outlier
            if (pFrame->mvbOutlier[idx])
            {
                // 计算这条边上次优化后的误差
                e->computeError();
            }

            // 就是error*\Omega*error，表示了这条边考虑置信度以后的误差大小
            const float chi2 = e->chi2();

            // 当地图点在当前帧的深度值小于10时，该地图点属于close（近点）
            // mTrackDepth是在Frame.cc的isInFrustum函数中计算出来的
            bool bClose = pFrame->mvpMapPoints[idx]->mTrackDepth < 10.f;

            // 判断某地图点为外点的条件有以下三种：
            // 1.该地图点不是近点并且误差大于卡方检验值chi2Mono[it]
            // 2.该地图点是近点并且误差大于卡方检验值chi2close
            // 3.深度不为正
            // 每次优化后，用更小的卡方检验值，原因是随着优化的进行，对划分为内点的信任程度越来越低
            if ((chi2 > chi2Mono[it] && !bClose) || (bClose && chi2 > chi2close) || !e->isDepthPositive())
            {
                // 将该点设置为外点
                pFrame->mvbOutlier[idx] = true;
                // 外点不参与下一轮优化
                e->setLevel(1);
                // 单目坏点数+1
                nBadMono++;
            }
            else
            {
                // 将该点设置为内点（暂时）
                pFrame->mvbOutlier[idx] = false;
                // 内点继续参与下一轮优化
                e->setLevel(0);
                // 单目内点数+1
                nInliersMono++;
            }

            // 从第三次优化开始就不设置鲁棒核函数了，原因是经过两轮优化已经趋向准确值，不会有太大误差
            if (it == 2)
                e->setRobustKernel(0);
        }

        // For stereo observations
        for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
        {
            EdgeStereoOnlyPose *e = vpEdgesStereo[i];

            const size_t idx = vnIndexEdgeStereo[i];

            if (pFrame->mvbOutlier[idx])
            {
                e->computeError();
            }

            const float chi2 = e->chi2();

            if (chi2 > chi2Stereo[it])
            {
                pFrame->mvbOutlier[idx] = true;
                e->setLevel(1); // not included in next optimization
                nBadStereo++;
            }
            else
            {
                pFrame->mvbOutlier[idx] = false;
                e->setLevel(0);
                nInliersStereo++;
            }

            if (it == 2)
                e->setRobustKernel(0);
        }

        // 内点总数=单目内点数+双目内点数
        nInliers = nInliersMono + nInliersStereo;
        // 坏点数=单目坏点数+双目坏点数
        nBad = nBadMono + nBadStereo;

        if (optimizer.edges().size() < 10)
        {
            cout << "PIOLKF: NOT ENOUGH EDGES" << endl;
            break;
        }
    }

    // If not too much tracks, recover not too bad points
    // 9. 若4次优化后内点数小于30，尝试恢复一部分不那么糟糕的坏点
    if ((nInliers < 30) && !bRecInit)
    {
        // 重新从0开始统计坏点数
        nBad = 0;
        // 单目可容忍的卡方检验最大值（如果误差比这还大就不要挣扎了...）
        const float chi2MonoOut = 18.f;
        const float chi2StereoOut = 24.f;
        EdgeMonoOnlyPose *e1;
        EdgeStereoOnlyPose *e2;
        // 遍历所有单目特征点
        for (size_t i = 0, iend = vnIndexEdgeMono.size(); i < iend; i++)
        {
            const size_t idx = vnIndexEdgeMono[i];
            // 获取这些特征点对应的边
            e1 = vpEdgesMono[i];
            e1->computeError();
            // 判断误差值是否超过单目可容忍的卡方检验最大值，是的话就把这个点保下来
            if (e1->chi2() < chi2MonoOut)
                pFrame->mvbOutlier[idx] = false;
            else
                nBad++;
        }
        for (size_t i = 0, iend = vnIndexEdgeStereo.size(); i < iend; i++)
        {
            const size_t idx = vnIndexEdgeStereo[i];
            e2 = vpEdgesStereo[i];
            e2->computeError();
            if (e2->chi2() < chi2StereoOut)
                pFrame->mvbOutlier[idx] = false;
            else
                nBad++;
        }
    }

    // 10. 更新当前帧位姿、速度、IMU偏置

    // Recover optimized pose, velocity and biases
    // 给当前帧设置优化后的旋转、位移、速度，用来更新位姿
    pFrame->SetImuPoseVelocity(VP->estimate().Rwb.cast<float>(), VP->estimate().twb.cast<float>(), VV->estimate().cast<float>());
    Vector6d b;
    b << VG->estimate(), VA->estimate();
    // 给当前帧设置优化后的bg，ba
    pFrame->mImuBias = IMU::Bias(b[3], b[4], b[5], b[0], b[1], b[2]);

    // 11. 记录当前帧的优化状态，包括参数信息和对应的海森矩阵
    // Recover Hessian, marginalize keyFframe states and generate new prior for frame
    Eigen::Matrix<double, 15, 15> H;
    H.setZero();

    // H(x)=J(x).t()*info*J(x)

    // J(x)取的是EdgeInertial中的_jacobianOplus[4]和_jacobianOplus[5]，即EdgeInertial::computeError计算出来的er,ev,ep对当前帧Pose和Velocity的偏导
    //因此ei->GetHessian2的结果为：
    // H(∂er/∂r) H(∂er/∂t) H(∂er/∂v)
    // H(∂ev/∂r) H(∂ev/∂t) H(∂ev/∂v)
    // H(∂ep/∂r) H(∂ep/∂t) H(∂ep/∂v)
    //每项H都是3x3，故GetHessian2的结果是9x9
    H.block<9, 9>(0, 0) += ei->GetHessian2();

    // J(x)取的是EdgeGyroRW中的_jacobianOplusXj，即EdgeGyroRW::computeError计算出来的_error(ebg)对当前帧bg的偏导
    //因此egr->GetHessian2的结果为：
    // H(∂ebg/∂bg) ，3x3
    H.block<3, 3>(9, 9) += egr->GetHessian2();

    // J(x)取的是EdgeAccRW中的_jacobianOplusXj，即EdgeAccRW::computeError计算出来的_error(ebg)对当前帧ba的偏导
    //因此ear->GetHessian2的结果为：
    // H(∂eba/∂ba)  ，3x3
    H.block<3, 3>(12, 12) += ear->GetHessian2();

    //经过前面几步，Hessian Matrix长这个样子（注：省略了->GetHessian2()）
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // ei ei ei ei ei ei ei ei ei   0      0     0    0     0    0
    // 0  0  0  0  0  0  0   0  0  egr egr egr  0     0     0
    // 0  0  0  0  0  0  0   0  0  egr egr egr  0     0     0
    // 0  0  0  0  0  0  0   0  0  egr egr egr  0     0     0
    // 0  0  0  0  0  0  0   0  0    0     0      0  ear ear ear
    // 0  0  0  0  0  0  0   0  0    0     0      0  ear ear ear
    // 0  0  0  0  0  0  0   0  0    0     0      0  ear ear ear

    int tot_in = 0, tot_out = 0;
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        EdgeMonoOnlyPose *e = vpEdgesMono[i];

        const size_t idx = vnIndexEdgeMono[i];

        if (!pFrame->mvbOutlier[idx])
        {
            H.block<6, 6>(0, 0) += e->GetHessian();
            tot_in++;
        }
        else
            tot_out++;
    }

    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        EdgeStereoOnlyPose *e = vpEdgesStereo[i];

        const size_t idx = vnIndexEdgeStereo[i];

        if (!pFrame->mvbOutlier[idx])
        {
            H.block<6, 6>(0, 0) += e->GetHessian();
            tot_in++;
        }
        else
            tot_out++;
    }

    // 设eie = ei->GetHessian2()+∑(e->GetHessian())
    // 则最终Hessian Matrix长这样
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // eie eie eie eie eie eie         ei ei ei   0      0     0    0     0    0
    // ei    ei    ei    ei   ei   ei  ei ei ei   0      0     0    0     0    0
    // ei    ei    ei    ei   ei   ei  ei ei ei   0      0     0    0     0    0
    // ei    ei    ei    ei   ei   ei  ei ei ei   0      0     0    0     0    0
    //  0     0     0     0     0    0   0   0  0  egr egr egr  0     0     0
    //  0     0     0     0     0    0   0   0  0  egr egr egr  0     0     0
    //  0     0     0     0     0    0   0   0  0  egr egr egr  0     0     0
    //  0     0     0     0     0    0   0   0  0    0     0      0  ear ear ear
    //  0     0     0     0     0    0   0   0  0    0     0      0  ear ear ear
    //  0     0     0     0     0    0   0   0  0    0     0      0  ear ear ear

    // 构造一个ConstraintPoseImu对象，为下一帧提供先验约束
    // 构造对象所使用的参数是当前帧P、V、BG、BA的估计值和H矩阵
    pFrame->mpcpi = new ConstraintPoseImu(VP->estimate().Rwb, VP->estimate().twb, VV->estimate(), VG->estimate(), VA->estimate(), H);
    // 在PoseInertialOptimizationLastFrame函数中，会将ConstraintPoseImu信息作为“上一帧先验约束”生成一条优化边

    // 返回值：内点数 = 总地图点数目 - 坏点（外点）数目
    return nInitialCorrespondences - nBad;
}

/**
 * @brief 使用上一帧+当前帧的视觉信息和IMU信息，优化当前帧位姿
 *
 * 可分为以下几个步骤：
 * // Step 1：创建g2o优化器，初始化顶点和边
 * // Step 2：启动多轮优化，剔除外点
 * // Step 3：更新当前帧位姿、速度、IMU偏置
 * // Step 4：记录当前帧的优化状态，包括参数信息和边缘化后的海森矩阵
 *
 * @param[in] pFrame 当前帧，也是待优化的帧
 * @param[in] bRecInit 调用这个函数的位置并没有传这个参数，因此它的值默认为false
 * @return int 返回优化后的内点数
 */
int Optimizer::PoseInertialOptimizationLastFrame(Frame *pFrame, bool bRecInit)
{
    // Step 1：创建g2o优化器，初始化顶点和边
    // 构建一个稀疏求解器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    // 使用dense的求解器，（常见非dense求解器有cholmod线性求解器和shur补线性求解器）
    linearSolver = new g2o::LinearSolverDense<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    // 使用高斯牛顿求解器
    g2o::OptimizationAlgorithmGaussNewton *solver = new g2o::OptimizationAlgorithmGaussNewton(solver_ptr);
    optimizer.setAlgorithm(solver);
    optimizer.setVerbose(false);

    // 当前帧单（左）目地图点数目
    int nInitialMonoCorrespondences = 0;
    int nInitialStereoCorrespondences = 0;
    int nInitialCorrespondences = 0;

    // Set Current Frame vertex
    // 当前帧的位姿，旋转+平移，6-dim
    VertexPose *VP = new VertexPose(pFrame);
    VP->setId(0);
    VP->setFixed(false);  // 需要优化，所以不固定
    optimizer.addVertex(VP);
    // 当前帧的速度，3-dim
    VertexVelocity *VV = new VertexVelocity(pFrame);
    VV->setId(1);
    VV->setFixed(false);
    optimizer.addVertex(VV);
    // 当前帧的陀螺仪偏置，3-dim
    VertexGyroBias *VG = new VertexGyroBias(pFrame);
    VG->setId(2);
    VG->setFixed(false);
    optimizer.addVertex(VG);
    // 当前帧的加速度偏置，3-dim
    VertexAccBias *VA = new VertexAccBias(pFrame);
    VA->setId(3);
    VA->setFixed(false);
    optimizer.addVertex(VA);

    // Set MapPoint vertices
    // 当前帧的特征点总数 N = Nleft + Nright
    // 对于单目 N!=0, Nleft=-1，Nright=-1
    // 对于双目 N!=0, Nleft!=-1，Nright!=-1
    const int N = pFrame->N;
    // 当前帧左目的特征点总数
    const int Nleft = pFrame->Nleft;
    // 当前帧是否存在右目（即是否为双目），存在为true
    //?感觉可以更直接点啊 bRight = (Nright!=-1)
    const bool bRight = (Nleft != -1);

    vector<EdgeMonoOnlyPose *> vpEdgesMono;
    vector<EdgeStereoOnlyPose *> vpEdgesStereo;
    vector<size_t> vnIndexEdgeMono;
    vector<size_t> vnIndexEdgeStereo;
    vpEdgesMono.reserve(N);
    vpEdgesStereo.reserve(N);
    vnIndexEdgeMono.reserve(N);
    vnIndexEdgeStereo.reserve(N);

    // 自由度为2的卡方分布，显著性水平为0.05，对应的临界阈值5.991
    const float thHuberMono = sqrt(5.991);
    // 自由度为3的卡方分布，显著性水平为0.05，对应的临界阈值7.815
    const float thHuberStereo = sqrt(7.815);

    {
        // 锁定地图点。由于需要使用地图点来构造顶点和边,因此不希望在构造的过程中部分地图点被改写造成不一致甚至是段错误
        unique_lock<mutex> lock(MapPoint::mGlobalMutex);

        for (int i = 0; i < N; i++)
        {
            MapPoint *pMP = pFrame->mvpMapPoints[i];
            if (pMP)
            {
                cv::KeyPoint kpUn;
                // Left monocular observation
                // 这里说的Left monocular包含两种情况：1.单目情况 2.双目情况下的左目
                if ((!bRight && pFrame->mvuRight[i] < 0) || i < Nleft)
                {
                    // 如果是双目情况下的左目
                    if (i < Nleft)  // pair left-right
                        // 使用未畸变校正的特征点
                        kpUn = pFrame->mvKeys[i];
                    // 如果是单目
                    else
                        // 使用畸变校正过的特征点
                        kpUn = pFrame->mvKeysUn[i];

                    // 单目地图点计数增加
                    nInitialMonoCorrespondences++;
                    // 当前地图点默认设置为不是外点
                    pFrame->mvbOutlier[i] = false;
                    // 观测的特征点
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    // 第一种边(视觉重投影约束)：地图点投影到该帧图像的坐标与特征点坐标偏差尽可能小
                    EdgeMonoOnlyPose *e = new EdgeMonoOnlyPose(pMP->GetWorldPos(), 0);

                    // 将位姿作为第一个顶点
                    e->setVertex(0, VP);

                    // 设置观测值，即去畸变后的像素坐标
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    // 获取不确定度，这里调用uncertainty2返回固定值1.0
                    // ?这里返回1.0是作为缺省值，是否可以根据对视觉信息的信任度动态修改这个值，比如标定的误差？
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs);

                    // invSigma2 = (Inverse(协方差矩阵))^2，表明该约束在各个维度上的可信度
                    //  图像金字塔层数越高，可信度越差
                    const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    // 设置该约束的信息矩阵
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    // 设置鲁棒核函数，避免其误差的平方项出现数值过大的增长 注：后续在优化2次后会用e->setRobustKernel(0)禁掉鲁棒核函数
                    e->setRobustKernel(rk);
                    // 重投影误差的自由度为2，设置对应的卡方阈值
                    rk->setDelta(thHuberMono);

                    // 将第一种边加入优化器
                    optimizer.addEdge(e);

                    // 将第一种边加入vpEdgesMono
                    vpEdgesMono.push_back(e);
                    // 将对应的特征点索引加入vnIndexEdgeMono
                    vnIndexEdgeMono.push_back(i);
                }
                // Stereo observation
                else if (!bRight)
                {
                    nInitialStereoCorrespondences++;
                    pFrame->mvbOutlier[i] = false;

                    kpUn = pFrame->mvKeysUn[i];
                    const float kp_ur = pFrame->mvuRight[i];
                    Eigen::Matrix<double, 3, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    EdgeStereoOnlyPose *e = new EdgeStereoOnlyPose(pMP->GetWorldPos());

                    e->setVertex(0, VP);
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs.head(2));

                    const float &invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix3d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    optimizer.addEdge(e);

                    vpEdgesStereo.push_back(e);
                    vnIndexEdgeStereo.push_back(i);
                }

                // Right monocular observation
                if (bRight && i >= Nleft)
                {
                    nInitialMonoCorrespondences++;
                    pFrame->mvbOutlier[i] = false;

                    kpUn = pFrame->mvKeysRight[i - Nleft];
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    EdgeMonoOnlyPose *e = new EdgeMonoOnlyPose(pMP->GetWorldPos(), 1);

                    e->setVertex(0, VP);
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pFrame->mpCamera->uncertainty2(obs);

                    const float invSigma2 = pFrame->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);

                    optimizer.addEdge(e);

                    vpEdgesMono.push_back(e);
                    vnIndexEdgeMono.push_back(i);
                }
            }
        }
    }
    // 统计参与优化的地图点总数目
    nInitialCorrespondences = nInitialMonoCorrespondences + nInitialStereoCorrespondences;

    // Set Previous Frame Vertex
    // pFp为上一帧
    Frame *pFp = pFrame->mpPrevFrame;

    // 上一帧的位姿，旋转+平移，6-dim
    VertexPose *VPk = new VertexPose(pFp);
    VPk->setId(4);
    VPk->setFixed(false);
    optimizer.addVertex(VPk);
    // 上一帧的速度，3-dim
    VertexVelocity *VVk = new VertexVelocity(pFp);
    VVk->setId(5);
    VVk->setFixed(false);
    optimizer.addVertex(VVk);
    // 上一帧的陀螺仪偏置，3-dim
    VertexGyroBias *VGk = new VertexGyroBias(pFp);
    VGk->setId(6);
    VGk->setFixed(false);
    optimizer.addVertex(VGk);
    // 上一帧的加速度偏置，3-dim
    VertexAccBias *VAk = new VertexAccBias(pFp);
    VAk->setId(7);
    VAk->setFixed(false);
    optimizer.addVertex(VAk);
    // setFixed(false)这个设置使以上四个顶点（15个参数）的值随优化而变，这样做会给上一帧再提供一些优化空间
    // 但理论上不应该优化过多，否则会有不良影响，故后面的代码会用第五种边来约束上一帧的变化量

    // 第二种边（IMU预积分约束）：两帧之间位姿的变化量与IMU预积分的值偏差尽可能小
    EdgeInertial *ei = new EdgeInertial(pFrame->mpImuPreintegratedFrame);

    // 将上一帧四个顶点（P、V、BG、BA）和当前帧两个顶点（P、V）加入第二种边
    ei->setVertex(0, VPk);
    ei->setVertex(1, VVk);
    ei->setVertex(2, VGk);
    ei->setVertex(3, VAk);
    ei->setVertex(4, VP);
    ei->setVertex(5, VV);
    // 把第二种边加入优化器
    optimizer.addEdge(ei);

    // 第三种边（陀螺仪随机游走约束）：陀螺仪的随机游走值在相邻帧间不会相差太多  residual=VG-VGk
    // 用大白话来讲就是用固定的VGK拽住VG，防止VG在优化中放飞自我
    EdgeGyroRW *egr = new EdgeGyroRW();
    // 将上一帧的BG加入第三种边
    egr->setVertex(0, VGk);
    // 将当前帧的BG加入第三种边
    egr->setVertex(1, VG);
    // C值在预积分阶段更新，range(9,12)对应陀螺仪偏置的协方差，最终cvInfoG值为inv(∑(GyroRW^2/freq))
    Eigen::Matrix3d InfoG = pFrame->mpImuPreintegrated->C.block<3, 3>(9, 9).cast<double>().inverse();
    egr->setInformation(InfoG);
    // 把第三种边加入优化器
    optimizer.addEdge(egr);

    // 第四种边（加速度随机游走约束）：加速度的随机游走值在相近帧间不会相差太多  residual=VA-VAk
    // 用大白话来讲就是用固定的VAK拽住VA，防止VA在优化中放飞自我
    EdgeAccRW *ear = new EdgeAccRW();
    // 将上一帧的BA加入第四种边
    ear->setVertex(0, VAk);
    // 将当前帧的BA加入第四种边
    ear->setVertex(1, VA);
    // C值在预积分阶段更新，range(12,15)对应加速度偏置的协方差，最终cvInfoG值为inv(∑(AccRW^2/freq))
    Eigen::Matrix3d InfoA = pFrame->mpImuPreintegrated->C.block<3, 3>(12, 12).cast<double>().inverse();
    ear->setInformation(InfoA);
    // 把第四种边加入优化器
    optimizer.addEdge(ear);

    // ?既然有判空的操作，可以区分一下有先验信息（五条边）和无先验信息（四条边）的情况
    if (!pFp->mpcpi)
        Verbose::PrintMess("pFp->mpcpi does not exist!!!\nPrevious Frame " + to_string(pFp->mnId), Verbose::VERBOSITY_NORMAL);

    // 第五种边（先验约束）：上一帧信息随优化的改变量要尽可能小
    EdgePriorPoseImu *ep = new EdgePriorPoseImu(pFp->mpcpi);

    // 将上一帧的四个顶点（P、V、BG、BA）加入第五种边
    ep->setVertex(0, VPk);
    ep->setVertex(1, VVk);
    ep->setVertex(2, VGk);
    ep->setVertex(3, VAk);
    g2o::RobustKernelHuber *rkp = new g2o::RobustKernelHuber;
    ep->setRobustKernel(rkp);
    rkp->setDelta(5);
    // 把第五种边加入优化器
    optimizer.addEdge(ep);

    // Step 2：启动多轮优化，剔除外点

    // We perform 4 optimizations, after each optimization we classify observation as inlier/outlier
    // At the next optimization, outliers are not included, but at the end they can be classified as inliers again.
    //与PoseInertialOptimizationLastKeyFrame函数对比，区别在于：在优化过程中保持卡方阈值不变
    // 以下参数的解释
    // 自由度为2的卡方分布，显著性水平为0.05，对应的临界阈值5.991
    // 自由度为3的卡方分布，显著性水平为0.05，对应的临界阈值7.815
    // 自由度为3的卡方分布，显著性水平为0.02，对应的临界阈值9.8
    // 自由度为3的卡方分布，显著性水平为0.001，对应的临界阈值15.6
    // 计算方法：https://stattrek.com/online-calculator/chi-square.aspx
    const float chi2Mono[4] = {5.991, 5.991, 5.991, 5.991};
    const float chi2Stereo[4] = {15.6f, 9.8f, 7.815f, 7.815f};
    // 4次优化的迭代次数都为10
    const int its[4] = {10, 10, 10, 10};

    // 坏点数
    int nBad = 0;
    // 单目坏点数
    int nBadMono = 0;
    // 双目坏点数
    int nBadStereo = 0;
    // 单目内点数
    int nInliersMono = 0;
    // 双目内点数
    int nInliersStereo = 0;
    // 内点数
    int nInliers = 0;
    for (size_t it = 0; it < 4; it++)
    {
        // 初始化优化器,这里的参数0代表只对level为0的边进行优化（不传参数默认也是0）
        optimizer.initializeOptimization(0);
        // 每次优化迭代十次
        optimizer.optimize(its[it]);

        // 每次优化都重新统计各类点的数目
        nBad = 0;
        nBadMono = 0;
        nBadStereo = 0;
        nInliers = 0;
        nInliersMono = 0;
        nInliersStereo = 0;
        // 使用1.5倍的chi2Mono作为“近点”的卡方检验值，意味着地图点越近，检验越宽松
        // 地图点如何定义为“近点”在下面的代码中有解释
        float chi2close = 1.5 * chi2Mono[it];

        for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
        {
            EdgeMonoOnlyPose *e = vpEdgesMono[i];

            const size_t idx = vnIndexEdgeMono[i];

            // 当地图点在当前帧的深度值小于10时，该地图点属于close（近点）
            // mTrackDepth是在Frame.cc的isInFrustum函数中计算出来的
            bool bClose = pFrame->mvpMapPoints[idx]->mTrackDepth < 10.f;

            // 如果这条误差边是来自于outlier
            if (pFrame->mvbOutlier[idx])
            {
                //计算本次优化后的误差
                e->computeError();
            }

            // 就是error*\Omega*error，表示了这条边考虑置信度以后的误差大小
            const float chi2 = e->chi2();

            // 判断某地图点为外点的条件有以下三种：
            // 1.该地图点不是近点并且误差大于卡方检验值chi2Mono[it]
            // 2.该地图点是近点并且误差大于卡方检验值chi2close
            // 3.深度不为正
            // 每次优化后，用更小的卡方检验值，原因是随着优化的进行，对划分为内点的信任程度越来越低
            if ((chi2 > chi2Mono[it] && !bClose) || (bClose && chi2 > chi2close) || !e->isDepthPositive())
            {
                // 将该点设置为外点
                pFrame->mvbOutlier[idx] = true;
                // 外点不参与下一轮优化
                e->setLevel(1);
                // 单目坏点数+1
                nBadMono++;
            }
            else
            {
                // 将该点设置为内点（暂时）
                pFrame->mvbOutlier[idx] = false;
                // 内点继续参与下一轮优化
                e->setLevel(0);
                // 单目内点数+1
                nInliersMono++;
            }

            // 从第三次优化开始就不设置鲁棒核函数了，原因是经过两轮优化已经趋向准确值，不会有太大误差
            if (it == 2)
                e->setRobustKernel(0);
        }

        for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
        {
            EdgeStereoOnlyPose *e = vpEdgesStereo[i];

            const size_t idx = vnIndexEdgeStereo[i];

            if (pFrame->mvbOutlier[idx])
            {
                e->computeError();
            }

            const float chi2 = e->chi2();

            if (chi2 > chi2Stereo[it])
            {
                pFrame->mvbOutlier[idx] = true;
                e->setLevel(1);
                nBadStereo++;
            }
            else
            {
                pFrame->mvbOutlier[idx] = false;
                e->setLevel(0);
                nInliersStereo++;
            }

            if (it == 2)
                e->setRobustKernel(0);
        }

        // 内点总数=单目内点数+双目内点数
        nInliers = nInliersMono + nInliersStereo;
        // 坏点数=单目坏点数+双目坏点数
        nBad = nBadMono + nBadStereo;

        if (optimizer.edges().size() < 10)
        {
            cout << "PIOLF: NOT ENOUGH EDGES" << endl;
            break;
        }
    }

    // 若4次优化后内点数小于30，尝试恢复一部分不那么糟糕的坏点
    if ((nInliers < 30) && !bRecInit)
    {
        // 重新从0开始统计坏点数
        nBad = 0;
        // 单目可容忍的卡方检验最大值（如果误差比这还大就不要挣扎了...）
        const float chi2MonoOut = 18.f;
        const float chi2StereoOut = 24.f;
        EdgeMonoOnlyPose *e1;
        EdgeStereoOnlyPose *e2;
        // 遍历所有单目特征点
        for (size_t i = 0, iend = vnIndexEdgeMono.size(); i < iend; i++)
        {
            const size_t idx = vnIndexEdgeMono[i];
            // 获取这些特征点对应的边
            e1 = vpEdgesMono[i];
            e1->computeError();
            if (e1->chi2() < chi2MonoOut)
                pFrame->mvbOutlier[idx] = false;
            else
                nBad++;
        }
        for (size_t i = 0, iend = vnIndexEdgeStereo.size(); i < iend; i++)
        {
            const size_t idx = vnIndexEdgeStereo[i];
            e2 = vpEdgesStereo[i];
            e2->computeError();
            if (e2->chi2() < chi2StereoOut)
                pFrame->mvbOutlier[idx] = false;
            else
                nBad++;
        }
    }

    // ?多此一举？优化过程中nInliers这个值已经计算过了，nInliersMono和nInliersStereo在后续代码中一直保持不变
    nInliers = nInliersMono + nInliersStereo;

    // Step 3：更新当前帧位姿、速度、IMU偏置

    // Recover optimized pose, velocity and biases
    // 给当前帧设置优化后的旋转、位移、速度，用来更新位姿
    pFrame->SetImuPoseVelocity(VP->estimate().Rwb.cast<float>(), VP->estimate().twb.cast<float>(), VV->estimate().cast<float>());
    Vector6d b;
    b << VG->estimate(), VA->estimate();
    // 给当前帧设置优化后的bg，ba
    pFrame->mImuBias = IMU::Bias(b[3], b[4], b[5], b[0], b[1], b[2]);

    // Step 4：记录当前帧的优化状态，包括参数信息和边缘化后的海森矩阵
    // Recover Hessian, marginalize previous frame states and generate new prior for frame
    // 包含本次优化所有信息矩阵的和，它代表本次优化对确定性的影响
    Eigen::Matrix<double, 30, 30> H;
    H.setZero();

    //第1步，加上EdgeInertial（IMU预积分约束）的海森矩阵
    // ei的定义
    // EdgeInertial* ei = new EdgeInertial(pFrame->mpImuPreintegratedFrame);
    // ei->setVertex(0, VPk);
    // ei->setVertex(1, VVk);
    // ei->setVertex(2, VGk);
    // ei->setVertex(3, VAk);
    // ei->setVertex(4, VP);  // VertexPose* VP = new VertexPose(pFrame);
    // ei->setVertex(5, VV);  // VertexVelocity* VV = new VertexVelocity(pFrame);
    // ei->GetHessian()  =  J.t * J 下同，不做详细标注了
    // J
    //       rn + tn  vn    gn   an   rn+1 + tn+1  vn+1
    // er      Jp1    Jv1  Jg1   Ja1      Jp2      Jv2
    // 角标1表示上一帧，2表示当前帧
    //      6            3             3           3            6            3
    // Jp1.t * Jp1  Jp1.t * Jv1  Jp1.t * Jg1  Jp1.t * Ja1  Jp1.t * Jp2  Jp1.t * Jv2     6
    // Jv1.t * Jp1  Jv1.t * Jv1  Jv1.t * Jg1  Jv1.t * Ja1  Jv1.t * Jp2  Jv1.t * Jv2     3
    // Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1  Jg1.t * Ja1  Jg1.t * Jp2  Jg1.t * Jv2     3
    // Ja1.t * Jp1  Ja1.t * Jv1  Ja1.t * Jg1  Ja1.t * Ja1  Ja1.t * Jp2  Ja1.t * Jv2     3
    // Jp2.t * Jp1  Jp2.t * Jv1  Jp2.t * Jg1  Jp2.t * Ja1  Jp2.t * Jp2  Jp2.t * Jv2     6
    // Jv2.t * Jp1  Jv2.t * Jv1  Jv2.t * Jg1  Jv2.t * Ja1  Jv2.t * Jp2  Jv2.t * Jv2     3
    // 所以矩阵是24*24 的
    H.block<24, 24>(0, 0) += ei->GetHessian();
    // 经过这步H变成了
    // 列数 6            3             3           3            6           3        6
    // ---------------------------------------------------------------------------------- 行数
    // Jp1.t * Jp1  Jp1.t * Jv1  Jp1.t * Jg1  Jp1.t * Ja1  Jp1.t * Jp2  Jp1.t * Jv2   0 |  6
    // Jv1.t * Jp1  Jv1.t * Jv1  Jv1.t * Jg1  Jv1.t * Ja1  Jv1.t * Jp2  Jv1.t * Jv2   0 |  3
    // Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1  Jg1.t * Ja1  Jg1.t * Jp2  Jg1.t * Jv2   0 |  3
    // Ja1.t * Jp1  Ja1.t * Jv1  Ja1.t * Jg1  Ja1.t * Ja1  Ja1.t * Jp2  Ja1.t * Jv2   0 |  3
    // Jp2.t * Jp1  Jp2.t * Jv1  Jp2.t * Jg1  Jp2.t * Ja1  Jp2.t * Jp2  Jp2.t * Jv2   0 |  6
    // Jv2.t * Jp1  Jv2.t * Jv1  Jv2.t * Jg1  Jv2.t * Ja1  Jv2.t * Jp2  Jv2.t * Jv2   0 |  3
    //     0            0            0            0            0           0          0 |  6
    // ----------------------------------------------------------------------------------

    //第2步，加上EdgeGyroRW（陀螺仪随机游走约束）的信息矩阵：
    //|   0~8   |       9~11     | 12~23 |     24~26     |27~29
    // 9~11是上一帧的bg(3-dim)，24~26是当前帧的bg(3-dim)
    //  egr的定义
    //  EdgeGyroRW* egr = new EdgeGyroRW();
    //  egr->setVertex(0, VGk);
    //  egr->setVertex(1, VG);
    Eigen::Matrix<double, 6, 6> Hgr = egr->GetHessian();
    H.block<3, 3>(9, 9) += Hgr.block<3, 3>(0, 0);   // Jgr1.t * Jgr1
    H.block<3, 3>(9, 24) += Hgr.block<3, 3>(0, 3);  // Jgr1.t * Jgr2
    H.block<3, 3>(24, 9) += Hgr.block<3, 3>(3, 0);  // Jgr2.t * Jgr1
    H.block<3, 3>(24, 24) += Hgr.block<3, 3>(3, 3); // Jgr2.t * Jgr2
    // 经过这步H变成了
    // 列数 6            3                    3                      3            6           3             3         3
    // ----------------------------------------------------------------------------------------------------------------- 行数
    // Jp1.t * Jp1  Jp1.t * Jv1         Jp1.t * Jg1           Jp1.t * Ja1  Jp1.t * Jp2  Jp1.t * Jv2        0         0 |  6
    // Jv1.t * Jp1  Jv1.t * Jv1         Jv1.t * Jg1           Jv1.t * Ja1  Jv1.t * Jp2  Jv1.t * Jv2        0         0 |  3
    // Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1 + Jgr1.t * Jgr1  Jg1.t * Ja1  Jg1.t * Jp2  Jg1.t * Jv2  Jgr1.t * Jgr2   0 |  3
    // Ja1.t * Jp1  Ja1.t * Jv1         Ja1.t * Jg1           Ja1.t * Ja1  Ja1.t * Jp2  Ja1.t * Jv2        0         0 |  3
    // Jp2.t * Jp1  Jp2.t * Jv1         Jp2.t * Jg1           Jp2.t * Ja1  Jp2.t * Jp2  Jp2.t * Jv2        0         0 |  6
    // Jv2.t * Jp1  Jv2.t * Jv1         Jv2.t * Jg1           Jv2.t * Ja1  Jv2.t * Jp2  Jv2.t * Jv2        0         0 |  3
    //     0            0             Jgr2.t * Jgr1                 0            0           0       Jgr2.t * Jgr2   0 |  3
    //     0            0                    0                      0            0           0             0         0 |  3
    // -----------------------------------------------------------------------------------------------------------------

    //第3步，加上EdgeAccRW（加速度随机游走约束）的信息矩阵：
    //|   0~11   |      12~14    | 15~26 |     27~29     |30
    // 12~14是上一帧的ba(3-dim)，27~29是当前帧的ba(3-dim)
    // ear的定义
    // EdgeAccRW* ear = new EdgeAccRW();
    // ear->setVertex(0, VAk);
    // ear->setVertex(1, VA);
    Eigen::Matrix<double, 6, 6> Har = ear->GetHessian();
    H.block<3, 3>(12, 12) += Har.block<3, 3>(0, 0); // Jar1.t * Jar1
    H.block<3, 3>(12, 27) += Har.block<3, 3>(0, 3); // Jar1.t * Jar2
    H.block<3, 3>(27, 12) += Har.block<3, 3>(3, 0); // Jar2.t * Jar1
    H.block<3, 3>(27, 27) += Har.block<3, 3>(3, 3); // Jar2.t * Jar2
    // 经过这步H变成了
    // 列数 6            3                    3                            3                         6           3             3              3
    // --------------------------------------------------------------------------------------------------------------------------------------------------- 行数
    // |  Jp1.t * Jp1  Jp1.t * Jv1         Jp1.t * Jg1                 Jp1.t * Ja1            |  Jp1.t * Jp2  Jp1.t * Jv2        0              0        |  6
    // |  Jv1.t * Jp1  Jv1.t * Jv1         Jv1.t * Jg1                 Jv1.t * Ja1            |  Jv1.t * Jp2  Jv1.t * Jv2        0              0        |  3
    // |  Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1 + Jgr1.t * Jgr1        Jg1.t * Ja1            |  Jg1.t * Jp2  Jg1.t * Jv2  Jgr1.t * Jgr2        0        |  3
    // |  Ja1.t * Jp1  Ja1.t * Jv1         Ja1.t * Jg1           Ja1.t * Ja1 + Jar1.t * Jar1  |  Ja1.t * Jp2  Ja1.t * Jv2        0       Jar1.t * Jar2   |  3
    // |--------------------------------------------------------------------------------------------------------------------------------------------------
    // |  Jp2.t * Jp1  Jp2.t * Jv1         Jp2.t * Jg1                 Jp2.t * Ja1            |  Jp2.t * Jp2  Jp2.t * Jv2        0              0        |  6
    // |  Jv2.t * Jp1  Jv2.t * Jv1         Jv2.t * Jg1                 Jv2.t * Ja1            |  Jv2.t * Jp2  Jv2.t * Jv2        0              0        |  3
    // |      0            0              Jgr2.t * Jgr1                      0                |        0           0       Jgr2.t * Jgr2        0        |  3
    // |      0            0                    0                     Jar2.t * Jar1           |        0           0             0        Jar2.t * Jar2  |  3
    // ---------------------------------------------------------------------------------------------------------------------------------------------------

    //第4步，加上EdgePriorPoseImu（先验约束）的信息矩阵：
    //|   0~14   |  15~29
    // 0~14是上一帧的P(6-dim)、V(3-dim)、BG(3-dim)、BA(3-dim)
    // ep定义
    // EdgePriorPoseImu* ep = new EdgePriorPoseImu(pFp->mpcpi);
    // ep->setVertex(0, VPk);
    // ep->setVertex(1, VVk);
    // ep->setVertex(2, VGk);
    // ep->setVertex(3, VAk);
    //      6            3             3           3
    // Jp1.t * Jp1  Jp1.t * Jv1  Jp1.t * Jg1  Jp1.t * Ja1     6
    // Jv1.t * Jp1  Jv1.t * Jv1  Jv1.t * Jg1  Jv1.t * Ja1     3
    // Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1  Jg1.t * Ja1     3
    // Ja1.t * Jp1  Ja1.t * Jv1  Ja1.t * Jg1  Ja1.t * Ja1     3
    H.block<15, 15>(0, 0) += ep->GetHessian(); // 上一帧 的H矩阵，矩阵太大了不写了。。。总之就是加到下标为1相关的了

    int tot_in = 0, tot_out = 0;
    // 第5步 关于位姿的海森
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        EdgeMonoOnlyPose *e = vpEdgesMono[i];

        const size_t idx = vnIndexEdgeMono[i];

        if (!pFrame->mvbOutlier[idx])
        {
            //  0~14  |     15~20   | 21~29
            // 15~20是当前帧的P(6-dim)
            H.block<6, 6>(15, 15) += e->GetHessian(); // 当前帧的H矩阵，矩阵太大了不写了。。。总之就是加到p2相关的了
            tot_in++;
        }
        else
            tot_out++;
    }

    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        EdgeStereoOnlyPose *e = vpEdgesStereo[i];

        const size_t idx = vnIndexEdgeStereo[i];

        if (!pFrame->mvbOutlier[idx])
        {
            //  0~14  |     15~20   | 21~29
            H.block<6, 6>(15, 15) += e->GetHessian();
            tot_in++;
        }
        else
            tot_out++;
    }

    // H  = |B  E.t| ------> |0             0|
    //      |E    A|         |0 A-E*B.inv*E.t|
    H = Marginalize(H, 0, 14);
    /*
    Marginalize里的函数在此处的调用等效于：
    Eigen::JacobiSVD<Eigen::MatrixXd> svd(H.block(0, 0, 15, 15), Eigen::ComputeThinU | Eigen::ComputeThinV);
    Eigen::JacobiSVD<Eigen::MatrixXd>::SingularValuesType singularValues_inv = svd.singularValues();
    for (int i = 0; i < 15; ++i)
    {
        if (singularValues_inv(i) > 1e-6)
            singularValues_inv(i) = 1.0 / singularValues_inv(i);
        else
            singularValues_inv(i) = 0;
    }
    Eigen::MatrixXd invHb = svd.matrixV() * singularValues_inv.asDiagonal() * svd.matrixU().transpose();
    H.block(15, 15, 15, 15) = H.block(15, 15, 15, 15) - H.block(15, 0, 15, 15) * invHb - H.block(0, 15, 15, 15);
    */

    //构造一个ConstraintPoseImu对象，为下一帧提供先验约束
    //构造对象所使用的参数是当前帧P、V、BG、BA的估计值和边缘化后的H矩阵
    pFrame->mpcpi = new ConstraintPoseImu(VP->estimate().Rwb, VP->estimate().twb, VV->estimate(), VG->estimate(), VA->estimate(), H.block<15, 15>(15, 15));
    //下一帧使用的EdgePriorPoseImu参数来自于此
    delete pFp->mpcpi;
    pFp->mpcpi = NULL;

    //返回值：内点数 = 总地图点数目 - 坏点（外点）数目
    return nInitialCorrespondences - nBad;
}

/**
 * @brief PoseInertialOptimizationLastFrame 中使用 Marginalize(H, 0, 14);
 * 使用舒尔补的方式边缘化海森矩阵，边缘化。
 * 列数 6            3                    3                            3                         6           3             3              3
 * --------------------------------------------------------------------------------------------------------------------------------------------------- 行数
 * |  Jp1.t * Jp1  Jp1.t * Jv1         Jp1.t * Jg1                 Jp1.t * Ja1            |  Jp1.t * Jp2  Jp1.t * Jv2        0              0        |  6
 * |  Jv1.t * Jp1  Jv1.t * Jv1         Jv1.t * Jg1                 Jv1.t * Ja1            |  Jv1.t * Jp2  Jv1.t * Jv2        0              0        |  3
 * |  Jg1.t * Jp1  Jg1.t * Jv1  Jg1.t * Jg1 + Jgr1.t * Jgr1        Jg1.t * Ja1            |  Jg1.t * Jp2  Jg1.t * Jv2  Jgr1.t * Jgr2        0        |  3
 * |  Ja1.t * Jp1  Ja1.t * Jv1         Ja1.t * Jg1           Ja1.t * Ja1 + Jar1.t * Jar1  |  Ja1.t * Jp2  Ja1.t * Jv2  Jar1.t * Jar2        0        |  3
 * |--------------------------------------------------------------------------------------------------------------------------------------------------
 * |  Jp2.t * Jp1  Jp2.t * Jv1         Jp2.t * Jg1                 Jp2.t * Ja1            |  Jp2.t * Jp2  Jp2.t * Jv2        0              0        |  6
 * |  Jv2.t * Jp1  Jv2.t * Jv1         Jv2.t * Jg1                 Jv2.t * Ja1            |  Jv2.t * Jp2  Jv2.t * Jv2        0              0        |  3
 * |      0            0              Jgr2.t * Jgr1                      0                |        0           0       Jgr2.t * Jgr2        0        |  3
 * |      0            0                    0                     Jar2.t * Jar1           |        0           0             0        Jar2.t * Jar2  |  3
 * ---------------------------------------------------------------------------------------------------------------------------------------------------
 * @param H 30*30的海森矩阵
 * @param start 开始位置
 * @param end 结束位置
 */
Eigen::MatrixXd Optimizer::Marginalize(const Eigen::MatrixXd &H, const int &start, const int &end)
{
    // Goal
    // a  | ab | ac       a*  | 0 | ac*
    // ba | b  | bc  -->  0   | 0 | 0
    // ca | cb | c        ca* | 0 | c*

    // Size of block before block to marginalize
    const int a = start;
    // Size of block to marginalize
    const int b = end - start + 1;
    // Size of block after block to marginalize
    const int c = H.cols() - (end + 1);

    // Reorder as follows:
    // a  | ab | ac       a  | ac | ab
    // ba | b  | bc  -->  ca | c  | cb
    // ca | cb | c        ba | bc | b

    Eigen::MatrixXd Hn = Eigen::MatrixXd::Zero(H.rows(), H.cols());
    if (a > 0)
    {
        Hn.block(0, 0, a, a) = H.block(0, 0, a, a);
        Hn.block(0, a + c, a, b) = H.block(0, a, a, b);
        Hn.block(a + c, 0, b, a) = H.block(a, 0, b, a);
    }
    if (a > 0 && c > 0)
    {
        Hn.block(0, a, a, c) = H.block(0, a + b, a, c);
        Hn.block(a, 0, c, a) = H.block(a + b, 0, c, a);
    }
    if (c > 0)
    {
        Hn.block(a, a, c, c) = H.block(a + b, a + b, c, c);
        Hn.block(a, a + c, c, b) = H.block(a + b, a, c, b);
        Hn.block(a + c, a, b, c) = H.block(a, a + b, b, c);
    }
    Hn.block(a + c, a + c, b, b) = H.block(a, a, b, b);

    // Perform marginalization (Schur complement)
    Eigen::JacobiSVD<Eigen::MatrixXd> svd(Hn.block(a + c, a + c, b, b), Eigen::ComputeThinU | Eigen::ComputeThinV);
    Eigen::JacobiSVD<Eigen::MatrixXd>::SingularValuesType singularValues_inv = svd.singularValues();
    for (int i = 0; i < b; ++i)
    {
        if (singularValues_inv(i) > 1e-6)
            singularValues_inv(i) = 1.0 / singularValues_inv(i);
        else
            singularValues_inv(i) = 0;
    }
    Eigen::MatrixXd invHb = svd.matrixV() * singularValues_inv.asDiagonal() * svd.matrixU().transpose();
    Hn.block(0, 0, a + c, a + c) = Hn.block(0, 0, a + c, a + c) - Hn.block(0, a + c, a + c, b) * invHb * Hn.block(a + c, 0, b, a + c);
    Hn.block(a + c, a + c, b, b) = Eigen::MatrixXd::Zero(b, b);
    Hn.block(0, a + c, a + c, b) = Eigen::MatrixXd::Zero(a + c, b);
    Hn.block(a + c, 0, b, a + c) = Eigen::MatrixXd::Zero(b, a + c);

    // Inverse reorder
    // a*  | ac* | 0       a*  | 0 | ac*
    // ca* | c*  | 0  -->  0   | 0 | 0
    // 0   | 0   | 0       ca* | 0 | c*
    Eigen::MatrixXd res = Eigen::MatrixXd::Zero(H.rows(), H.cols());
    if (a > 0)
    {
        res.block(0, 0, a, a) = Hn.block(0, 0, a, a);
        res.block(0, a, a, b) = Hn.block(0, a + c, a, b);
        res.block(a, 0, b, a) = Hn.block(a + c, 0, b, a);
    }
    if (a > 0 && c > 0)
    {
        res.block(0, a + b, a, c) = Hn.block(0, a, a, c);
        res.block(a + b, 0, c, a) = Hn.block(a, 0, c, a);
    }
    if (c > 0)
    {
        res.block(a + b, a + b, c, c) = Hn.block(a, a, c, c);
        res.block(a + b, a, c, b) = Hn.block(a, a + c, c, b);
        res.block(a, a + b, b, c) = Hn.block(a + c, a, b, c);
    }

    res.block(a, a, b, b) = Hn.block(a + c, a + c, b, b);

    return res;
}


/**************************************以下为局部地图优化**************************************************************/

/**
 * @brief Local Bundle Adjustment LocalMapping::Run() 使用，纯视觉
 * @param pKF        KeyFrame
 * @param pbStopFlag 是否停止优化的标志
 * @param pMap       在优化后，更新状态时需要用到Map的互斥量mMutexMapUpdate
 * @param 剩下的都是调试用的
 *
 * 总结下与ORBSLAM2的不同
 * 前面操作基本一样，但优化时2代去掉了误差大的点又进行优化了，3代只是统计但没有去掉继续优化，而后都是将误差大的点干掉
 */
void Optimizer::LocalBundleAdjustment(KeyFrame *pKF, bool *pbStopFlag, Map *pMap, int &num_fixedKF, int &num_OptKF, int &num_MPs, int &num_edges)
{
    // 该优化函数用于LocalMapping线程的局部BA优化
    // Local KeyFrames: First Breath Search from Current Keyframe
    list<KeyFrame *> lLocalKeyFrames;

    // 步骤1：将当前关键帧加入lLocalKeyFrames
    lLocalKeyFrames.push_back(pKF);
    pKF->mnBALocalForKF = pKF->mnId;
    Map *pCurrentMap = pKF->GetMap();

    // 步骤2：找到关键帧连接的关键帧（一级相连），加入lLocalKeyFrames中
    const vector<KeyFrame *> vNeighKFs = pKF->GetVectorCovisibleKeyFrames();
    for (int i = 0, iend = vNeighKFs.size(); i < iend; i++)
    {
        KeyFrame *pKFi = vNeighKFs[i];
        // 记录局部优化id，该数为不断变化，数值等于局部化的关键帧的id，该id用于防止重复添加
        pKFi->mnBALocalForKF = pKF->mnId;
        if (!pKFi->isBad() && pKFi->GetMap() == pCurrentMap)
            lLocalKeyFrames.push_back(pKFi);
    }

    // Local MapPoints seen in Local KeyFrames
    num_fixedKF = 0;
    // 步骤3：遍历lLocalKeyFrames中关键帧，将它们观测的MapPoints加入到lLocalMapPoints
    list<MapPoint *> lLocalMapPoints;
    set<MapPoint *> sNumObsMP;
    for (list<KeyFrame *>::iterator lit = lLocalKeyFrames.begin(), lend = lLocalKeyFrames.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        if (pKFi->mnId == pMap->GetInitKFid())
        {
            num_fixedKF = 1;
        }
        vector<MapPoint *> vpMPs = pKFi->GetMapPointMatches();
        for (vector<MapPoint *>::iterator vit = vpMPs.begin(), vend = vpMPs.end(); vit != vend; vit++)
        {
            MapPoint *pMP = *vit;
            if (pMP)
                if (!pMP->isBad() && pMP->GetMap() == pCurrentMap)
                {

                    if (pMP->mnBALocalForKF != pKF->mnId)
                    {
                        lLocalMapPoints.push_back(pMP);
                        pMP->mnBALocalForKF = pKF->mnId;
                    }
                }
        }
    }

    // Fixed Keyframes. Keyframes that see Local MapPoints but that are not Local Keyframes
    // 步骤4：得到能被局部MapPoints观测到，但不属于局部关键帧的关键帧，这些关键帧在局部BA优化时不优化
    list<KeyFrame *> lFixedCameras;
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        map<KeyFrame *, tuple<int, int>> observations = (*lit)->GetObservations();
        for (map<KeyFrame *, tuple<int, int>>::iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (pKFi->mnBALocalForKF != pKF->mnId && pKFi->mnBAFixedForKF != pKF->mnId)
            {
                pKFi->mnBAFixedForKF = pKF->mnId;
                if (!pKFi->isBad() && pKFi->GetMap() == pCurrentMap)
                    lFixedCameras.push_back(pKFi);
            }
        }
    }
    // 步骤4.1：相比ORBSLAM2多出了判断固定关键帧的个数，最起码要两个固定的,如果实在没有就把lLocalKeyFrames中最早的KF固定，还是不够再加上第二早的KF固定
    num_fixedKF = lFixedCameras.size() + num_fixedKF;

    // 1.0 版本没有以下这段
    // if (num_fixedKF < 2)
    // {
    //     list<KeyFrame *>::iterator lit = lLocalKeyFrames.begin();
    //     int lowerId = pKF->mnId;
    //     KeyFrame *pLowerKf;
    //     int secondLowerId = pKF->mnId;
    //     KeyFrame *pSecondLowerKF;

    //     for (; lit != lLocalKeyFrames.end(); lit++)
    //     {
    //         KeyFrame *pKFi = *lit;
    //         if (pKFi == pKF || pKFi->mnId == pMap->GetInitKFid())
    //         {
    //             continue;
    //         }

    //         if (pKFi->mnId < lowerId)
    //         {
    //             lowerId = pKFi->mnId;
    //             pLowerKf = pKFi;
    //         }
    //         else if (pKFi->mnId < secondLowerId)
    //         {
    //             secondLowerId = pKFi->mnId;
    //             pSecondLowerKF = pKFi;
    //         }
    //     }
    //     lFixedCameras.push_back(pLowerKf);
    //     lLocalKeyFrames.remove(pLowerKf);
    //     num_fixedKF++;
    //     if (num_fixedKF < 2)
    //     {
    //         lFixedCameras.push_back(pSecondLowerKF);
    //         lLocalKeyFrames.remove(pSecondLowerKF);
    //         num_fixedKF++;
    //     }
    // }

    if (num_fixedKF == 0)
    {
        Verbose::PrintMess("LM-LBA: There are 0 fixed KF in the optimizations, LBA aborted", Verbose::VERBOSITY_NORMAL);
        return;
    }

    // Setup optimizer
    // 步骤5：构造g2o优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolver_6_3::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolver_6_3::PoseMatrixType>();

    g2o::BlockSolver_6_3 *solver_ptr = new g2o::BlockSolver_6_3(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    if (pMap->IsInertial())
        solver->setUserLambdaInit(100.0);

    optimizer.setAlgorithm(solver);
    optimizer.setVerbose(false);

    if (pbStopFlag)
        optimizer.setForceStopFlag(pbStopFlag);

    unsigned long maxKFid = 0;

    // DEBUG LBA
    pCurrentMap->msOptKFs.clear();
    pCurrentMap->msFixedKFs.clear();

    // Set Local KeyFrame vertices
    // 步骤6：添加顶点：Pose of Local KeyFrame
    for (list<KeyFrame *>::iterator lit = lLocalKeyFrames.begin(), lend = lLocalKeyFrames.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
        Sophus::SE3<float> Tcw = pKFi->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        vSE3->setId(pKFi->mnId);
        vSE3->setFixed(pKFi->mnId == pMap->GetInitKFid());
        optimizer.addVertex(vSE3);
        if (pKFi->mnId > maxKFid)
            maxKFid = pKFi->mnId;
        // DEBUG LBA
        pCurrentMap->msOptKFs.insert(pKFi->mnId);
    }
    num_OptKF = lLocalKeyFrames.size();

    // Set Fixed KeyFrame vertices
    // 步骤7：添加顶点：Pose of Fixed KeyFrame，注意这里调用了vSE3->setFixed(true)。
    for (list<KeyFrame *>::iterator lit = lFixedCameras.begin(), lend = lFixedCameras.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
        Sophus::SE3<float> Tcw = pKFi->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        vSE3->setId(pKFi->mnId);
        vSE3->setFixed(true);
        optimizer.addVertex(vSE3);
        if (pKFi->mnId > maxKFid)
            maxKFid = pKFi->mnId;
        // DEBUG LBA
        pCurrentMap->msFixedKFs.insert(pKFi->mnId);
    }

    // Set MapPoint vertices
    // 步骤7：添加3D顶点
    // 存放的方式(举例)
    // 边id: 1 2 3 4 5 6 7 8 9
    // KFid: 1 2 3 4 1 2 3 2 3
    // MPid: 1 1 1 1 2 2 2 3 3
    // 所以这个个数大约是点数×帧数，实际肯定比这个要少
    const int nExpectedSize = (lLocalKeyFrames.size() + lFixedCameras.size()) * lLocalMapPoints.size();

    // 存放单目时的边
    vector<ORB_SLAM3::EdgeSE3ProjectXYZ *> vpEdgesMono;
    vpEdgesMono.reserve(nExpectedSize);
    // 存放双目鱼眼时另一个相机的边
    vector<ORB_SLAM3::EdgeSE3ProjectXYZToBody *> vpEdgesBody;
    vpEdgesBody.reserve(nExpectedSize);
    // 存放单目时的KF
    vector<KeyFrame *> vpEdgeKFMono;
    vpEdgeKFMono.reserve(nExpectedSize);
    // 存放单目时的MP

    // 存放双目鱼眼时另一个相机的KF
    vector<KeyFrame *> vpEdgeKFBody;
    vpEdgeKFBody.reserve(nExpectedSize);
    // 存放单目时的MP
    vector<MapPoint *> vpMapPointEdgeMono;
    vpMapPointEdgeMono.reserve(nExpectedSize);
    // 存放双目鱼眼时另一个相机的MP
    vector<MapPoint *> vpMapPointEdgeBody;
    vpMapPointEdgeBody.reserve(nExpectedSize);

    // 存放双目时的边
    vector<g2o::EdgeStereoSE3ProjectXYZ *> vpEdgesStereo;
    vpEdgesStereo.reserve(nExpectedSize);
    // 存放双目时的KF
    vector<KeyFrame *> vpEdgeKFStereo;
    vpEdgeKFStereo.reserve(nExpectedSize);
    // 存放双目时的MP
    vector<MapPoint *> vpMapPointEdgeStereo;
    vpMapPointEdgeStereo.reserve(nExpectedSize);

    const float thHuberMono = sqrt(5.991);
    const float thHuberStereo = sqrt(7.815);

    int nPoints = 0;

    int nEdges = 0;

    // 添加顶点：MapPoint
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        MapPoint *pMP = *lit;
        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMP->GetWorldPos().cast<double>());
        int id = pMP->mnId + maxKFid + 1;
        vPoint->setId(id);
        // 这里的边缘化与滑动窗口不同，而是为了加速稀疏矩阵的计算BlockSolver_6_3默认了6维度的不边缘化，3自由度的三维点被边缘化，所以所有三维点都设置边缘化
        vPoint->setMarginalized(true);
        optimizer.addVertex(vPoint);
        nPoints++;

        const map<KeyFrame *, tuple<int, int>> observations = pMP->GetObservations();

        // Set edges
        //  步骤8：对每一对关联的MapPoint和KeyFrame构建边
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (!pKFi->isBad() && pKFi->GetMap() == pCurrentMap)
            {
                const int leftIndex = get<0>(mit->second);

                // Monocular observation
                // 单目
                if (leftIndex != -1 && pKFi->mvuRight[get<0>(mit->second)] < 0)
                {
                    const cv::KeyPoint &kpUn = pKFi->mvKeysUn[leftIndex];
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    ORB_SLAM3::EdgeSE3ProjectXYZ *e = new ORB_SLAM3::EdgeSE3ProjectXYZ();

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);
                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);

                    e->pCamera = pKFi->mpCamera;

                    optimizer.addEdge(e);
                    vpEdgesMono.push_back(e);
                    vpEdgeKFMono.push_back(pKFi);
                    vpMapPointEdgeMono.push_back(pMP);

                    nEdges++;
                }
                else if (leftIndex != -1 && pKFi->mvuRight[get<0>(mit->second)] >= 0) // Stereo observation
                {
                    const cv::KeyPoint &kpUn = pKFi->mvKeysUn[leftIndex];
                    Eigen::Matrix<double, 3, 1> obs;
                    const float kp_ur = pKFi->mvuRight[get<0>(mit->second)];
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    g2o::EdgeStereoSE3ProjectXYZ *e = new g2o::EdgeStereoSE3ProjectXYZ();

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);
                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];
                    Eigen::Matrix3d Info = Eigen::Matrix3d::Identity() * invSigma2;
                    e->setInformation(Info);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    e->fx = pKFi->fx;
                    e->fy = pKFi->fy;
                    e->cx = pKFi->cx;
                    e->cy = pKFi->cy;
                    e->bf = pKFi->mbf;

                    optimizer.addEdge(e);
                    vpEdgesStereo.push_back(e);
                    vpEdgeKFStereo.push_back(pKFi);
                    vpMapPointEdgeStereo.push_back(pMP);

                    nEdges++;
                }

                if (pKFi->mpCamera2)
                {
                    int rightIndex = get<1>(mit->second);

                    if (rightIndex != -1)
                    {
                        rightIndex -= pKFi->NLeft;

                        Eigen::Matrix<double, 2, 1> obs;
                        cv::KeyPoint kp = pKFi->mvKeysRight[rightIndex];
                        obs << kp.pt.x, kp.pt.y;

                        ORB_SLAM3::EdgeSE3ProjectXYZToBody *e = new ORB_SLAM3::EdgeSE3ProjectXYZToBody();

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                        e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                        e->setMeasurement(obs);
                        const float &invSigma2 = pKFi->mvInvLevelSigma2[kp.octave];
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(thHuberMono);

                        Sophus::SE3f Trl = pKFi->GetRelativePoseTrl();
                        e->mTrl = g2o::SE3Quat(Trl.unit_quaternion().cast<double>(), Trl.translation().cast<double>());

                        e->pCamera = pKFi->mpCamera2;

                        optimizer.addEdge(e);
                        vpEdgesBody.push_back(e);
                        vpEdgeKFBody.push_back(pKFi);
                        vpMapPointEdgeBody.push_back(pMP);

                        nEdges++;
                    }
                }
            }
        }
    }
    num_edges = nEdges;

    if (pbStopFlag)
        if (*pbStopFlag)
            return;

    // 步骤9：开始优化
    optimizer.initializeOptimization();
    optimizer.optimize(10);

    vector<pair<KeyFrame *, MapPoint *>> vToErase;
    vToErase.reserve(vpEdgesMono.size() + vpEdgesBody.size() + vpEdgesStereo.size());

    // Check inlier observations
    // 步骤10：在优化后重新计算误差，剔除连接误差比较大的关键帧和MapPoint
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        ORB_SLAM3::EdgeSE3ProjectXYZ *e = vpEdgesMono[i];
        MapPoint *pMP = vpMapPointEdgeMono[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > 5.991 || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFMono[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    for (size_t i = 0, iend = vpEdgesBody.size(); i < iend; i++)
    {
        ORB_SLAM3::EdgeSE3ProjectXYZToBody *e = vpEdgesBody[i];
        MapPoint *pMP = vpMapPointEdgeBody[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > 5.991 || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFBody[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        g2o::EdgeStereoSE3ProjectXYZ *e = vpEdgesStereo[i];
        MapPoint *pMP = vpMapPointEdgeStereo[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > 7.815 || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFStereo[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    // Get Map Mutex
    unique_lock<mutex> lock(pMap->mMutexMapUpdate);

    if (!vToErase.empty())
    {
        for (size_t i = 0; i < vToErase.size(); i++)
        {
            KeyFrame *pKFi = vToErase[i].first;
            MapPoint *pMPi = vToErase[i].second;
            pKFi->EraseMapPointMatch(pMPi);
            pMPi->EraseObservation(pKFi);
        }
    }

    // Recover optimized data
    // Keyframes
    bool bShowStats = false;
    for (list<KeyFrame *>::iterator lit = lLocalKeyFrames.begin(), lend = lLocalKeyFrames.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        g2o::VertexSE3Expmap *vSE3 = static_cast<g2o::VertexSE3Expmap *>(optimizer.vertex(pKFi->mnId));
        g2o::SE3Quat SE3quat = vSE3->estimate();
        Sophus::SE3f Tiw(SE3quat.rotation().cast<float>(), SE3quat.translation().cast<float>());
        pKFi->SetPose(Tiw);
    }

    // Points
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        MapPoint *pMP = *lit;
        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMP->mnId + maxKFid + 1));
        pMP->SetWorldPos(vPoint->estimate().cast<float>());
        pMP->UpdateNormalAndDepth();
    }

    pMap->IncreaseChangeIndex();
}

/**
 * @brief 局部地图＋惯导BA LocalMapping IMU中使用，地图经过imu初始化时用这个函数代替LocalBundleAdjustment
 *
 * @param[in] pKF               //关键帧
 * @param[in] pbStopFlag        //是否停止的标志
 * @param[in] pMap              //地图
 * @param[in] num_fixedKF       //固定不优化关键帧的数目
 * @param[in] num_OptKF
 * @param[in] num_MPs
 * @param[in] num_edges
 * @param[in] bLarge            成功跟踪匹配的点数是否足够多
 * @param[in] bRecInit          经过BA2初始化后这个变量为false
 */
void Optimizer::LocalInertialBA(
    KeyFrame *pKF, bool *pbStopFlag, Map *pMap, int &num_fixedKF, int &num_OptKF,
    int &num_MPs, int &num_edges, bool bLarge, bool bRecInit)
{
    // Step 1. 确定待优化的关键帧们
    Map *pCurrentMap = pKF->GetMap();

    int maxOpt = 10; // 最大优化关键帧数目
    int opt_it = 10; // 每次优化的迭代次数
    if (bLarge)
    {
        maxOpt = 25;
        opt_it = 4;
    }
    // 预计待优化的关键帧数，min函数是为了控制优化关键帧的数量
    const int Nd = std::min((int)pCurrentMap->KeyFramesInMap() - 2, maxOpt);
    const unsigned long maxKFid = pKF->mnId;

    vector<KeyFrame *> vpOptimizableKFs;
    const vector<KeyFrame *> vpNeighsKFs = pKF->GetVectorCovisibleKeyFrames();
    list<KeyFrame *> lpOptVisKFs;

    vpOptimizableKFs.reserve(Nd);
    vpOptimizableKFs.push_back(pKF);
    pKF->mnBALocalForKF = pKF->mnId;
    for (int i = 1; i < Nd; i++)
    {
        if (vpOptimizableKFs.back()->mPrevKF)
        {
            vpOptimizableKFs.push_back(vpOptimizableKFs.back()->mPrevKF);
            vpOptimizableKFs.back()->mnBALocalForKF = pKF->mnId;
        }
        else
            break;
    }

    int N = vpOptimizableKFs.size();

    // Optimizable points seen by temporal optimizable keyframes
    // Step 2. 确定这些关键帧对应的地图点，存入lLocalMapPoints
    list<MapPoint *> lLocalMapPoints;
    for (int i = 0; i < N; i++)
    {
        vector<MapPoint *> vpMPs = vpOptimizableKFs[i]->GetMapPointMatches();
        for (vector<MapPoint *>::iterator vit = vpMPs.begin(), vend = vpMPs.end(); vit != vend; vit++)
        {
            MapPoint *pMP = *vit;
            if (pMP)
                if (!pMP->isBad())
                    if (pMP->mnBALocalForKF != pKF->mnId)
                    {
                        lLocalMapPoints.push_back(pMP);
                        pMP->mnBALocalForKF = pKF->mnId;
                    }
        }
    }

    // Fixed Keyframe: First frame previous KF to optimization window)
    // Step 3. 固定一帧，为vpOptimizableKFs中最早的那一关键帧的上一关键帧，如果没有上一关键帧了就用最早的那一帧，毕竟目前得到的地图虽然有尺度但并不是绝对的位置
    list<KeyFrame *> lFixedKeyFrames;
    if (vpOptimizableKFs.back()->mPrevKF)
    {
        lFixedKeyFrames.push_back(vpOptimizableKFs.back()->mPrevKF);
        vpOptimizableKFs.back()->mPrevKF->mnBAFixedForKF = pKF->mnId;
    }
    else
    {
        vpOptimizableKFs.back()->mnBALocalForKF = 0;
        vpOptimizableKFs.back()->mnBAFixedForKF = pKF->mnId;
        lFixedKeyFrames.push_back(vpOptimizableKFs.back());
        vpOptimizableKFs.pop_back();
    }

    // Optimizable visual KFs
    // 4. 做了一系列操作发现最后lpOptVisKFs为空。这段应该是调试遗留代码，如果实现的话其实就是把共视图中在前面没有加过的关键帧们加进来，
    // 但作者可能发现之前就把共视图的全部帧加进来了，也有可能发现优化的效果不好浪费时间
    // 获得与当前关键帧有共视关系的一些关键帧，大于15个点，排序为从小到大
    const int maxCovKF = 0;
    for (int i = 0, iend = vpNeighsKFs.size(); i < iend; i++)
    {
        if (lpOptVisKFs.size() >= maxCovKF)
            break;

        KeyFrame *pKFi = vpNeighsKFs[i];
        if (pKFi->mnBALocalForKF == pKF->mnId || pKFi->mnBAFixedForKF == pKF->mnId)
            continue;
        pKFi->mnBALocalForKF = pKF->mnId;
        if (!pKFi->isBad() && pKFi->GetMap() == pCurrentMap)
        {
            lpOptVisKFs.push_back(pKFi);

            vector<MapPoint *> vpMPs = pKFi->GetMapPointMatches();
            for (vector<MapPoint *>::iterator vit = vpMPs.begin(), vend = vpMPs.end(); vit != vend; vit++)
            {
                MapPoint *pMP = *vit;
                if (pMP)
                    if (!pMP->isBad())
                        if (pMP->mnBALocalForKF != pKF->mnId)
                        {
                            lLocalMapPoints.push_back(pMP);
                            pMP->mnBALocalForKF = pKF->mnId;
                        }
            }
        }
    }

    // Fixed KFs which are not covisible optimizable
    // 5. 将所有mp点对应的关键帧（除了前面加过的）放入到固定组里面，后面优化时不改变
    const int maxFixKF = 200;

    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        map<KeyFrame *, tuple<int, int>> observations = (*lit)->GetObservations();
        for (map<KeyFrame *, tuple<int, int>>::iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (pKFi->mnBALocalForKF != pKF->mnId && pKFi->mnBAFixedForKF != pKF->mnId)
            {
                pKFi->mnBAFixedForKF = pKF->mnId;
                if (!pKFi->isBad())
                {
                    lFixedKeyFrames.push_back(pKFi);
                    break;
                }
            }
        }
        if (lFixedKeyFrames.size() >= maxFixKF)
            break;
    }

    bool bNonFixed = (lFixedKeyFrames.size() == 0);

    // Setup optimizer
    // 6. 构造优化器，正式开始优化
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;
    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    if (bLarge)
    {
        g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
        solver->setUserLambdaInit(1e-2); // to avoid iterating for finding optimal lambda
        optimizer.setAlgorithm(solver);
    }
    else
    {
        g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
        solver->setUserLambdaInit(1e0);
        optimizer.setAlgorithm(solver);
    }

    // Set Local temporal KeyFrame vertices
    // 7. 建立关于关键帧的节点，其中包括，位姿，速度，以及两个偏置
    N = vpOptimizableKFs.size();
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];

        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(false);
        optimizer.addVertex(VP);

        if (pKFi->bImu)
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(false);
            optimizer.addVertex(VV);
            VertexGyroBias *VG = new VertexGyroBias(pKFi);
            VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
            VG->setFixed(false);
            optimizer.addVertex(VG);
            VertexAccBias *VA = new VertexAccBias(pKFi);
            VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
            VA->setFixed(false);
            optimizer.addVertex(VA);
        }
    }

    // Set Local visual KeyFrame vertices
    // 8. 建立关于共视关键帧的节点，但这里为空
    for (list<KeyFrame *>::iterator it = lpOptVisKFs.begin(), itEnd = lpOptVisKFs.end(); it != itEnd; it++)
    {
        KeyFrame *pKFi = *it;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(false);
        optimizer.addVertex(VP);
    }

    // Set Fixed KeyFrame vertices
    // 9. 建立关于固定关键帧的节点，其中包括，位姿，速度，以及两个偏置
    for (list<KeyFrame *>::iterator lit = lFixedKeyFrames.begin(), lend = lFixedKeyFrames.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(true);
        optimizer.addVertex(VP);

        if (pKFi->bImu) // This should be done only for keyframe just before temporal window
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(true);
            optimizer.addVertex(VV);
            VertexGyroBias *VG = new VertexGyroBias(pKFi);
            VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
            VG->setFixed(true);
            optimizer.addVertex(VG);
            VertexAccBias *VA = new VertexAccBias(pKFi);
            VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
            VA->setFixed(true);
            optimizer.addVertex(VA);
        }
    }

    // Create intertial constraints
    // 暂时没看到有什么用
    vector<EdgeInertial *> vei(N, (EdgeInertial *)NULL);
    vector<EdgeGyroRW *> vegr(N, (EdgeGyroRW *)NULL);
    vector<EdgeAccRW *> vear(N, (EdgeAccRW *)NULL);
    // 10. 建立惯性边，没有imu跳过
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];

        if (!pKFi->mPrevKF)
        {
            cout << "NOT INERTIAL LINK TO PREVIOUS FRAME!!!!" << endl;
            continue;
        }
        if (pKFi->bImu && pKFi->mPrevKF->bImu && pKFi->mpImuPreintegrated)
        {
            pKFi->mpImuPreintegrated->SetNewBias(pKFi->mPrevKF->GetImuBias());
            g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VV1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 1);
            g2o::HyperGraph::Vertex *VG1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 2);
            g2o::HyperGraph::Vertex *VA1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 3);
            g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
            g2o::HyperGraph::Vertex *VV2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1);
            g2o::HyperGraph::Vertex *VG2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2);
            g2o::HyperGraph::Vertex *VA2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3);

            if (!VP1 || !VV1 || !VG1 || !VA1 || !VP2 || !VV2 || !VG2 || !VA2)
            {
                cerr << "Error " << VP1 << ", " << VV1 << ", " << VG1 << ", " << VA1 << ", " << VP2 << ", " << VV2 << ", " << VG2 << ", " << VA2 << endl;
                continue;
            }

            vei[i] = new EdgeInertial(pKFi->mpImuPreintegrated);

            vei[i]->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
            vei[i]->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
            vei[i]->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG1));
            vei[i]->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA1));
            vei[i]->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
            vei[i]->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));

            // 到最早的一个可优化的关键帧或者地图没有ba2时添加鲁棒核函数
            if (i == N - 1 || bRecInit)
            {
                // All inertial residuals are included without robust cost function, but not that one linking the
                // last optimizable keyframe inside of the local window and the first fixed keyframe out. The
                // information matrix for this measurement is also downweighted. This is done to avoid accumulating
                // error due to fixing variables.
                // 所有惯性残差都没有鲁棒核，但不包括窗口内最早一个可优化关键帧与第一个固定关键帧链接起来的惯性残差。
                // 该度量的信息矩阵也被降权。这样做是为了避免由于固定变量而累积错误。
                g2o::RobustKernelHuber *rki = new g2o::RobustKernelHuber;
                vei[i]->setRobustKernel(rki);
                if (i == N - 1)
                    vei[i]->setInformation(vei[i]->information() * 1e-2);
                rki->setDelta(sqrt(16.92));
            }
            optimizer.addEdge(vei[i]);

            vegr[i] = new EdgeGyroRW();
            vegr[i]->setVertex(0, VG1);
            vegr[i]->setVertex(1, VG2);
            Eigen::Matrix3d InfoG = pKFi->mpImuPreintegrated->C.block<3, 3>(9, 9).cast<double>().inverse();
            vegr[i]->setInformation(InfoG);
            optimizer.addEdge(vegr[i]);

            vear[i] = new EdgeAccRW();
            vear[i]->setVertex(0, VA1);
            vear[i]->setVertex(1, VA2);
            Eigen::Matrix3d InfoA = pKFi->mpImuPreintegrated->C.block<3, 3>(12, 12).cast<double>().inverse();
            vear[i]->setInformation(InfoA);

            optimizer.addEdge(vear[i]);
        }
        else
            cout << "ERROR building inertial edge" << endl;
    }

    // Set MapPoint vertices
    const int nExpectedSize = (N + lFixedKeyFrames.size()) * lLocalMapPoints.size();

    // Mono
    vector<EdgeMono *> vpEdgesMono;
    vpEdgesMono.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFMono;
    vpEdgeKFMono.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeMono;
    vpMapPointEdgeMono.reserve(nExpectedSize);

    // Stereo
    vector<EdgeStereo *> vpEdgesStereo;
    vpEdgesStereo.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFStereo;
    vpEdgeKFStereo.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeStereo;
    vpMapPointEdgeStereo.reserve(nExpectedSize);

    // 11. 建立视觉边
    const float thHuberMono = sqrt(5.991);
    const float chi2Mono2 = 5.991;
    const float thHuberStereo = sqrt(7.815);
    const float chi2Stereo2 = 7.815;

    const unsigned long iniMPid = maxKFid * 5;

    map<int, int> mVisEdges;
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];
        mVisEdges[pKFi->mnId] = 0;
    }
    for (list<KeyFrame *>::iterator lit = lFixedKeyFrames.begin(), lend = lFixedKeyFrames.end(); lit != lend; lit++)
    {
        mVisEdges[(*lit)->mnId] = 0;
    }

    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        MapPoint *pMP = *lit;
        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMP->GetWorldPos().cast<double>());

        unsigned long id = pMP->mnId + iniMPid + 1;
        vPoint->setId(id);
        vPoint->setMarginalized(true);
        optimizer.addVertex(vPoint);
        const map<KeyFrame *, tuple<int, int>> observations = pMP->GetObservations();

        // Create visual constraints
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (pKFi->mnBALocalForKF != pKF->mnId && pKFi->mnBAFixedForKF != pKF->mnId)
                continue;

            if (!pKFi->isBad() && pKFi->GetMap() == pCurrentMap)
            {
                const int leftIndex = get<0>(mit->second);

                cv::KeyPoint kpUn;

                // Monocular left observation
                if (leftIndex != -1 && pKFi->mvuRight[leftIndex] < 0)
                {
                    mVisEdges[pKFi->mnId]++;

                    kpUn = pKFi->mvKeysUn[leftIndex];
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    EdgeMono *e = new EdgeMono(0);

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pKFi->mpCamera->uncertainty2(obs);

                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);

                    optimizer.addEdge(e);
                    vpEdgesMono.push_back(e);
                    vpEdgeKFMono.push_back(pKFi);
                    vpMapPointEdgeMono.push_back(pMP);
                }
                // Stereo-observation
                else if (leftIndex != -1) // Stereo observation
                {
                    kpUn = pKFi->mvKeysUn[leftIndex];
                    mVisEdges[pKFi->mnId]++;

                    const float kp_ur = pKFi->mvuRight[leftIndex];
                    Eigen::Matrix<double, 3, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    EdgeStereo *e = new EdgeStereo(0);

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);

                    // Add here uncerteinty
                    const float unc2 = pKFi->mpCamera->uncertainty2(obs.head(2));

                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave] / unc2;
                    e->setInformation(Eigen::Matrix3d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    optimizer.addEdge(e);
                    vpEdgesStereo.push_back(e);
                    vpEdgeKFStereo.push_back(pKFi);
                    vpMapPointEdgeStereo.push_back(pMP);
                }

                // Monocular right observation
                if (pKFi->mpCamera2)
                {
                    int rightIndex = get<1>(mit->second);

                    if (rightIndex != -1)
                    {
                        rightIndex -= pKFi->NLeft;
                        mVisEdges[pKFi->mnId]++;

                        Eigen::Matrix<double, 2, 1> obs;
                        cv::KeyPoint kp = pKFi->mvKeysRight[rightIndex];
                        obs << kp.pt.x, kp.pt.y;

                        EdgeMono *e = new EdgeMono(1);

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                        e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                        e->setMeasurement(obs);

                        // Add here uncerteinty
                        const float unc2 = pKFi->mpCamera->uncertainty2(obs);

                        const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave] / unc2;
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(thHuberMono);

                        optimizer.addEdge(e);
                        vpEdgesMono.push_back(e);
                        vpEdgeKFMono.push_back(pKFi);
                        vpMapPointEdgeMono.push_back(pMP);
                    }
                }
            }
        }
    }

    // cout << "Total map points: " << lLocalMapPoints.size() << endl;
    //  TODO debug会报错先注释掉
    for (map<int, int>::iterator mit = mVisEdges.begin(), mend = mVisEdges.end(); mit != mend; mit++)
    {
        assert(mit->second >= 3);
    }

    // 12. 开始优化
    optimizer.initializeOptimization();
    optimizer.computeActiveErrors();

    float err = optimizer.activeRobustChi2();
    optimizer.optimize(opt_it); // Originally to 2
    float err_end = optimizer.activeRobustChi2();
    if (pbStopFlag)
        optimizer.setForceStopFlag(pbStopFlag);

    vector<pair<KeyFrame *, MapPoint *>> vToErase;
    vToErase.reserve(vpEdgesMono.size() + vpEdgesStereo.size());

    // 13. 开始确认待删除的连接关系
    // Check inlier observations
    // Mono
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        EdgeMono *e = vpEdgesMono[i];
        MapPoint *pMP = vpMapPointEdgeMono[i];
        bool bClose = pMP->mTrackDepth < 10.f;

        if (pMP->isBad())
            continue;

        if ((e->chi2() > chi2Mono2 && !bClose) || (e->chi2() > 1.5f * chi2Mono2 && bClose) || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFMono[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    // Stereo
    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        EdgeStereo *e = vpEdgesStereo[i];
        MapPoint *pMP = vpMapPointEdgeStereo[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > chi2Stereo2)
        {
            KeyFrame *pKFi = vpEdgeKFStereo[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    // Get Map Mutex and erase outliers
    unique_lock<mutex> lock(pMap->mMutexMapUpdate);

    // TODO: Some convergence problems have been detected here
    if ((2 * err < err_end || isnan(err) || isnan(err_end)) && !bLarge) // bGN)
    {
        cout << "FAIL LOCAL-INERTIAL BA!!!!" << endl;
        return;
    }

    // 14. 删除连接关系
    if (!vToErase.empty())
    {
        for (size_t i = 0; i < vToErase.size(); i++)
        {
            KeyFrame *pKFi = vToErase[i].first;
            MapPoint *pMPi = vToErase[i].second;
            pKFi->EraseMapPointMatch(pMPi);
            pMPi->EraseObservation(pKFi);
        }
    }

    for (list<KeyFrame *>::iterator lit = lFixedKeyFrames.begin(), lend = lFixedKeyFrames.end(); lit != lend; lit++)
        (*lit)->mnBAFixedForKF = 0;

    // 15. 取出结果
    // Recover optimized data
    // Local temporal Keyframes
    N = vpOptimizableKFs.size();
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];

        VertexPose *VP = static_cast<VertexPose *>(optimizer.vertex(pKFi->mnId));
        Sophus::SE3f Tcw(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
        pKFi->SetPose(Tcw);
        pKFi->mnBALocalForKF = 0;

        if (pKFi->bImu)
        {
            VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1));
            pKFi->SetVelocity(VV->estimate().cast<float>());
            VertexGyroBias *VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2));
            VertexAccBias *VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3));
            Vector6d b;
            b << VG->estimate(), VA->estimate();
            pKFi->SetNewBias(IMU::Bias(b[3], b[4], b[5], b[0], b[1], b[2]));
        }
    }

    // Local visual KeyFrame
    // 空
    for (list<KeyFrame *>::iterator it = lpOptVisKFs.begin(), itEnd = lpOptVisKFs.end(); it != itEnd; it++)
    {
        KeyFrame *pKFi = *it;
        VertexPose *VP = static_cast<VertexPose *>(optimizer.vertex(pKFi->mnId));
        Sophus::SE3f Tcw(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
        pKFi->SetPose(Tcw);
        pKFi->mnBALocalForKF = 0;
    }

    // Points
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        MapPoint *pMP = *lit;
        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMP->mnId + iniMPid + 1));
        pMP->SetWorldPos(vPoint->estimate().cast<float>());
        pMP->UpdateNormalAndDepth();
    }

    pMap->IncreaseChangeIndex();
}

/**************************************以下为全局优化**************************************************************/

/**
 * @brief 全局BA： pMap中所有的MapPoints和关键帧做bundle adjustment优化
 * 这个全局BA优化在本程序中有两个地方使用：
 * 1、单目初始化：CreateInitialMapMonocular函数
 * 2、闭环优化：RunGlobalBundleAdjustment函数
 * @param[in] pMap                  地图点
 * @param[in] nIterations           迭代次数
 * @param[in] pbStopFlag            外部控制BA结束标志
 * @param[in] nLoopKF               形成了闭环的当前关键帧的id
 * @param[in] bRobust               是否使用鲁棒核函数
 */
void Optimizer::GlobalBundleAdjustemnt(Map *pMap, int nIterations, bool *pbStopFlag, const unsigned long nLoopKF, const bool bRobust)
{
    // 获取地图中的所有关键帧
    vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();
    // 获取地图中的所有地图点
    vector<MapPoint *> vpMP = pMap->GetAllMapPoints();
    // 调用GBA
    BundleAdjustment(vpKFs, vpMP, nIterations, pbStopFlag, nLoopKF, bRobust);
}

/**
 * @brief bundle adjustment 优化过程
 * @param[in] vpKFs                 参与BA的所有关键帧
 * @param[in] vpMP                  参与BA的所有地图点
 * @param[in] nIterations           优化迭代次数
 * @param[in] pbStopFlag            外部控制BA结束标志
 * @param[in] nLoopKF               形成了闭环的当前关键帧的id
 * @param[in] bRobust               是否使用核函数
 */
void Optimizer::BundleAdjustment(
    const vector<KeyFrame *> &vpKFs, const vector<MapPoint *> &vpMP,
    int nIterations, bool *pbStopFlag, const unsigned long nLoopKF, const bool bRobust)
{
    // 不参与优化的地图点，下面会用到
    vector<bool> vbNotIncludedMP;
    vbNotIncludedMP.resize(vpMP.size());

    Map *pMap = vpKFs[0]->GetMap();
    // Step 1 初始化g2o优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolver_6_3::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolver_6_3::PoseMatrixType>();

    g2o::BlockSolver_6_3 *solver_ptr = new g2o::BlockSolver_6_3(linearSolver);

    // 使用LM算法优化
    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    optimizer.setAlgorithm(solver);
    optimizer.setVerbose(false);
    // 如果这个时候外部请求终止，那就结束
    // 注意这句执行之后，外部再请求结束BA，就结束不了了
    if (pbStopFlag)
        optimizer.setForceStopFlag(pbStopFlag);

    // 记录添加到优化器中的顶点的最大关键帧id
    long unsigned int maxKFid = 0;

    const int nExpectedSize = (vpKFs.size()) * vpMP.size();

    vector<ORB_SLAM3::EdgeSE3ProjectXYZ *> vpEdgesMono;
    vpEdgesMono.reserve(nExpectedSize);

    vector<ORB_SLAM3::EdgeSE3ProjectXYZToBody *> vpEdgesBody;
    vpEdgesBody.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFMono;
    vpEdgeKFMono.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFBody;
    vpEdgeKFBody.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeMono;
    vpMapPointEdgeMono.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeBody;
    vpMapPointEdgeBody.reserve(nExpectedSize);

    vector<g2o::EdgeStereoSE3ProjectXYZ *> vpEdgesStereo;
    vpEdgesStereo.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFStereo;
    vpEdgeKFStereo.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeStereo;
    vpMapPointEdgeStereo.reserve(nExpectedSize);

    // Step 2 向优化器添加顶点
    // Set KeyFrame vertices
    // Step 2.1 ：向优化器添加关键帧位姿顶点
    // 对于当前地图中的所有关键帧
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKF = vpKFs[i];
        // 去除无效的
        if (pKF->isBad())
            continue;

        // 对于每一个能用的关键帧构造SE3顶点,其实就是当前关键帧的位姿
        g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
        Sophus::SE3<float> Tcw = pKF->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        vSE3->setId(pKF->mnId);
        // 只有第0帧关键帧不优化（参考基准）
        vSE3->setFixed(pKF->mnId == pMap->GetInitKFid());
        // 向优化器中添加顶点，并且更新maxKFid
        optimizer.addVertex(vSE3);
        if (pKF->mnId > maxKFid)
            maxKFid = pKF->mnId;
    }

    // 卡方分布 95% 以上可信度的时候的阈值
    const float thHuber2D = sqrt(5.99);  // 自由度为2
    const float thHuber3D = sqrt(7.815); // 自由度为3

    // Set MapPoint vertices
    // Step 2.2：向优化器添加MapPoints顶点
    // 遍历地图中的所有地图点
    for (size_t i = 0; i < vpMP.size(); i++)
    {
        MapPoint *pMP = vpMP[i];
        // 跳过无效地图点
        if (pMP->isBad())
            continue;

        // 创建顶点
        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMP->GetWorldPos().cast<double>());
        // 前面记录maxKFid 是在这里使用的
        const int id = pMP->mnId + maxKFid + 1;
        vPoint->setId(id);
        // g2o在做BA的优化时必须将其所有地图点全部schur掉，否则会出错。
        // 原因是使用了g2o::LinearSolver<BalBlockSolver::PoseMatrixType>这个类型来指定linearsolver,
        // 其中模板参数当中的位姿矩阵类型在程序中为相机姿态参数的维度，于是BA当中schur消元后解得线性方程组必须是只含有相机姿态变量。
        // Ceres库则没有这样的限制
        vPoint->setMarginalized(true);
        optimizer.addVertex(vPoint);

        // 边的关系，其实就是点和关键帧之间观测的关系
        const map<KeyFrame *, tuple<int, int>> observations = pMP->GetObservations();

        // 边计数
        int nEdges = 0;
        // SET EDGES
        //  Step 3：向优化器添加投影边（是在遍历地图点、添加地图点的顶点的时候顺便添加的）
        //  遍历观察到当前地图点的所有关键帧
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(); mit != observations.end(); mit++)
        {
            KeyFrame *pKF = mit->first;
            // 滤出不合法的关键帧
            if (pKF->isBad() || pKF->mnId > maxKFid)
                continue;
            if (optimizer.vertex(id) == NULL || optimizer.vertex(pKF->mnId) == NULL)
                continue;
            nEdges++;

            const int leftIndex = get<0>(mit->second);

            if (leftIndex != -1 && pKF->mvuRight[get<0>(mit->second)] < 0)
            {
                // 如果是单目相机按照下面操作
                const cv::KeyPoint &kpUn = pKF->mvKeysUn[leftIndex];

                Eigen::Matrix<double, 2, 1> obs;
                obs << kpUn.pt.x, kpUn.pt.y;

                // 创建边
                ORB_SLAM3::EdgeSE3ProjectXYZ *e = new ORB_SLAM3::EdgeSE3ProjectXYZ();
                // 填充数据，构造约束关系
                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mnId)));
                e->setMeasurement(obs);
                // 信息矩阵，也是协方差，表明了这个约束的观测在各个维度（x,y）上的可信程度，在我们这里对于具体的一个点，两个坐标的可信程度都是相同的，
                // 其可信程度受到特征点在图像金字塔中的图层有关，图层越高，可信度越差
                // 为了避免出现信息矩阵中元素为负数的情况，这里使用的是sigma^(-2)
                const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave];
                e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                // 如果要使用鲁棒核函数
                if (bRobust)
                {
                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    // 这里的重投影误差，自由度为2，所以这里设置为卡方分布中自由度为2的阈值，如果重投影的误差大约大于1个像素的时候，就认为不太靠谱的点了，
                    // 核函数是为了避免其误差的平方项出现数值上过大的增长
                    rk->setDelta(thHuber2D);
                }

                // 设置相机内参
                e->pCamera = pKF->mpCamera;

                optimizer.addEdge(e);

                vpEdgesMono.push_back(e);
                vpEdgeKFMono.push_back(pKF);
                vpMapPointEdgeMono.push_back(pMP);
            }
            else if (leftIndex != -1 && pKF->mvuRight[leftIndex] >= 0) // Stereo observation
            {
                // 双目或RGBD相机按照下面操作
                // 双目相机的观测数据则是由三个部分组成：投影点的x坐标，投影点的y坐标，以及投影点在右目中的x坐标（默认y方向上已经对齐了）

                const cv::KeyPoint &kpUn = pKF->mvKeysUn[leftIndex];

                Eigen::Matrix<double, 3, 1> obs;
                const float kp_ur = pKF->mvuRight[get<0>(mit->second)];
                obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                // 对于双目输入，g2o也有专门的误差边
                g2o::EdgeStereoSE3ProjectXYZ *e = new g2o::EdgeStereoSE3ProjectXYZ();
                // 填充
                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mnId)));
                e->setMeasurement(obs);
                // 信息矩阵这里是相同的，考虑的是左目特征点的所在图层
                const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave];
                Eigen::Matrix3d Info = Eigen::Matrix3d::Identity() * invSigma2;
                e->setInformation(Info);

                // 如果要使用鲁棒核函数
                if (bRobust)
                {
                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    // 由于现在的观测有三个值，重投影误差会有三个平方项的和组成，因此对应的卡方分布的自由度为3，所以这里设置的也是自由度为3的时候的阈值
                    rk->setDelta(thHuber3D);
                }

                // 填充相机的基本参数
                e->fx = pKF->fx;
                e->fy = pKF->fy;
                e->cx = pKF->cx;
                e->cy = pKF->cy;
                e->bf = pKF->mbf;

                optimizer.addEdge(e);

                vpEdgesStereo.push_back(e);
                vpEdgeKFStereo.push_back(pKF);
                vpMapPointEdgeStereo.push_back(pMP);
            }

            if (pKF->mpCamera2)
            {
                int rightIndex = get<1>(mit->second);

                if (rightIndex != -1 && rightIndex < pKF->mvKeysRight.size())
                {
                    rightIndex -= pKF->NLeft;

                    Eigen::Matrix<double, 2, 1> obs;
                    cv::KeyPoint kp = pKF->mvKeysRight[rightIndex];
                    obs << kp.pt.x, kp.pt.y;

                    ORB_SLAM3::EdgeSE3ProjectXYZToBody *e = new ORB_SLAM3::EdgeSE3ProjectXYZToBody();

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mnId)));
                    e->setMeasurement(obs);
                    const float &invSigma2 = pKF->mvInvLevelSigma2[kp.octave];
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuber2D);

                    Sophus::SE3f Trl = pKF->GetRelativePoseTrl();
                    e->mTrl = g2o::SE3Quat(Trl.unit_quaternion().cast<double>(), Trl.translation().cast<double>());

                    e->pCamera = pKF->mpCamera2;

                    optimizer.addEdge(e);
                    vpEdgesBody.push_back(e);
                    vpEdgeKFBody.push_back(pKF);
                    vpMapPointEdgeBody.push_back(pMP);
                }
            }
        }

        // 如果因为一些特殊原因,实际上并没有任何关键帧观测到当前的这个地图点,那么就删除掉这个顶点,并且这个地图点也就不参与优化
        if (nEdges == 0)
        {
            optimizer.removeVertex(vPoint);
            vbNotIncludedMP[i] = true;
        }
        else
        {
            vbNotIncludedMP[i] = false;
        }
    }

    // Optimize!
    // Step 4：开始优化
    optimizer.setVerbose(false);
    optimizer.initializeOptimization();
    optimizer.optimize(nIterations);
    Verbose::PrintMess("BA: End of the optimization", Verbose::VERBOSITY_NORMAL);

    // Recover optimized data
    // Step 5：得到优化的结果

    // Step 5.1 Keyframes
    // 遍历所有的关键帧
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKF = vpKFs[i];
        if (pKF->isBad())
            continue;
        // 获取到优化结果
        g2o::VertexSE3Expmap *vSE3 = static_cast<g2o::VertexSE3Expmap *>(optimizer.vertex(pKF->mnId));

        g2o::SE3Quat SE3quat = vSE3->estimate();


        if (nLoopKF == pMap->GetOriginKF()->mnId)
        {
            // 原则上来讲不会出现"当前闭环关键帧是第0帧"的情况,如果这种情况出现,只能够说明是在创建初始地图点的时候调用的这个全局BA函数.
            // 这个时候,地图中就只有两个关键帧,其中优化后的位姿数据可以直接写入到帧的成员变量中
            // 老白：视觉初始化时
            pKF->SetPose(Sophus::SE3f(SE3quat.rotation().cast<float>(), SE3quat.translation().cast<float>()));
        }
        else
        {
            // 正常的操作,先把优化后的位姿写入到帧的一个专门的成员变量mTcwGBA中备用
            pKF->mTcwGBA = Sophus::SE3d(SE3quat.rotation(), SE3quat.translation()).cast<float>();
            pKF->mnBAGlobalForKF = nLoopKF; // 标记这个关键帧参与了这次全局优化

            // 下面都是一些调试操作，计算优化前后的位移
            Sophus::SE3f mTwc = pKF->GetPoseInverse();
            Sophus::SE3f mTcGBA_c = pKF->mTcwGBA * mTwc;
            Eigen::Vector3f vector_dist = mTcGBA_c.translation();
            double dist = vector_dist.norm();
            if (dist > 1)
            {
                int numMonoBadPoints = 0, numMonoOptPoints = 0;
                int numStereoBadPoints = 0, numStereoOptPoints = 0;
                vector<MapPoint *> vpMonoMPsOpt, vpStereoMPsOpt;

                for (size_t i2 = 0, iend = vpEdgesMono.size(); i2 < iend; i2++)
                {
                    ORB_SLAM3::EdgeSE3ProjectXYZ *e = vpEdgesMono[i2];
                    MapPoint *pMP = vpMapPointEdgeMono[i2];
                    KeyFrame *pKFedge = vpEdgeKFMono[i2];

                    if (pKF != pKFedge)
                    {
                        continue;
                    }

                    if (pMP->isBad())
                        continue;

                    if (e->chi2() > 5.991 || !e->isDepthPositive())
                    {
                        numMonoBadPoints++;
                    }
                    else
                    {
                        numMonoOptPoints++;
                        vpMonoMPsOpt.push_back(pMP);
                    }
                }

                for (size_t i2 = 0, iend = vpEdgesStereo.size(); i2 < iend; i2++)
                {
                    g2o::EdgeStereoSE3ProjectXYZ *e = vpEdgesStereo[i2];
                    MapPoint *pMP = vpMapPointEdgeStereo[i2];
                    KeyFrame *pKFedge = vpEdgeKFMono[i2];

                    if (pKF != pKFedge)
                    {
                        continue;
                    }

                    if (pMP->isBad())
                        continue;

                    if (e->chi2() > 7.815 || !e->isDepthPositive())
                    {
                        numStereoBadPoints++;
                    }
                    else
                    {
                        numStereoOptPoints++;
                        vpStereoMPsOpt.push_back(pMP);
                    }
                }
            }
        }
    }

    // Step 5.2 遍历所有地图点,去除其中没有参与优化过程的地图点
    for (size_t i = 0; i < vpMP.size(); i++)
    {
        if (vbNotIncludedMP[i])
            continue;

        MapPoint *pMP = vpMP[i];

        if (pMP->isBad())
            continue;
        // 获取优化之后的地图点的位置
        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMP->mnId + maxKFid + 1));

        if (nLoopKF == pMap->GetOriginKF()->mnId)
        {
            // 如果这个GBA是在创建初始地图的时候调用的话,那么地图点的位姿也可以直接写入
            pMP->SetWorldPos(vPoint->estimate().cast<float>());
            pMP->UpdateNormalAndDepth();
        }
        else
        {
            // 反之,如果是正常的闭环过程调用,就先临时保存一下
            pMP->mPosGBA = vPoint->estimate().cast<float>();
            pMP->mnBAGlobalForKF = nLoopKF;
        }
    }
}

/**
 * @brief imu初始化优化，LocalMapping::InitializeIMU中使用 LoopClosing::RunGlobalBundleAdjustment
 * 地图全部做BA。也就是imu版的GlobalBundleAdjustemnt
 * @param pMap 地图
 * @param its 迭代次数
 * @param bFixLocal 是否固定局部，为false
 * @param nLoopId 回环id
 * @param pbStopFlag 是否停止的标志
 * @param bInit 提供priorG时为true，此时偏置只优化最后一帧的至0，然后所有关键帧的偏置都赋值为优化后的值
 *              若为false，则建立每两帧之间的偏置边，优化使其相差为0
 * @param priorG 陀螺仪偏置的信息矩阵系数，主动设置时一般bInit为true，也就是只优化最后一帧的偏置，这个数会作为计算信息矩阵时使用
 * @param priorA 加速度计偏置的信息矩阵系数
 * @param vSingVal 没用，估计调试用的
 * @param bHess 没用，估计调试用的
 */
void Optimizer::FullInertialBA(
    Map *pMap, int its, const bool bFixLocal, const long unsigned int nLoopId, bool *pbStopFlag,
    bool bInit, float priorG, float priorA, Eigen::VectorXd *vSingVal, bool *bHess)
{
    // 获取地图里所有mp与kf，以及最大kf的id
    long unsigned int maxKFid = pMap->GetMaxKFid();
    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();
    const vector<MapPoint *> vpMPs = pMap->GetAllMapPoints();

    // Setup optimizer
    // 1. 很经典的一套设置优化器流程
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    solver->setUserLambdaInit(1e-5);
    optimizer.setAlgorithm(solver);
    optimizer.setVerbose(false);

    if (pbStopFlag)
        optimizer.setForceStopFlag(pbStopFlag);

    int nNonFixed = 0;

    // 2. 设置关键帧与偏置节点
    // Set KeyFrame vertices
    KeyFrame *pIncKF;  // vpKFs中最后一个id符合要求的关键帧
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        pIncKF = pKFi;
        bool bFixed = false;
        if (bFixLocal)
        {
            bFixed = (pKFi->mnBALocalForKF >= (maxKFid - 1)) || (pKFi->mnBAFixedForKF >= (maxKFid - 1));
            if (!bFixed)
                nNonFixed++;
            VP->setFixed(bFixed);  // 固定，这里可能跟回环有关
        }
        optimizer.addVertex(VP);
        // 如果是初始化的那几个及后面的关键帧，加入速度节点
        if (pKFi->bImu)
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(bFixed);
            optimizer.addVertex(VV);
            // priorA==0.f 时 bInit为false，也就是又加入了偏置节点
            if (!bInit)
            {
                VertexGyroBias *VG = new VertexGyroBias(pKFi);
                VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
                VG->setFixed(bFixed);
                optimizer.addVertex(VG);
                VertexAccBias *VA = new VertexAccBias(pKFi);
                VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
                VA->setFixed(bFixed);
                optimizer.addVertex(VA);
            }
        }
    }
    // priorA!=0.f 时 bInit为true，加入了偏置节点
    if (bInit)
    {
        VertexGyroBias *VG = new VertexGyroBias(pIncKF);
        VG->setId(4 * maxKFid + 2);
        VG->setFixed(false);
        optimizer.addVertex(VG);
        VertexAccBias *VA = new VertexAccBias(pIncKF);
        VA->setId(4 * maxKFid + 3);
        VA->setFixed(false);
        optimizer.addVertex(VA);
    }
    // false
    if (bFixLocal)
    {
        if (nNonFixed < 3)
            return;
    }

    // 3. 添加关于imu的边
    // IMU links
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        // 必须有对应的上一个关键帧，感觉跟下面的判定冲突了
        if (!pKFi->mPrevKF)
        {
            Verbose::PrintMess("NOT INERTIAL LINK TO PREVIOUS FRAME!", Verbose::VERBOSITY_NORMAL);
            continue;
        }

        if (pKFi->mPrevKF && pKFi->mnId <= maxKFid)
        {
            if (pKFi->isBad() || pKFi->mPrevKF->mnId > maxKFid)
                continue;
            // 这两个都必须为初始化后的关键帧
            if (pKFi->bImu && pKFi->mPrevKF->bImu)
            {
                // 3.1 根据上一帧的偏置设定当前帧的新偏置
                pKFi->mpImuPreintegrated->SetNewBias(pKFi->mPrevKF->GetImuBias());
                // 3.2 提取节点
                g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
                g2o::HyperGraph::Vertex *VV1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 1);

                g2o::HyperGraph::Vertex *VG1;
                g2o::HyperGraph::Vertex *VA1;
                g2o::HyperGraph::Vertex *VG2;
                g2o::HyperGraph::Vertex *VA2;
                // 根据不同输入配置相应的偏置节点
                if (!bInit)
                {
                    VG1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 2);
                    VA1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 3);
                    VG2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2);
                    VA2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3);
                }
                else
                {
                    VG1 = optimizer.vertex(4 * maxKFid + 2);
                    VA1 = optimizer.vertex(4 * maxKFid + 3);
                }

                g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
                g2o::HyperGraph::Vertex *VV2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1);

                if (!bInit)
                {
                    if (!VP1 || !VV1 || !VG1 || !VA1 || !VP2 || !VV2 || !VG2 || !VA2)
                    {
                        cout << "Error" << VP1 << ", " << VV1 << ", " << VG1 << ", " << VA1 << ", " << VP2 << ", " << VV2 << ", " << VG2 << ", " << VA2 << endl;
                        continue;
                    }
                }
                else
                {
                    if (!VP1 || !VV1 || !VG1 || !VA1 || !VP2 || !VV2)
                    {
                        cout << "Error" << VP1 << ", " << VV1 << ", " << VG1 << ", " << VA1 << ", " << VP2 << ", " << VV2 << endl;
                        continue;
                    }
                }
                // 3.3 设置边
                EdgeInertial *ei = new EdgeInertial(pKFi->mpImuPreintegrated);
                ei->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
                ei->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
                ei->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG1));
                ei->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA1));
                ei->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
                ei->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));

                g2o::RobustKernelHuber *rki = new g2o::RobustKernelHuber;
                ei->setRobustKernel(rki);
                // 9个自由度的卡方检验（0.05）
                rki->setDelta(sqrt(16.92));

                optimizer.addEdge(ei);
                // 加了每一个关键帧的偏置时，还要优化两帧之间偏置的误差
                if (!bInit)
                {
                    EdgeGyroRW *egr = new EdgeGyroRW();
                    egr->setVertex(0, VG1);
                    egr->setVertex(1, VG2);
                    Eigen::Matrix3d InfoG = pKFi->mpImuPreintegrated->C.block<3, 3>(9, 9).cast<double>().inverse();
                    egr->setInformation(InfoG);
                    egr->computeError();
                    optimizer.addEdge(egr);

                    EdgeAccRW *ear = new EdgeAccRW();
                    ear->setVertex(0, VA1);
                    ear->setVertex(1, VA2);
                    Eigen::Matrix3d InfoA = pKFi->mpImuPreintegrated->C.block<3, 3>(12, 12).cast<double>().inverse();
                    ear->setInformation(InfoA);
                    ear->computeError();
                    optimizer.addEdge(ear);
                }
            }
            else
                cout << pKFi->mnId << " or " << pKFi->mPrevKF->mnId << " no imu" << endl;
        }
    }

    // 只加入pIncKF帧的偏置，优化偏置到0
    if (bInit)
    {
        g2o::HyperGraph::Vertex *VG = optimizer.vertex(4 * maxKFid + 2);
        g2o::HyperGraph::Vertex *VA = optimizer.vertex(4 * maxKFid + 3);

        // Add prior to comon biases
        Eigen::Vector3f bprior;
        bprior.setZero();

        EdgePriorAcc *epa = new EdgePriorAcc(bprior);
        epa->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
        double infoPriorA = priorA; //
        epa->setInformation(infoPriorA * Eigen::Matrix3d::Identity());
        optimizer.addEdge(epa);

        EdgePriorGyro *epg = new EdgePriorGyro(bprior);
        epg->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
        double infoPriorG = priorG; //
        epg->setInformation(infoPriorG * Eigen::Matrix3d::Identity());
        optimizer.addEdge(epg);
    }

    const float thHuberMono = sqrt(5.991);
    const float thHuberStereo = sqrt(7.815);

    const unsigned long iniMPid = maxKFid * 5;

    vector<bool> vbNotIncludedMP(vpMPs.size(), false);
    // 5. 添加关于mp的节点与边，这段比较好理解，很传统的视觉上的重投影误差
    for (size_t i = 0; i < vpMPs.size(); i++)
    {
        MapPoint *pMP = vpMPs[i];
        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMP->GetWorldPos().cast<double>());
        unsigned long id = pMP->mnId + iniMPid + 1;
        vPoint->setId(id);
        vPoint->setMarginalized(true);
        optimizer.addVertex(vPoint);

        const map<KeyFrame *, tuple<int, int>> observations = pMP->GetObservations();

        bool bAllFixed = true;

        // Set edges
        //  遍历所有能观测到这个点的关键帧
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (pKFi->mnId > maxKFid)
                continue;

            if (!pKFi->isBad())
            {
                const int leftIndex = get<0>(mit->second);
                cv::KeyPoint kpUn;
                // 添加边
                if (leftIndex != -1 && pKFi->mvuRight[get<0>(mit->second)] < 0) // Monocular observation
                {
                    kpUn = pKFi->mvKeysUn[leftIndex];
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    EdgeMono *e = new EdgeMono(0);

                    g2o::OptimizableGraph::Vertex *VP = dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId));
                    if (bAllFixed)
                        if (!VP->fixed())
                            bAllFixed = false;

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, VP);
                    e->setMeasurement(obs);
                    const float invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];

                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);

                    optimizer.addEdge(e);
                }
                else if (leftIndex != -1 && pKFi->mvuRight[leftIndex] >= 0) // stereo observation
                {
                    kpUn = pKFi->mvKeysUn[leftIndex];
                    const float kp_ur = pKFi->mvuRight[leftIndex];
                    Eigen::Matrix<double, 3, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    EdgeStereo *e = new EdgeStereo(0);

                    g2o::OptimizableGraph::Vertex *VP = dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId));
                    if (bAllFixed)
                        if (!VP->fixed())
                            bAllFixed = false;

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, VP);
                    e->setMeasurement(obs);
                    const float invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];

                    e->setInformation(Eigen::Matrix3d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    optimizer.addEdge(e);
                }

                if (pKFi->mpCamera2)
                { // Monocular right observation
                    int rightIndex = get<1>(mit->second);

                    if (rightIndex != -1 && rightIndex < pKFi->mvKeysRight.size())
                    {
                        rightIndex -= pKFi->NLeft;

                        Eigen::Matrix<double, 2, 1> obs;
                        kpUn = pKFi->mvKeysRight[rightIndex];
                        obs << kpUn.pt.x, kpUn.pt.y;

                        EdgeMono *e = new EdgeMono(1);

                        g2o::OptimizableGraph::Vertex *VP = dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId));
                        if (bAllFixed)
                            if (!VP->fixed())
                                bAllFixed = false;

                        e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                        e->setVertex(1, VP);
                        e->setMeasurement(obs);
                        const float invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];
                        e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                        g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                        e->setRobustKernel(rk);
                        rk->setDelta(thHuberMono);

                        optimizer.addEdge(e);
                    }
                }
            }
        }

        // false
        if (bAllFixed)
        {
            optimizer.removeVertex(vPoint);
            vbNotIncludedMP[i] = true;
        }
    }

    if (pbStopFlag)
        if (*pbStopFlag)
            return;

    optimizer.initializeOptimization();
    optimizer.optimize(its);

    // 5. 取出优化结果，对应的值赋值
    // Recover optimized data
    // Keyframes
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;
        VertexPose *VP = static_cast<VertexPose *>(optimizer.vertex(pKFi->mnId));
        if (nLoopId == 0)
        {
            Sophus::SE3f Tcw(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
            pKFi->SetPose(Tcw);
        }
        else
        {
            pKFi->mTcwGBA = Sophus::SE3f(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
            pKFi->mnBAGlobalForKF = nLoopId;
        }
        if (pKFi->bImu)
        {
            VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1));
            if (nLoopId == 0)
            {
                pKFi->SetVelocity(VV->estimate().cast<float>());
            }
            else
            {
                pKFi->mVwbGBA = VV->estimate().cast<float>();
            }

            VertexGyroBias *VG;
            VertexAccBias *VA;
            if (!bInit)
            {
                VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2));
                VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3));
            }
            else
            {
                VG = static_cast<VertexGyroBias *>(optimizer.vertex(4 * maxKFid + 2));
                VA = static_cast<VertexAccBias *>(optimizer.vertex(4 * maxKFid + 3));
            }

            Vector6d vb;
            vb << VG->estimate(), VA->estimate();
            IMU::Bias b(vb[3], vb[4], vb[5], vb[0], vb[1], vb[2]);
            if (nLoopId == 0)
            {
                pKFi->SetNewBias(b);
            }
            else
            {
                pKFi->mBiasGBA = b;
            }
        }
    }

    // Points
    for (size_t i = 0; i < vpMPs.size(); i++)
    {
        if (vbNotIncludedMP[i])
            continue;

        MapPoint *pMP = vpMPs[i];
        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMP->mnId + iniMPid + 1));

        if (nLoopId == 0)
        {
            pMP->SetWorldPos(vPoint->estimate().cast<float>());
            pMP->UpdateNormalAndDepth();
        }
        else
        {
            pMP->mPosGBA = vPoint->estimate().cast<float>();
            pMP->mnBAGlobalForKF = nLoopId;
        }
    }

    pMap->IncreaseChangeIndex();
}

/**************************************以下为尺度与重力优化**************************************************************/

/**
 * @brief imu初始化优化，LocalMapping::InitializeIMU中使用，其中kf的位姿是固定不变的
 * 优化目标 重力方向，尺度，速度与偏置
 * @param pMap 地图
 * @param Rwg 重力方向到速度方向的转角
 * @param scale 尺度（输出cout用）
 * @param bg 陀螺仪偏置（输出cout用）
 * @param ba 加速度计偏置（输出cout用）
 * @param bMono 是否为单目
 * @param covInertial 惯导协方差矩阵(暂时没用，9*9的0矩阵)
 * @param bFixedVel 是否固定速度不优化，false
 * @param bGauss  没用，false
 * @param priorG 陀螺仪偏置的信息矩阵系数
 * @param priorA 加速度计偏置的信息矩阵系数
 */
void Optimizer::InertialOptimization(
    Map *pMap, Eigen::Matrix3d &Rwg, double &scale, Eigen::Vector3d &bg, Eigen::Vector3d &ba, bool bMono,
    Eigen::MatrixXd &covInertial, bool bFixedVel, bool bGauss, float priorG, float priorA)
{
    Verbose::PrintMess("inertial optimization", Verbose::VERBOSITY_NORMAL);
    int its = 200;
    long unsigned int maxKFid = pMap->GetMaxKFid();
    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();  // 获取所有关键帧

    // Setup optimizer
    // 1. 构建优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

    if (priorG != 0.f)
        solver->setUserLambdaInit(1e3);

    optimizer.setAlgorithm(solver);

    // Set KeyFrame vertices (fixed poses and optimizable velocities)
    // 2. 确定关键帧节点（锁住的位姿及可优化的速度）
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        // 跳过id大于当前地图最大id的关键帧
        if (pKFi->mnId > maxKFid)
            continue;
        VertexPose *VP = new VertexPose(pKFi);  // 继承于public g2o::BaseVertex<6, ImuCamPose>
        VP->setId(pKFi->mnId);
        VP->setFixed(true);
        optimizer.addVertex(VP);

        VertexVelocity *VV = new VertexVelocity(pKFi);  // 继承于public g2o::BaseVertex<3, Eigen::Vector3d>
        VV->setId(maxKFid + (pKFi->mnId) + 1);
        if (bFixedVel)
            VV->setFixed(true);
        else
            VV->setFixed(false);

        optimizer.addVertex(VV);
    }

    // Biases
    // 3. 确定偏置节点，陀螺仪与加速度计
    VertexGyroBias *VG = new VertexGyroBias(vpKFs.front());
    VG->setId(maxKFid * 2 + 2);
    if (bFixedVel)
        VG->setFixed(true);
    else
        VG->setFixed(false);
    optimizer.addVertex(VG);
    VertexAccBias *VA = new VertexAccBias(vpKFs.front());
    VA->setId(maxKFid * 2 + 3);
    if (bFixedVel)
        VA->setFixed(true);
    else
        VA->setFixed(false);

    optimizer.addVertex(VA);

    // prior acc bias
    Eigen::Vector3f bprior;
    bprior.setZero();

    // 4. 添加关于加速度计与陀螺仪偏置的边，这两个边加入是保证第一帧的偏置为0
    EdgePriorAcc *epa = new EdgePriorAcc(bprior);
    epa->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
    double infoPriorA = priorA;
    epa->setInformation(infoPriorA * Eigen::Matrix3d::Identity());
    optimizer.addEdge(epa);
    EdgePriorGyro *epg = new EdgePriorGyro(bprior);
    epg->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
    double infoPriorG = priorG;
    epg->setInformation(infoPriorG * Eigen::Matrix3d::Identity());
    optimizer.addEdge(epg);

    // Gravity and scale
    // 5. 添加关于重力方向与尺度的节点
    VertexGDir *VGDir = new VertexGDir(Rwg);
    VGDir->setId(maxKFid * 2 + 4);
    VGDir->setFixed(false);
    optimizer.addVertex(VGDir);
    VertexScale *VS = new VertexScale(scale);
    VS->setId(maxKFid * 2 + 5);
    VS->setFixed(!bMono); // Fixed for stereo case
    optimizer.addVertex(VS);

    // Graph edges
    // IMU links with gravity and scale
    // 6. imu信息链接重力方向与尺度信息
    vector<EdgeInertialGS *> vpei;  // 后面虽然加入了边，但是没有用到，应该调试用的
    vpei.reserve(vpKFs.size());
    vector<pair<KeyFrame *, KeyFrame *>> vppUsedKF;
    vppUsedKF.reserve(vpKFs.size());  // 后面虽然加入了关键帧，但是没有用到，应该调试用的
    // std::cout << "build optimization graph" << std::endl;

    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];

        if (pKFi->mPrevKF && pKFi->mnId <= maxKFid)
        {
            if (pKFi->isBad() || pKFi->mPrevKF->mnId > maxKFid)
                continue;
            if (!pKFi->mpImuPreintegrated)
                std::cout << "Not preintegrated measurement" << std::endl;
            // 到这里的条件是pKFi是好的，并且它有上一个关键帧，且他们的id要小于最大id
            // 6.1 检查节点指针是否为空
            // 将pKFi偏置设定为上一关键帧的偏置
            pKFi->mpImuPreintegrated->SetNewBias(pKFi->mPrevKF->GetImuBias());
            g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VV1 = optimizer.vertex(maxKFid + (pKFi->mPrevKF->mnId) + 1);
            g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
            g2o::HyperGraph::Vertex *VV2 = optimizer.vertex(maxKFid + (pKFi->mnId) + 1);
            g2o::HyperGraph::Vertex *VG = optimizer.vertex(maxKFid * 2 + 2);
            g2o::HyperGraph::Vertex *VA = optimizer.vertex(maxKFid * 2 + 3);
            g2o::HyperGraph::Vertex *VGDir = optimizer.vertex(maxKFid * 2 + 4);
            g2o::HyperGraph::Vertex *VS = optimizer.vertex(maxKFid * 2 + 5);
            if (!VP1 || !VV1 || !VG || !VA || !VP2 || !VV2 || !VGDir || !VS)
            {
                cout << "Error" << VP1 << ", " << VV1 << ", " << VG << ", " << VA << ", " << VP2 << ", " << VV2 << ", " << VGDir << ", " << VS << endl;

                continue;
            }
            // 6.2 这是一个大边。。。。包含了上面所有信息，注意到前面的两个偏置也做了两个一元边加入
            EdgeInertialGS *ei = new EdgeInertialGS(pKFi->mpImuPreintegrated);
            ei->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
            ei->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
            ei->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
            ei->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
            ei->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
            ei->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));
            ei->setVertex(6, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VGDir));
            ei->setVertex(7, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VS));

            vpei.push_back(ei);

            vppUsedKF.push_back(make_pair(pKFi->mPrevKF, pKFi));
            optimizer.addEdge(ei);
        }
    }

    // Compute error for different scales
    std::set<g2o::HyperGraph::Edge *> setEdges = optimizer.edges();

    optimizer.setVerbose(false);
    optimizer.initializeOptimization();
    optimizer.optimize(its);

    // 7. 取数
    scale = VS->estimate();

    // Recover optimized data
    // Biases
    VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid * 2 + 2));
    VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid * 2 + 3));
    Vector6d vb;
    vb << VG->estimate(), VA->estimate();
    bg << VG->estimate();
    ba << VA->estimate();
    scale = VS->estimate();

    IMU::Bias b(vb[3], vb[4], vb[5], vb[0], vb[1], vb[2]);
    Rwg = VGDir->estimate().Rwg;

    // Keyframes velocities and biases
    const int N = vpKFs.size();
    for (size_t i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;

        VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + (pKFi->mnId) + 1));
        Eigen::Vector3d Vw = VV->estimate(); // Velocity is scaled after
        pKFi->SetVelocity(Vw.cast<float>());

        if ((pKFi->GetGyroBias() - bg.cast<float>()).norm() > 0.01)
        {
            pKFi->SetNewBias(b);
            if (pKFi->mpImuPreintegrated)
                pKFi->mpImuPreintegrated->Reintegrate();
        }
        else
            pKFi->SetNewBias(b);
    }
}

/**
 * @brief LoopClosing::MergeLocal2 中使用
 * 跟参数最多的那个同名函数不同的地方在于很多节点不可选是否固定，优化的目标有：
 * 速度，偏置
 * @param pMap 地图
 * @param bg 陀螺仪偏置
 * @param ba 加速度计偏置
 * @param priorG 陀螺仪偏置的信息矩阵系数
 * @param priorA 加速度计偏置的信息矩阵系数
 */
void Optimizer::InertialOptimization(Map *pMap, Eigen::Vector3d &bg, Eigen::Vector3d &ba, float priorG, float priorA)
{
    int its = 200; // Check number of iterations
    long unsigned int maxKFid = pMap->GetMaxKFid();
    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();

    // Setup optimizer
    // 1. 构建优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    solver->setUserLambdaInit(1e3);

    optimizer.setAlgorithm(solver);

    // Set KeyFrame vertices (fixed poses and optimizable velocities)
    // 2. 构建节点，固定rt
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(true);
        optimizer.addVertex(VP);

        VertexVelocity *VV = new VertexVelocity(pKFi);
        VV->setId(maxKFid + (pKFi->mnId) + 1);
        VV->setFixed(false);

        optimizer.addVertex(VV);
    }

    // Biases
    // 3. 第一个关键帧的偏置
    VertexGyroBias *VG = new VertexGyroBias(vpKFs.front());
    VG->setId(maxKFid * 2 + 2);
    VG->setFixed(false);
    optimizer.addVertex(VG);

    VertexAccBias *VA = new VertexAccBias(vpKFs.front());
    VA->setId(maxKFid * 2 + 3);
    VA->setFixed(false);

    optimizer.addVertex(VA);
    // prior acc bias
    Eigen::Vector3f bprior;
    bprior.setZero();

    // 4. 先验边，让偏置趋向于0
    EdgePriorAcc *epa = new EdgePriorAcc(bprior);
    epa->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
    double infoPriorA = priorA;
    epa->setInformation(infoPriorA * Eigen::Matrix3d::Identity());
    optimizer.addEdge(epa);
    EdgePriorGyro *epg = new EdgePriorGyro(bprior);
    epg->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
    double infoPriorG = priorG;
    epg->setInformation(infoPriorG * Eigen::Matrix3d::Identity());
    optimizer.addEdge(epg);

    // Gravity and scale
    // 5. 注意这里固定了尺度与重力方向，所以只优化了偏置与速度
    VertexGDir *VGDir = new VertexGDir(Eigen::Matrix3d::Identity());
    VGDir->setId(maxKFid * 2 + 4);
    VGDir->setFixed(true);
    optimizer.addVertex(VGDir);
    VertexScale *VS = new VertexScale(1.0);
    VS->setId(maxKFid * 2 + 5);
    VS->setFixed(true); // Fixed since scale is obtained from already well initialized map
    optimizer.addVertex(VS);

    // Graph edges
    // IMU links with gravity and scale
    vector<EdgeInertialGS *> vpei;
    vpei.reserve(vpKFs.size());
    vector<pair<KeyFrame *, KeyFrame *>> vppUsedKF;
    vppUsedKF.reserve(vpKFs.size());

    // 6. 设置残差边
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];

        if (pKFi->mPrevKF && pKFi->mnId <= maxKFid)
        {
            if (pKFi->isBad() || pKFi->mPrevKF->mnId > maxKFid)
                continue;

            pKFi->mpImuPreintegrated->SetNewBias(pKFi->mPrevKF->GetImuBias());
            g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VV1 = optimizer.vertex(maxKFid + (pKFi->mPrevKF->mnId) + 1);
            g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
            g2o::HyperGraph::Vertex *VV2 = optimizer.vertex(maxKFid + (pKFi->mnId) + 1);
            g2o::HyperGraph::Vertex *VG = optimizer.vertex(maxKFid * 2 + 2);
            g2o::HyperGraph::Vertex *VA = optimizer.vertex(maxKFid * 2 + 3);
            g2o::HyperGraph::Vertex *VGDir = optimizer.vertex(maxKFid * 2 + 4);
            g2o::HyperGraph::Vertex *VS = optimizer.vertex(maxKFid * 2 + 5);
            if (!VP1 || !VV1 || !VG || !VA || !VP2 || !VV2 || !VGDir || !VS)
            {
                cout << "Error" << VP1 << ", " << VV1 << ", " << VG << ", " << VA << ", " << VP2 << ", " << VV2 << ", " << VGDir << ", " << VS << endl;

                continue;
            }
            EdgeInertialGS *ei = new EdgeInertialGS(pKFi->mpImuPreintegrated);
            ei->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
            ei->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
            ei->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
            ei->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
            ei->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
            ei->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));
            ei->setVertex(6, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VGDir));
            ei->setVertex(7, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VS));

            vpei.push_back(ei);

            vppUsedKF.push_back(make_pair(pKFi->mPrevKF, pKFi));
            optimizer.addEdge(ei);
        }
    }

    // 7. 优化
    // Compute error for different scales
    optimizer.setVerbose(false);
    optimizer.initializeOptimization();
    optimizer.optimize(its);

    // 8. 取数
    // Recover optimized data
    // Biases
    VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid * 2 + 2));
    VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid * 2 + 3));
    Vector6d vb;
    vb << VG->estimate(), VA->estimate();
    bg << VG->estimate();
    ba << VA->estimate();

    IMU::Bias b(vb[3], vb[4], vb[5], vb[0], vb[1], vb[2]);

    // Keyframes velocities and biases
    const int N = vpKFs.size();
    for (size_t i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;

        VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + (pKFi->mnId) + 1));
        Eigen::Vector3d Vw = VV->estimate();
        pKFi->SetVelocity(Vw.cast<float>());

        if ((pKFi->GetGyroBias() - bg.cast<float>()).norm() > 0.01)
        {
            pKFi->SetNewBias(b);
            if (pKFi->mpImuPreintegrated)
                pKFi->mpImuPreintegrated->Reintegrate();
        }
        else
            pKFi->SetNewBias(b);
    }
}

/**
 * @brief 优化重力方向与尺度，LocalMapping::ScaleRefinement()中使用，优化目标有：
 * 重力方向与尺度
 * @param pMap 地图
 * @param Rwg 重力方向到速度方向的转角
 * @param scale 尺度
 */
void Optimizer::InertialOptimization(Map *pMap, Eigen::Matrix3d &Rwg, double &scale)
{
    int its = 10;
    long unsigned int maxKFid = pMap->GetMaxKFid();
    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();

    // 1. 构建优化器
    // Setup optimizer
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmGaussNewton *solver = new g2o::OptimizationAlgorithmGaussNewton(solver_ptr);
    optimizer.setAlgorithm(solver);

    // Set KeyFrame vertices (all variables are fixed)
    // 2. 添加帧节点，其中包括位姿，速度，两个偏置
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];
        if (pKFi->mnId > maxKFid)
            continue;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(true);
        optimizer.addVertex(VP);

        VertexVelocity *VV = new VertexVelocity(pKFi);
        VV->setId(maxKFid + 1 + (pKFi->mnId));
        VV->setFixed(true);
        optimizer.addVertex(VV);

        // Vertex of fixed biases
        VertexGyroBias *VG = new VertexGyroBias(vpKFs.front());
        VG->setId(2 * (maxKFid + 1) + (pKFi->mnId));
        VG->setFixed(true);
        optimizer.addVertex(VG);
        VertexAccBias *VA = new VertexAccBias(vpKFs.front());
        VA->setId(3 * (maxKFid + 1) + (pKFi->mnId));
        VA->setFixed(true);
        optimizer.addVertex(VA);
    }

    // 3. 添加重力方向与尺度的节点，为优化对象
    // Gravity and scale
    VertexGDir *VGDir = new VertexGDir(Rwg);
    VGDir->setId(4 * (maxKFid + 1));
    VGDir->setFixed(false);
    optimizer.addVertex(VGDir);
    VertexScale *VS = new VertexScale(scale);
    VS->setId(4 * (maxKFid + 1) + 1);
    VS->setFixed(false);
    optimizer.addVertex(VS);

    // Graph edges
    // 4. 添加边
    int count_edges = 0;
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];

        if (pKFi->mPrevKF && pKFi->mnId <= maxKFid)
        {
            if (pKFi->isBad() || pKFi->mPrevKF->mnId > maxKFid)
                continue;

            g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VV1 = optimizer.vertex((maxKFid + 1) + pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
            g2o::HyperGraph::Vertex *VV2 = optimizer.vertex((maxKFid + 1) + pKFi->mnId);
            g2o::HyperGraph::Vertex *VG = optimizer.vertex(2 * (maxKFid + 1) + pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VA = optimizer.vertex(3 * (maxKFid + 1) + pKFi->mPrevKF->mnId);
            g2o::HyperGraph::Vertex *VGDir = optimizer.vertex(4 * (maxKFid + 1));
            g2o::HyperGraph::Vertex *VS = optimizer.vertex(4 * (maxKFid + 1) + 1);
            if (!VP1 || !VV1 || !VG || !VA || !VP2 || !VV2 || !VGDir || !VS)
            {
                Verbose::PrintMess("Error" + to_string(VP1->id()) + ", " + to_string(VV1->id()) + ", " + to_string(VG->id()) + ", " + to_string(VA->id()) + ", " + to_string(VP2->id()) + ", " + to_string(VV2->id()) + ", " + to_string(VGDir->id()) + ", " + to_string(VS->id()), Verbose::VERBOSITY_NORMAL);

                continue;
            }
            count_edges++;
            EdgeInertialGS *ei = new EdgeInertialGS(pKFi->mpImuPreintegrated);
            ei->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
            ei->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
            ei->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG));
            ei->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA));
            ei->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
            ei->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));
            ei->setVertex(6, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VGDir));
            ei->setVertex(7, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VS));
            g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
            ei->setRobustKernel(rk);
            rk->setDelta(1.f);
            optimizer.addEdge(ei);
        }
    }
    // 5. 优化
    // Compute error for different scales
    optimizer.setVerbose(false);
    optimizer.initializeOptimization();
    optimizer.computeActiveErrors();
    float err = optimizer.activeRobustChi2();
    optimizer.optimize(its);
    optimizer.computeActiveErrors();
    float err_end = optimizer.activeRobustChi2();
    // Recover optimized data
    // 6. 取数
    scale = VS->estimate();
    Rwg = VGDir->estimate().Rwg;
}

/**************************************以下为sim3优化**************************************************************/

/**
 * @brief LoopClosing::DetectAndReffineSim3FromLastKF使用
 * 1投2， 2投1这么来  只优化g2oS12
 * @param pKF1         当前关键帧
 * @param pKF2         候选关键帧
 * @param vpMatches1   当前关键帧与地图匹配上的MP，中间会有NULL，与当前关键帧的特征点一一对应
 * @param g2oS12       相似变换矩阵
 * @param th2          误差上限的平方
 * @param bFixScale    是否固定尺度，单目或者单目imu未做到3阶段初始化为false
 * @param mAcumHessian 计算累计海森矩阵（没啥用）
 * @param bAllPoints   是否计算所有点（都为true）
 */
int Optimizer::OptimizeSim3(
    KeyFrame *pKF1, KeyFrame *pKF2, vector<MapPoint *> &vpMatches1, g2o::Sim3 &g2oS12, const float th2,
    const bool bFixScale, Eigen::Matrix<double, 7, 7> &mAcumHessian, const bool bAllPoints)
{
    // 1. 初始化g2o优化器
    // 先构造求解器
    g2o::SparseOptimizer optimizer;
    // 构造线性方程求解器，Hx = -b的求解器
    g2o::BlockSolverX::LinearSolverType *linearSolver;
    // 使用dense的求解器，（常见非dense求解器有cholmod线性求解器和shur补线性求解器）
    linearSolver = new g2o::LinearSolverDense<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);
    // 使用L-M迭代
    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    optimizer.setAlgorithm(solver);

    // Camera poses
    // 2.1 添加Sim3顶点
    const Eigen::Matrix3f R1w = pKF1->GetRotation();
    const Eigen::Vector3f t1w = pKF1->GetTranslation();
    const Eigen::Matrix3f R2w = pKF2->GetRotation();
    const Eigen::Vector3f t2w = pKF2->GetTranslation();

    // Set Sim3 vertex
    ORB_SLAM3::VertexSim3Expmap *vSim3 = new ORB_SLAM3::VertexSim3Expmap();
    vSim3->_fix_scale = bFixScale;
    vSim3->setEstimate(g2oS12);
    vSim3->setId(0);
    vSim3->setFixed(false);
    vSim3->pCamera1 = pKF1->mpCamera;
    vSim3->pCamera2 = pKF2->mpCamera;
    optimizer.addVertex(vSim3);

    // Set MapPoint vertices
    // 2.1 添加MP顶点
    const int N = vpMatches1.size();
    const vector<MapPoint *> vpMapPoints1 = pKF1->GetMapPointMatches();
    vector<ORB_SLAM3::EdgeSim3ProjectXYZ *> vpEdges12;         // pKF2对应的MapPoints到pKF1的投影
    vector<ORB_SLAM3::EdgeInverseSim3ProjectXYZ *> vpEdges21;  // pKF1对应的MapPoints到pKF2的投影
    vector<size_t> vnIndexEdge;
    vector<bool> vbIsInKF2;

    vnIndexEdge.reserve(2 * N);
    vpEdges12.reserve(2 * N);
    vpEdges21.reserve(2 * N);
    vbIsInKF2.reserve(2 * N);

    const float deltaHuber = sqrt(th2);

    int nCorrespondences = 0;
    int nBadMPs = 0;         // 没有实际用处，没有输出信息
    int nInKF2 = 0;          // 输出信息用
    int nOutKF2 = 0;         // 输出信息用
    int nMatchWithoutMP = 0; // 输出信息用

    vector<int> vIdsOnlyInKF2;

    // 2.2 遍历当前关键帧的所有MP点
    for (int i = 0; i < N; i++)
    {
        if (!vpMatches1[i])
            continue;

        // pMP1和pMP2是匹配的MapPoints，pMP1表示当前帧正常对应的mp，pMP2表示对应的回环的mp
        MapPoint *pMP1 = vpMapPoints1[i];
        MapPoint *pMP2 = vpMatches1[i];

        // (1, 2) (3, 4) (5, 6)
        const int id1 = 2 * i + 1;
        const int id2 = 2 * (i + 1);

        // 返回这个点在pKF2关键帧中对应的特征点id
        const int i2 = get<0>(pMP2->GetIndexInKeyFrame(pKF2));

        Eigen::Vector3f P3D1c;  // 点1在当前关键帧相机坐标系下的坐标
        Eigen::Vector3f P3D2c;  // 点2在候选关键帧相机坐标系下的坐标

        if (pMP1 && pMP2)
        {
            if (!pMP1->isBad() && !pMP2->isBad())
            {
                // 2.3 添加PointXYZ顶点， 且设为了固定
                g2o::VertexSBAPointXYZ *vPoint1 = new g2o::VertexSBAPointXYZ();
                Eigen::Vector3f P3D1w = pMP1->GetWorldPos();
                P3D1c = R1w * P3D1w + t1w;
                vPoint1->setEstimate(P3D1c.cast<double>());  // 点1在当前关键帧下的三维点坐标
                vPoint1->setId(id1);
                vPoint1->setFixed(true);
                optimizer.addVertex(vPoint1);

                g2o::VertexSBAPointXYZ *vPoint2 = new g2o::VertexSBAPointXYZ();
                Eigen::Vector3f P3D2w = pMP2->GetWorldPos();
                P3D2c = R2w * P3D2w + t2w;
                vPoint2->setEstimate(P3D2c.cast<double>());  // 点2在候选关键帧下的三维点坐标
                vPoint2->setId(id2);
                vPoint2->setFixed(true);
                optimizer.addVertex(vPoint2);
            }
            else
            {
                nBadMPs++;
                continue;
            }
        }
        else
        {
            nMatchWithoutMP++;

            // The 3D position in KF1 doesn't exist
            if (!pMP2->isBad())
            {
                // 执行到这里意味着特征点没有对应的原始MP，却有回环MP，将其投到候选帧里面
                g2o::VertexSBAPointXYZ *vPoint2 = new g2o::VertexSBAPointXYZ();
                Eigen::Vector3f P3D2w = pMP2->GetWorldPos();
                P3D2c = R2w * P3D2w + t2w;
                vPoint2->setEstimate(P3D2c.cast<double>());
                vPoint2->setId(id2);
                vPoint2->setFixed(true);
                optimizer.addVertex(vPoint2);

                vIdsOnlyInKF2.push_back(id2);
            }
            continue;
        }

        if (i2 < 0 && !bAllPoints)
        {
            Verbose::PrintMess("    Remove point -> i2: " + to_string(i2) + "; bAllPoints: " + to_string(bAllPoints), Verbose::VERBOSITY_DEBUG);
            continue;
        }

        if (P3D2c(2) < 0)
        {
            Verbose::PrintMess("Sim3: Z coordinate is negative", Verbose::VERBOSITY_DEBUG);
            continue;
        }

        nCorrespondences++;

        // 2.4 添加两个顶点（3D点）到相机投影的边
        // Set edge x1 = S12*X2
        Eigen::Matrix<double, 2, 1> obs1;
        const cv::KeyPoint &kpUn1 = pKF1->mvKeysUn[i];
        obs1 << kpUn1.pt.x, kpUn1.pt.y;

        // 这个边的误差计算方式
        // 1. 将点2通过g2oS12计算到当前关键帧下
        // 2. 点2在当前关键帧下投影到图像上与观测求误差
        ORB_SLAM3::EdgeSim3ProjectXYZ *e12 = new ORB_SLAM3::EdgeSim3ProjectXYZ();

        e12->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id2)));  // 2相机坐标系下的三维点
        e12->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));   // g2oS12
        e12->setMeasurement(obs1);
        const float &invSigmaSquare1 = pKF1->mvInvLevelSigma2[kpUn1.octave];
        e12->setInformation(Eigen::Matrix2d::Identity() * invSigmaSquare1);

        g2o::RobustKernelHuber *rk1 = new g2o::RobustKernelHuber;
        e12->setRobustKernel(rk1);
        rk1->setDelta(deltaHuber);
        optimizer.addEdge(e12);

        // Set edge x2 = S21*X1
        // 2.5 另一个边
        Eigen::Matrix<double, 2, 1> obs2;
        cv::KeyPoint kpUn2;
        bool inKF2;
        // 投之前要确定下这个点的像素坐标
        if (i2 >= 0)
        {
            kpUn2 = pKF2->mvKeysUn[i2];
            obs2 << kpUn2.pt.x, kpUn2.pt.y;
            inKF2 = true;

            nInKF2++; // 输出信息，表示在kf2中找到MP2的点数
        }
        else //!BUG 如果没找到，使用三维点投影到KF2中，表示并没有特征点与之对应（把这个结果当做obs2是不是会带来一些误差，而且还不通过内参吗？？？，重大bug）
        {
            float invz = 1 / P3D2c(2);
            float x = P3D2c(0) * invz;
            float y = P3D2c(1) * invz;

            // float u = pKF2->fx * x + pKF2->cx;
            // float v = pKF2->fy * y + pKF2->cy;
            // obs2 << u, v;
            // kpUn2 = cv::KeyPoint(cv::Point2f(u, v), pMP2->mnTrackScaleLevel);
            obs2 << x, y;
            kpUn2 = cv::KeyPoint(cv::Point2f(x, y), pMP2->mnTrackScaleLevel);

            inKF2 = false;
            nOutKF2++;
        }

        // 1相机坐标系下的三维点经过g2oS12投影到kf2下计算重投影误差
        ORB_SLAM3::EdgeInverseSim3ProjectXYZ *e21 = new ORB_SLAM3::EdgeInverseSim3ProjectXYZ();

        e21->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id1)));
        e21->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(0)));
        e21->setMeasurement(obs2);
        float invSigmaSquare2 = pKF2->mvInvLevelSigma2[kpUn2.octave];
        e21->setInformation(Eigen::Matrix2d::Identity() * invSigmaSquare2);

        g2o::RobustKernelHuber *rk2 = new g2o::RobustKernelHuber;
        e21->setRobustKernel(rk2);
        rk2->setDelta(deltaHuber);
        optimizer.addEdge(e21);

        vpEdges12.push_back(e12);
        vpEdges21.push_back(e21);
        vnIndexEdge.push_back(i);

        vbIsInKF2.push_back(inKF2);
    }

    // Optimize!
    // 3. 开始优化
    optimizer.initializeOptimization();
    optimizer.optimize(5);

    // Check inliers
    // 4.剔除一些误差大的边，因为e12与e21对应的是同一个三维点，所以只要有一个误差太大就直接搞掉
    // Check inliers
    // 进行卡方检验，大于阈值的边剔除，同时删除鲁棒核函数
    int nBad = 0;
    int nBadOutKF2 = 0;
    for (size_t i = 0; i < vpEdges12.size(); i++)
    {
        ORB_SLAM3::EdgeSim3ProjectXYZ *e12 = vpEdges12[i];
        ORB_SLAM3::EdgeInverseSim3ProjectXYZ *e21 = vpEdges21[i];
        if (!e12 || !e21)
            continue;

        if (e12->chi2() > th2 || e21->chi2() > th2)
        {
            size_t idx = vnIndexEdge[i];
            vpMatches1[idx] = static_cast<MapPoint *>(NULL);
            optimizer.removeEdge(e12);
            optimizer.removeEdge(e21);
            vpEdges12[i] = static_cast<ORB_SLAM3::EdgeSim3ProjectXYZ *>(NULL);
            vpEdges21[i] = static_cast<ORB_SLAM3::EdgeInverseSim3ProjectXYZ *>(NULL);
            nBad++;

            if (!vbIsInKF2[i])
            {
                nBadOutKF2++;
            }
            continue;
        }

        // Check if remove the robust adjustment improve the result
        e12->setRobustKernel(0);
        e21->setRobustKernel(0);
    }

    // 如果有坏点，迭代次数更多
    int nMoreIterations;
    if (nBad > 0)
        nMoreIterations = 10;
    else
        nMoreIterations = 5;

    if (nCorrespondences - nBad < 10)
        return 0;

    // Optimize again only with inliers
    // 5. 再一次优化
    optimizer.initializeOptimization();
    optimizer.optimize(nMoreIterations);

    int nIn = 0;
    mAcumHessian = Eigen::MatrixXd::Zero(7, 7);
    // 更新vpMatches1，删除外点，统计内点数量
    for (size_t i = 0; i < vpEdges12.size(); i++)
    {
        ORB_SLAM3::EdgeSim3ProjectXYZ *e12 = vpEdges12[i];
        ORB_SLAM3::EdgeInverseSim3ProjectXYZ *e21 = vpEdges21[i];
        if (!e12 || !e21)
            continue;

        e12->computeError();
        e21->computeError();

        if (e12->chi2() > th2 || e21->chi2() > th2)
        {
            size_t idx = vnIndexEdge[i];
            vpMatches1[idx] = static_cast<MapPoint *>(NULL);
        }
        else
        {
            nIn++;
        }
    }

    // Recover optimized Sim3、
    // 6.得到优化后的结果
    g2o::VertexSim3Expmap *vSim3_recov = static_cast<g2o::VertexSim3Expmap *>(optimizer.vertex(0));
    g2oS12 = vSim3_recov->estimate();

    return nIn;
}

/**************************************以下为地图回环优化**************************************************************/

/**
 * @brief  LoopClosing::CorrectLoop() 回环矫正时使用，纯视觉，全局本质图优化
 * 优化目标： 地图中所有MP与关键帧
 * @param pMap                当前的map
 * @param pLoopKF             mpLoopMatchedKF 与 mpCurrentKF 匹配的关键帧
 * @param pCurKF              mpCurrentKF 当前关键帧
 * @param NonCorrectedSim3    通过pKFi->GetPose()计算的放NonCorrectedSim3也就是回环前的位姿 这里面的帧只是与mpCurrentKF相关联的
 * @param CorrectedSim3       通过mg2oLoopScw计算的放CorrectedSim3 这里面的帧只是与mpCurrentKF相关联的
 * @param LoopConnections     因为回环而建立的新的帧与帧的连接关系，里面的key 为pCurKF共视帧 value为共视帧的共视帧
 * @param bFixScale           单目imu且未到第三次imu初始化为false
 */
void Optimizer::OptimizeEssentialGraph(
    Map *pMap, KeyFrame *pLoopKF, KeyFrame *pCurKF,
    const LoopClosing::KeyFrameAndPose &NonCorrectedSim3,
    const LoopClosing::KeyFrameAndPose &CorrectedSim3,
    const map<KeyFrame *, set<KeyFrame *>> &LoopConnections, const bool &bFixScale)
{
    // Setup optimizer
    // Step 1：构造优化器
    g2o::SparseOptimizer optimizer;
    optimizer.setVerbose(false);
    // 指定线性方程求解器使用Eigen的块求解器
    // 7表示位姿是sim3  3表示三维点坐标维度
    g2o::BlockSolver_7_3::LinearSolverType *linearSolver =
        new g2o::LinearSolverEigen<g2o::BlockSolver_7_3::PoseMatrixType>();
    // 构造线性求解器
    g2o::BlockSolver_7_3 *solver_ptr = new g2o::BlockSolver_7_3(linearSolver);
    // 使用LM算法进行非线性迭代
    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

    // 第一次迭代的初始lambda值，如未指定会自动计算一个合适的值
    solver->setUserLambdaInit(1e-16);
    optimizer.setAlgorithm(solver);

    // 获取当前地图中的所有关键帧 和地图点
    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();
    const vector<MapPoint *> vpMPs = pMap->GetAllMapPoints();

    // 最大关键帧id，用于添加顶点时使用
    const unsigned int nMaxKFid = pMap->GetMaxKFid();

    // 记录所有优化前关键帧的位姿，优先使用在闭环时通过Sim3传播调整过的Sim3位姿
    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vScw(nMaxKFid + 1); // 存放每一帧优化前的sim3
    // 记录所有关键帧经过本次本质图优化过的位姿
    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vCorrectedSwc(nMaxKFid + 1); // 存放每一帧优化后的sim3，修正mp位姿用
    // 这个变量没有用
    vector<g2o::VertexSim3Expmap *> vpVertices(nMaxKFid + 1); // 存放节点，没用，还占地方

    // 调试用，暂时不去管
    vector<Eigen::Vector3d> vZvectors(nMaxKFid + 1); // For debugging
    Eigen::Vector3d z_vec;
    z_vec << 0.0, 0.0, 1.0;
    // 两个关键帧之间共视关系的权重（也就是共视点的数目，单目x1，双目/rgbd x2）的最小值
    const int minFeat = 100; // MODIFICATION originally was set to 100

    // Set KeyFrame vertices
    // Step 2：将地图中所有关键帧的pose作为顶点添加到优化器
    // 尽可能使用经过Sim3调整的位姿
    // 遍历全局地图中的所有的关键帧
    for (size_t i = 0, iend = vpKFs.size(); i < iend; i++)
    {
        KeyFrame *pKF = vpKFs[i];
        if (pKF->isBad())
            continue;
        g2o::VertexSim3Expmap *VSim3 = new g2o::VertexSim3Expmap();
        // 关键帧在所有关键帧中的id，用来设置为顶点的id
        const int nIDi = pKF->mnId;

        LoopClosing::KeyFrameAndPose::const_iterator it = CorrectedSim3.find(pKF);

        if (it != CorrectedSim3.end())
        {
            // 如果该关键帧在闭环时通过Sim3传播调整过，优先用调整后的Sim3位姿
            vScw[nIDi] = it->second;
            VSim3->setEstimate(it->second);
        }
        else
        {
            // 如果该关键帧在闭环时没有通过Sim3传播调整过，用跟踪时的位姿
            Sophus::SE3d Tcw = pKF->GetPose().cast<double>();
            g2o::Sim3 Siw(Tcw.unit_quaternion(), Tcw.translation(), 1.0);
            vScw[nIDi] = Siw;
            VSim3->setEstimate(Siw);
        }
        // 固定第一帧
        if (pKF->mnId == pMap->GetInitKFid())
            VSim3->setFixed(true);

        VSim3->setId(nIDi);
        VSim3->setMarginalized(false);
        // 和当前系统的传感器有关，如果是RGBD或者是双目，那么就不需要优化sim3的缩放系数，保持为1即可
        VSim3->_fix_scale = bFixScale;

        optimizer.addVertex(VSim3);
        vZvectors[nIDi] = vScw[nIDi].rotation() * z_vec; // For debugging
        // 优化前的pose顶点，后面代码中没有使用
        vpVertices[nIDi] = VSim3;
    }

    // 保存由于闭环后优化sim3而出现的新的关键帧和关键帧之间的连接关系,因为回环而新建立的连接关系，其中id比较小的关键帧在前,id比较大的关键帧在后
    set<pair<long unsigned int, long unsigned int>> sInsertedEdges;

    const Eigen::Matrix<double, 7, 7> matLambda = Eigen::Matrix<double, 7, 7>::Identity();

    // Set Loop edges
    // Step 3：添加第1种边：LoopConnections是闭环时因为MapPoints调整而出现的新关键帧连接关系（包括当前帧与闭环匹配帧之间的连接关系）
    int count_loop = 0;
    for (map<KeyFrame *, set<KeyFrame *>>::const_iterator mit = LoopConnections.begin(), mend = LoopConnections.end(); mit != mend; mit++)
    {
        // 3.1 取出帧与帧们
        KeyFrame *pKF = mit->first;
        const long unsigned int nIDi = pKF->mnId;
        // 和pKF 有连接关系的关键帧
        const set<KeyFrame *> &spConnections = mit->second;
        const g2o::Sim3 Siw = vScw[nIDi];  // 优化前的位姿
        const g2o::Sim3 Swi = Siw.inverse();

        // 对于当前关键帧nIDi而言，遍历每一个新添加的关键帧nIDj链接关系
        for (set<KeyFrame *>::const_iterator sit = spConnections.begin(), send = spConnections.end(); sit != send; sit++)
        {
            const long unsigned int nIDj = (*sit)->mnId;
            // 这里的约束有点意思，对于每一个连接，只要是存在pCurKF或者pLoopKF 那这个连接不管共视了多少MP都优化
            // 反之没有的话共视度要大于100 构建本质图
            if ((nIDi != pCurKF->mnId || nIDj != pLoopKF->mnId) && pKF->GetWeight(*sit) < minFeat)
                continue;

            // 通过上面考验的帧有两种情况：
            // 得到两个pose间的Sim3变换，个人认为这里好像有些问题，假设说一个当前帧的共视帧，他在vScw中保存的位姿是更新后的
            // 如果与之相连的关键帧没有更新，那么是不是两个相对位姿的边有问题，先留个记号，可以调试看看
            const g2o::Sim3 Sjw = vScw[nIDj];
            // 得到两个pose间的Sim3变换
            const g2o::Sim3 Sji = Sjw * Swi;  // 优化前他们的相对位姿

            g2o::EdgeSim3 *e = new g2o::EdgeSim3();
            e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
            e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
            // Sji内部是经过了Sim调整的观测
            e->setMeasurement(Sji);

            // 信息矩阵是单位阵,说明这类新增加的边对总误差的贡献也都是一样大的
            e->information() = matLambda;

            optimizer.addEdge(e);
            count_loop++;
            // 保证id小的在前,大的在后
            sInsertedEdges.insert(make_pair(min(nIDi, nIDj), max(nIDi, nIDj)));
        }
    }

    // Set normal edges
    // 4. 添加跟踪时形成的边、闭环匹配成功形成的边
    for (size_t i = 0, iend = vpKFs.size(); i < iend; i++)
    {
        KeyFrame *pKF = vpKFs[i];

        const int nIDi = pKF->mnId;

        g2o::Sim3 Swi;  // 校正前的sim3

        LoopClosing::KeyFrameAndPose::const_iterator iti = NonCorrectedSim3.find(pKF);
        // 找到的话说明是关键帧的共视帧，没找到表示非共视帧，非共视帧vScw[nIDi]里面装的都是矫正前的
        // 所以不管怎样说 Swi都是校正前的
        if (iti != NonCorrectedSim3.end())
            Swi = (iti->second).inverse();
        else
            Swi = vScw[nIDi].inverse();

        KeyFrame *pParentKF = pKF->GetParent();

        // Spanning tree edge
        // 4.1 只添加扩展树的边（有父关键帧）
        if (pParentKF)
        {
            int nIDj = pParentKF->mnId;

            g2o::Sim3 Sjw;

            LoopClosing::KeyFrameAndPose::const_iterator itj = NonCorrectedSim3.find(pParentKF);

            if (itj != NonCorrectedSim3.end())
                Sjw = itj->second;
            else
                Sjw = vScw[nIDj];

            // 又是未校正的结果作为观测值
            g2o::Sim3 Sji = Sjw * Swi;

            g2o::EdgeSim3 *e = new g2o::EdgeSim3();
            e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
            e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
            e->setMeasurement(Sji);
            e->information() = matLambda;
            optimizer.addEdge(e);
        }

        // Loop edges
        // 4.2 添加在CorrectLoop函数中AddLoopEdge函数添加的闭环连接边（当前帧与闭环匹配帧之间的连接关系）
        // 使用经过Sim3调整前关键帧之间的相对关系作为边
        const set<KeyFrame *> sLoopEdges = pKF->GetLoopEdges();
        for (set<KeyFrame *>::const_iterator sit = sLoopEdges.begin(), send = sLoopEdges.end(); sit != send; sit++)
        {
            KeyFrame *pLKF = *sit;
            if (pLKF->mnId < pKF->mnId)
            {
                g2o::Sim3 Slw;

                LoopClosing::KeyFrameAndPose::const_iterator itl = NonCorrectedSim3.find(pLKF);

                if (itl != NonCorrectedSim3.end())
                    Slw = itl->second;
                else
                    Slw = vScw[pLKF->mnId];

                g2o::Sim3 Sli = Slw * Swi;
                g2o::EdgeSim3 *el = new g2o::EdgeSim3();
                el->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pLKF->mnId)));
                el->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                el->setMeasurement(Sli);
                el->information() = matLambda;
                optimizer.addEdge(el);
            }
        }

        // Covisibility graph edges
        // 4.3 对有很好共视关系的关键帧也作为边进行优化
        // 使用经过Sim3调整前关键帧之间的相对关系作为边
        const vector<KeyFrame *> vpConnectedKFs = pKF->GetCovisiblesByWeight(minFeat);
        for (vector<KeyFrame *>::const_iterator vit = vpConnectedKFs.begin(); vit != vpConnectedKFs.end(); vit++)
        {
            KeyFrame *pKFn = *vit;
            if (pKFn && pKFn != pParentKF && !pKF->hasChild(pKFn) /*&& !sLoopEdges.count(pKFn)*/)
            {
                if (!pKFn->isBad() && pKFn->mnId < pKF->mnId)
                {
                    if (sInsertedEdges.count(make_pair(min(pKF->mnId, pKFn->mnId), max(pKF->mnId, pKFn->mnId))))
                        continue;

                    g2o::Sim3 Snw;

                    LoopClosing::KeyFrameAndPose::const_iterator itn = NonCorrectedSim3.find(pKFn);

                    if (itn != NonCorrectedSim3.end())
                        Snw = itn->second;
                    else
                        Snw = vScw[pKFn->mnId];

                    g2o::Sim3 Sni = Snw * Swi;

                    g2o::EdgeSim3 *en = new g2o::EdgeSim3();
                    en->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFn->mnId)));
                    en->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                    en->setMeasurement(Sni);
                    en->information() = matLambda;
                    optimizer.addEdge(en);
                }
            }
        }

        // Inertial edges if inertial
        // 如果是imu的话还会找前一帧做优化
        if (pKF->bImu && pKF->mPrevKF)
        {
            g2o::Sim3 Spw;
            LoopClosing::KeyFrameAndPose::const_iterator itp = NonCorrectedSim3.find(pKF->mPrevKF);
            if (itp != NonCorrectedSim3.end())
                Spw = itp->second;
            else
                Spw = vScw[pKF->mPrevKF->mnId];

            g2o::Sim3 Spi = Spw * Swi;
            g2o::EdgeSim3 *ep = new g2o::EdgeSim3();
            ep->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mPrevKF->mnId)));
            ep->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
            ep->setMeasurement(Spi);
            ep->information() = matLambda;
            optimizer.addEdge(ep);
        }
    }

    // Optimize!
    // 5. 开始g2o优化
    optimizer.initializeOptimization();
    optimizer.computeActiveErrors();
    optimizer.optimize(20);
    optimizer.computeActiveErrors();
    unique_lock<mutex> lock(pMap->mMutexMapUpdate);

    // SE3 Pose Recovering. Sim3:[sR t;0 1] -> SE3:[R t/s;0 1]
    // 6. 设定优化后的位姿
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];

        const int nIDi = pKFi->mnId;

        g2o::VertexSim3Expmap *VSim3 = static_cast<g2o::VertexSim3Expmap *>(optimizer.vertex(nIDi));
        g2o::Sim3 CorrectedSiw = VSim3->estimate();
        vCorrectedSwc[nIDi] = CorrectedSiw.inverse();
        double s = CorrectedSiw.scale();

        Sophus::SE3f Tiw(CorrectedSiw.rotation().cast<float>(), CorrectedSiw.translation().cast<float>() / s);
        pKFi->SetPose(Tiw);
    }

    // Correct points. Transform to "non-optimized" reference keyframe pose and transform back with optimized pose
    // 7. 步骤5和步骤6优化得到关键帧的位姿后，MapPoints根据参考帧优化前后的相对关系调整自己的位置
    for (size_t i = 0, iend = vpMPs.size(); i < iend; i++)
    {
        MapPoint *pMP = vpMPs[i];

        if (pMP->isBad())
            continue;

        int nIDr;
        // 该MapPoint经过Sim3调整过，(LoopClosing.cpp，CorrectLoop函数，步骤2.2_
        if (pMP->mnCorrectedByKF == pCurKF->mnId)
        {
            nIDr = pMP->mnCorrectedReference;
        }
        else
        {
            // 通过情况下MapPoint的参考关键帧就是创建该MapPoint的那个关键帧
            KeyFrame *pRefKF = pMP->GetReferenceKeyFrame();
            nIDr = pRefKF->mnId;
        }

        // 得到MapPoint参考关键帧步骤5优化前的位姿
        g2o::Sim3 Srw = vScw[nIDr];
        // 得到MapPoint参考关键帧优化后的位姿
        g2o::Sim3 correctedSwr = vCorrectedSwc[nIDr];

        // 更新前坐标
        Eigen::Matrix<double, 3, 1> eigP3Dw = pMP->GetWorldPos().cast<double>();
        // 更新后坐标
        Eigen::Matrix<double, 3, 1> eigCorrectedP3Dw = correctedSwr.map(Srw.map(eigP3Dw));
        pMP->SetWorldPos(eigCorrectedP3Dw.cast<float>());

        pMP->UpdateNormalAndDepth();
    }

    // TODO Check this changeindex
    pMap->IncreaseChangeIndex();
}

/**
 * @brief  LoopClosing::CorrectLoop() 回环矫正时使用，IMU加视觉，全局本质图优化，流程基本与上面 OptimizeEssentialGraph 一模一样，同样有严重BUG
 * 优化目标： 地图中所有MP与关键帧
 * @param pMap                当前的map
 * @param pLoopKF             mpLoopMatchedKF 与 mpCurrentKF 匹配的关键帧
 * @param pCurKF              mpCurrentKF 当前关键帧
 * @param NonCorrectedSim3    通过pKFi->GetPose()计算的放NonCorrectedSim3也就是回环前的位姿 这里面的帧只是与mpCurrentKF相关联的
 * @param CorrectedSim3       通过mg2oLoopScw计算的放CorrectedSim3 这里面的帧只是与mpCurrentKF共视的
 * @param LoopConnections     因为回环而建立的新的帧与帧的连接关系，里面的key 全部为pCurKF的共视帧与其本身
 */
void Optimizer::OptimizeEssentialGraph4DoF(
    Map *pMap, KeyFrame *pLoopKF, KeyFrame *pCurKF,
    const LoopClosing::KeyFrameAndPose &NonCorrectedSim3,
    const LoopClosing::KeyFrameAndPose &CorrectedSim3,
    const map<KeyFrame *, set<KeyFrame *>> &LoopConnections)
{
    typedef g2o::BlockSolver<g2o::BlockSolverTraits<4, 4>> BlockSolver_4_4;

    // Setup optimizer
    // 1. 构建优化器
    g2o::SparseOptimizer optimizer;
    optimizer.setVerbose(false);
    g2o::BlockSolverX::LinearSolverType *linearSolver =
        new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();
    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

    optimizer.setAlgorithm(solver);

    const vector<KeyFrame *> vpKFs = pMap->GetAllKeyFrames();  // 所有关键帧
    const vector<MapPoint *> vpMPs = pMap->GetAllMapPoints();  // 所有mp

    const unsigned int nMaxKFid = pMap->GetMaxKFid();

    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vScw(nMaxKFid + 1);           // 存放每一帧优化前的sim3
    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vCorrectedSwc(nMaxKFid + 1);  // 存放每一帧优化后的sim3，修正mp位姿用

    vector<VertexPose4DoF *> vpVertices(nMaxKFid + 1);

    const int minFeat = 100;  // 100 本质图的权重
    // Set KeyFrame vertices
    // 2. 关键帧节点
    for (size_t i = 0, iend = vpKFs.size(); i < iend; i++)
    {
        KeyFrame *pKF = vpKFs[i];
        if (pKF->isBad())
            continue;

        // 自定义的一个优化4自由度的节点
        VertexPose4DoF *V4DoF;

        const int nIDi = pKF->mnId;

        LoopClosing::KeyFrameAndPose::const_iterator it = CorrectedSim3.find(pKF);

        // 找到了表示与当前关键帧共视，使用其他函数更新后的位姿
        if (it != CorrectedSim3.end())
        {
            vScw[nIDi] = it->second;
            const g2o::Sim3 Swc = it->second.inverse();
            Eigen::Matrix3d Rwc = Swc.rotation().toRotationMatrix();
            Eigen::Vector3d twc = Swc.translation();
            V4DoF = new VertexPose4DoF(Rwc, twc, pKF);
        }
        else
        {
            // 没有在CorrectedSim3里面找到表示与pCurKF并不关联，也就是离得远，并没有经过计算得到一个初始值，这里直接使用原始的位置
            Sophus::SE3d Tcw = pKF->GetPose().cast<double>();
            g2o::Sim3 Siw(Tcw.unit_quaternion(), Tcw.translation(), 1.0);

            vScw[nIDi] = Siw;
            V4DoF = new VertexPose4DoF(pKF);
        }

        // 固定回环帧
        if (pKF == pLoopKF)
            V4DoF->setFixed(true);

        V4DoF->setId(nIDi);
        V4DoF->setMarginalized(false);

        optimizer.addVertex(V4DoF);
        vpVertices[nIDi] = V4DoF;
    }

    set<pair<long unsigned int, long unsigned int>> sInsertedEdges;

    // Edge used in posegraph has still 6Dof, even if updates of camera poses are just in 4DoF
    Eigen::Matrix<double, 6, 6> matLambda = Eigen::Matrix<double, 6, 6>::Identity();
    matLambda(0, 0) = 1e3;
    matLambda(1, 1) = 1e3;
    matLambda(0, 0) = 1e3;

    // Set Loop edges
    // 3. 添加边：LoopConnections是闭环时因为MapPoints调整而出现的新关键帧连接关系（包括当前帧与闭环匹配帧之间的连接关系）
    Edge4DoF *e_loop;
    for (map<KeyFrame *, set<KeyFrame *>>::const_iterator mit = LoopConnections.begin(), mend = LoopConnections.end(); mit != mend; mit++)
    {
        // 3.1 取出帧与帧们
        KeyFrame *pKF = mit->first;
        const long unsigned int nIDi = pKF->mnId;
        const set<KeyFrame *> &spConnections = mit->second;
        const g2o::Sim3 Siw = vScw[nIDi];  // 优化前的位姿

        for (set<KeyFrame *>::const_iterator sit = spConnections.begin(), send = spConnections.end(); sit != send; sit++)
        {
            const long unsigned int nIDj = (*sit)->mnId;
            // 这里的约束有点意思，对于每一个连接，只要是存在pCurKF或者pLoopKF 那这个连接不管共视了多少MP都优化
            // 反之没有的话共视度要大于100 构建本质图
            if ((nIDi != pCurKF->mnId || nIDj != pLoopKF->mnId) && pKF->GetWeight(*sit) < minFeat)
                continue;

            const g2o::Sim3 Sjw = vScw[nIDj];
            // 得到两个pose间的Sim3变换，个人认为这里好像有些问题，假设说一个当前帧的共视帧，他在vScw中保存的位姿是更新后的
            // 如果与之相连的关键帧没有更新，那么是不是两个相对位姿的边有问题，先留个记号，可以调试看看
            const g2o::Sim3 Sij = Siw * Sjw.inverse();
            Eigen::Matrix4d Tij;
            Tij.block<3, 3>(0, 0) = Sij.rotation().toRotationMatrix();
            Tij.block<3, 1>(0, 3) = Sij.translation();
            Tij(3, 3) = 1.;

            // 认为相对位姿会比较准，那这个当一个约束
            Edge4DoF *e = new Edge4DoF(Tij);
            e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
            e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));

            e->information() = matLambda;
            e_loop = e;
            optimizer.addEdge(e);

            sInsertedEdges.insert(make_pair(min(nIDi, nIDj), max(nIDi, nIDj)));
        }
    }

    // 1. Set normal edges
    // 4. 添加跟踪时形成的边、闭环匹配成功形成的边
    for (size_t i = 0, iend = vpKFs.size(); i < iend; i++)
    {
        KeyFrame *pKF = vpKFs[i];

        const int nIDi = pKF->mnId;

        g2o::Sim3 Siw;

        // Use noncorrected poses for posegraph edges
        LoopClosing::KeyFrameAndPose::const_iterator iti = NonCorrectedSim3.find(pKF);

        // 找到的话说明是关键帧的共视帧，没找到表示非共视帧，非共视帧vScw[nIDi]里面装的都是矫正前的
        // 所以不管怎样说 Swi都是校正前的
        if (iti != NonCorrectedSim3.end())
            Siw = iti->second;
        else
            Siw = vScw[nIDi];

        // 1.1.0 Spanning tree edge
        // 4.1 只添加扩展树的边（有父关键帧） 这里并没有父帧，这段没执行到
        KeyFrame *pParentKF = static_cast<KeyFrame *>(NULL);
        if (pParentKF)
        {
            int nIDj = pParentKF->mnId;

            g2o::Sim3 Swj;

            LoopClosing::KeyFrameAndPose::const_iterator itj = NonCorrectedSim3.find(pParentKF);

            if (itj != NonCorrectedSim3.end())
                Swj = (itj->second).inverse();
            else
                Swj = vScw[nIDj].inverse();

            g2o::Sim3 Sij = Siw * Swj;
            Eigen::Matrix4d Tij;
            Tij.block<3, 3>(0, 0) = Sij.rotation().toRotationMatrix();
            Tij.block<3, 1>(0, 3) = Sij.translation();
            Tij(3, 3) = 1.;

            Edge4DoF *e = new Edge4DoF(Tij);
            e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
            e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
            e->information() = matLambda;
            optimizer.addEdge(e);
        }

        // 1.1.1 Inertial edges
        // 代替父帧的是利用mPrevKF，流程与上面一样
        KeyFrame *prevKF = pKF->mPrevKF;
        if (prevKF)
        {
            int nIDj = prevKF->mnId;

            g2o::Sim3 Swj;

            LoopClosing::KeyFrameAndPose::const_iterator itj = NonCorrectedSim3.find(prevKF);

            if (itj != NonCorrectedSim3.end())
                Swj = (itj->second).inverse();
            else
                Swj = vScw[nIDj].inverse();

            g2o::Sim3 Sij = Siw * Swj;
            Eigen::Matrix4d Tij;
            Tij.block<3, 3>(0, 0) = Sij.rotation().toRotationMatrix();
            Tij.block<3, 1>(0, 3) = Sij.translation();
            Tij(3, 3) = 1.;

            Edge4DoF *e = new Edge4DoF(Tij);
            e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
            e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
            e->information() = matLambda;
            optimizer.addEdge(e);
        }

        // 1.2 Loop edges
        // 4.2 添加在CorrectLoop函数中AddLoopEdge函数添加的闭环连接边（当前帧与闭环匹配帧之间的连接关系）
        // 使用经过Sim3调整前关键帧之间的相对关系作为边
        const set<KeyFrame *> sLoopEdges = pKF->GetLoopEdges();
        for (set<KeyFrame *>::const_iterator sit = sLoopEdges.begin(), send = sLoopEdges.end(); sit != send; sit++)
        {
            KeyFrame *pLKF = *sit;
            if (pLKF->mnId < pKF->mnId)
            {
                g2o::Sim3 Swl;

                LoopClosing::KeyFrameAndPose::const_iterator itl = NonCorrectedSim3.find(pLKF);

                if (itl != NonCorrectedSim3.end())
                    Swl = itl->second.inverse();
                else
                    Swl = vScw[pLKF->mnId].inverse();

                g2o::Sim3 Sil = Siw * Swl;
                Eigen::Matrix4d Til;
                Til.block<3, 3>(0, 0) = Sil.rotation().toRotationMatrix();
                Til.block<3, 1>(0, 3) = Sil.translation();
                Til(3, 3) = 1.;

                // 同样，认为相对位姿比较准
                Edge4DoF *e = new Edge4DoF(Til);
                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pLKF->mnId)));
                e->information() = matLambda;
                optimizer.addEdge(e);
            }
        }

        // 1.3 Covisibility graph edges
        // 4.3 最有很好共视关系的关键帧也作为边进行优化
        // 使用经过Sim3调整前关键帧之间的相对关系作为边
        const vector<KeyFrame *> vpConnectedKFs = pKF->GetCovisiblesByWeight(minFeat);
        for (vector<KeyFrame *>::const_iterator vit = vpConnectedKFs.begin(); vit != vpConnectedKFs.end(); vit++)
        {
            KeyFrame *pKFn = *vit;
            // 在之前没有添加过
            if (pKFn && pKFn != pParentKF && pKFn != prevKF && pKFn != pKF->mNextKF && !pKF->hasChild(pKFn) && !sLoopEdges.count(pKFn))
            {
                if (!pKFn->isBad() && pKFn->mnId < pKF->mnId)
                {
                    if (sInsertedEdges.count(make_pair(min(pKF->mnId, pKFn->mnId), max(pKF->mnId, pKFn->mnId))))
                        continue;

                    g2o::Sim3 Swn;

                    LoopClosing::KeyFrameAndPose::const_iterator itn = NonCorrectedSim3.find(pKFn);

                    if (itn != NonCorrectedSim3.end())
                        Swn = itn->second.inverse();
                    else
                        Swn = vScw[pKFn->mnId].inverse();

                    g2o::Sim3 Sin = Siw * Swn;
                    Eigen::Matrix4d Tin;
                    Tin.block<3, 3>(0, 0) = Sin.rotation().toRotationMatrix();
                    Tin.block<3, 1>(0, 3) = Sin.translation();
                    Tin(3, 3) = 1.;
                    Edge4DoF *e = new Edge4DoF(Tin);
                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFn->mnId)));
                    e->information() = matLambda;
                    optimizer.addEdge(e);
                }
            }
        }
    }

    // 5. 开始g2o优化
    optimizer.initializeOptimization();
    optimizer.computeActiveErrors();
    optimizer.optimize(20);

    unique_lock<mutex> lock(pMap->mMutexMapUpdate);

    // SE3 Pose Recovering. Sim3:[sR t;0 1] -> SE3:[R t/s;0 1]
    // 6. 设定优化后的位姿
    for (size_t i = 0; i < vpKFs.size(); i++)
    {
        KeyFrame *pKFi = vpKFs[i];

        const int nIDi = pKFi->mnId;

        VertexPose4DoF *Vi = static_cast<VertexPose4DoF *>(optimizer.vertex(nIDi));
        Eigen::Matrix3d Ri = Vi->estimate().Rcw[0];
        Eigen::Vector3d ti = Vi->estimate().tcw[0];

        g2o::Sim3 CorrectedSiw = g2o::Sim3(Ri, ti, 1.);
        vCorrectedSwc[nIDi] = CorrectedSiw.inverse();

        Sophus::SE3d Tiw(CorrectedSiw.rotation(), CorrectedSiw.translation());
        pKFi->SetPose(Tiw.cast<float>());
    }

    // Correct points. Transform to "non-optimized" reference keyframe pose and transform back with optimized pose
    // 7. 步骤5和步骤6优化得到关键帧的位姿后，MapPoints根据参考帧优化前后的相对关系调整自己的位置
    for (size_t i = 0, iend = vpMPs.size(); i < iend; i++)
    {
        MapPoint *pMP = vpMPs[i];

        if (pMP->isBad())
            continue;

        int nIDr;

        KeyFrame *pRefKF = pMP->GetReferenceKeyFrame();
        nIDr = pRefKF->mnId;

        // 得到MapPoint参考关键帧步骤5优化前的位姿
        g2o::Sim3 Srw = vScw[nIDr];
        // 得到MapPoint参考关键帧优化后的位姿
        g2o::Sim3 correctedSwr = vCorrectedSwc[nIDr];

        // 更新的过程就是先将他通过原始位姿转到相机坐标系下，再通过新的位姿转到更新后的世界坐标
        Eigen::Matrix<double, 3, 1> eigP3Dw = pMP->GetWorldPos().cast<double>();
        Eigen::Matrix<double, 3, 1> eigCorrectedP3Dw = correctedSwr.map(Srw.map(eigP3Dw));
        pMP->SetWorldPos(eigCorrectedP3Dw.cast<float>());

        pMP->UpdateNormalAndDepth();
    }
    pMap->IncreaseChangeIndex();
}



/**************************************以下为地图融合优化**************************************************************/

/**
 * @brief Local Bundle Adjustment LoopClosing::MergeLocal() 融合地图时使用，纯视觉
 * 优化目标： 1. vpAdjustKF; 2.vpAdjustKF与vpFixedKF对应的MP点
 * 优化所有的当前关键帧共视窗口里的关键帧和地图点, 固定所有融合帧共视窗口里的帧
 * @param pMainKF        mpCurrentKF 当前关键帧
 * @param vpAdjustKF     vpLocalCurrentWindowKFs 待优化的KF
 * @param vpFixedKF      vpMergeConnectedKFs 固定的KF
 * @param pbStopFlag     false
 */
void Optimizer::LocalBundleAdjustment(
    KeyFrame *pMainKF, vector<KeyFrame *> vpAdjustKF, vector<KeyFrame *> vpFixedKF, bool *pbStopFlag)
{
    bool bShowImages = false;

    vector<MapPoint *> vpMPs;

    // 1. 构建g2o优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolver_6_3::LinearSolverType *linearSolver;

    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolver_6_3::PoseMatrixType>();

    g2o::BlockSolver_6_3 *solver_ptr = new g2o::BlockSolver_6_3(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    optimizer.setAlgorithm(solver);

    optimizer.setVerbose(false);

    if (pbStopFlag)
        optimizer.setForceStopFlag(pbStopFlag);

    long unsigned int maxKFid = 0;
    set<KeyFrame *> spKeyFrameBA;  // 存放关键帧，包含固定的与不固定的

    Map *pCurrentMap = pMainKF->GetMap();

    // Set fixed KeyFrame vertices
    // 2. 构建固定关键帧的节点，并储存对应的MP
    int numInsertedPoints = 0;
    for (KeyFrame *pKFi : vpFixedKF)
    {
        if (pKFi->isBad() || pKFi->GetMap() != pCurrentMap)
        {
            Verbose::PrintMess("ERROR LBA: KF is bad or is not in the current map", Verbose::VERBOSITY_NORMAL);
            continue;
        }

        pKFi->mnBALocalForMerge = pMainKF->mnId;  // 防止重复添加

        g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
        Sophus::SE3<float> Tcw = pKFi->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        vSE3->setId(pKFi->mnId);
        vSE3->setFixed(true);
        optimizer.addVertex(vSE3);
        if (pKFi->mnId > maxKFid)
            maxKFid = pKFi->mnId;

        set<MapPoint *> spViewMPs = pKFi->GetMapPoints();
        for (MapPoint *pMPi : spViewMPs)
        {
            if (pMPi)
                if (!pMPi->isBad() && pMPi->GetMap() == pCurrentMap)

                    if (pMPi->mnBALocalForMerge != pMainKF->mnId)
                    {
                        vpMPs.push_back(pMPi);
                        pMPi->mnBALocalForMerge = pMainKF->mnId;
                        numInsertedPoints++;
                    }
        }

        spKeyFrameBA.insert(pKFi);
    }

    // Set non fixed Keyframe vertices
    // 3. 构建不固定关键帧的节点，并储存对应的MP
    set<KeyFrame *> spAdjustKF(vpAdjustKF.begin(), vpAdjustKF.end());
    numInsertedPoints = 0;
    for (KeyFrame *pKFi : vpAdjustKF)
    {
        if (pKFi->isBad() || pKFi->GetMap() != pCurrentMap)
            continue;

        pKFi->mnBALocalForMerge = pMainKF->mnId;  // 防止重复添加

        g2o::VertexSE3Expmap *vSE3 = new g2o::VertexSE3Expmap();
        Sophus::SE3<float> Tcw = pKFi->GetPose();
        vSE3->setEstimate(g2o::SE3Quat(Tcw.unit_quaternion().cast<double>(), Tcw.translation().cast<double>()));
        vSE3->setId(pKFi->mnId);
        optimizer.addVertex(vSE3);
        if (pKFi->mnId > maxKFid)
            maxKFid = pKFi->mnId;

        set<MapPoint *> spViewMPs = pKFi->GetMapPoints();
        for (MapPoint *pMPi : spViewMPs)
        {
            if (pMPi)
            {
                if (!pMPi->isBad() && pMPi->GetMap() == pCurrentMap)
                {
                    if (pMPi->mnBALocalForMerge != pMainKF->mnId)
                    {
                        vpMPs.push_back(pMPi);
                        pMPi->mnBALocalForMerge = pMainKF->mnId;
                        numInsertedPoints++;
                    }
                }
            }
        }

        spKeyFrameBA.insert(pKFi);
    }

    // 准备存放边的vector
    const int nExpectedSize = (vpAdjustKF.size() + vpFixedKF.size()) * vpMPs.size();

    vector<ORB_SLAM3::EdgeSE3ProjectXYZ *> vpEdgesMono;
    vpEdgesMono.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFMono;
    vpEdgeKFMono.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeMono;
    vpMapPointEdgeMono.reserve(nExpectedSize);

    vector<g2o::EdgeStereoSE3ProjectXYZ *> vpEdgesStereo;
    vpEdgesStereo.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFStereo;
    vpEdgeKFStereo.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeStereo;
    vpMapPointEdgeStereo.reserve(nExpectedSize);

    const float thHuber2D = sqrt(5.99);
    const float thHuber3D = sqrt(7.815);

    // Set MapPoint vertices
    map<KeyFrame *, int> mpObsKFs;      // 统计每个关键帧对应的MP点数，调试输出用
    map<KeyFrame *, int> mpObsFinalKFs; // 统计每个MP对应的关键帧数，调试输出用
    map<MapPoint *, int> mpObsMPs;      // 统计每个MP被观测的图片数，双目就是两个，调试输出用

    // 4. 确定MP节点与边的连接
    for (unsigned int i = 0; i < vpMPs.size(); ++i)
    {
        MapPoint *pMPi = vpMPs[i];
        if (pMPi->isBad())
            continue;

        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMPi->GetWorldPos().cast<double>());
        const int id = pMPi->mnId + maxKFid + 1;
        vPoint->setId(id);
        vPoint->setMarginalized(true);
        optimizer.addVertex(vPoint);

        const map<KeyFrame *, tuple<int, int>> observations = pMPi->GetObservations();
        int nEdges = 0;
        // SET EDGES
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(); mit != observations.end(); mit++)
        {
            KeyFrame *pKF = mit->first;
            // 跳过的条件
            // 1. 帧坏了
            // 2. 帧靠后
            // 3. 不在参与优化帧的里面
            // 4. 在左相机上不存在这个三维点
            if (pKF->isBad() || pKF->mnId > maxKFid || pKF->mnBALocalForMerge != pMainKF->mnId || !pKF->GetMapPoint(get<0>(mit->second)))
                continue;

            nEdges++;

            const cv::KeyPoint &kpUn = pKF->mvKeysUn[get<0>(mit->second)];

            // 注意！！！这里没有考虑相机2
            if (pKF->mvuRight[get<0>(mit->second)] < 0) // Monocular
            {
                mpObsMPs[pMPi]++;
                Eigen::Matrix<double, 2, 1> obs;
                obs << kpUn.pt.x, kpUn.pt.y;

                ORB_SLAM3::EdgeSE3ProjectXYZ *e = new ORB_SLAM3::EdgeSE3ProjectXYZ();

                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mnId)));
                e->setMeasurement(obs);
                const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave];
                e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                e->setRobustKernel(rk);
                rk->setDelta(thHuber2D);

                e->pCamera = pKF->mpCamera;

                optimizer.addEdge(e);

                vpEdgesMono.push_back(e);
                vpEdgeKFMono.push_back(pKF);
                vpMapPointEdgeMono.push_back(pMPi);

                mpObsKFs[pKF]++;
            }
            else // RGBD or Stereo
            {
                mpObsMPs[pMPi] += 2;
                Eigen::Matrix<double, 3, 1> obs;
                const float kp_ur = pKF->mvuRight[get<0>(mit->second)];
                obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                g2o::EdgeStereoSE3ProjectXYZ *e = new g2o::EdgeStereoSE3ProjectXYZ();

                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKF->mnId)));
                e->setMeasurement(obs);
                const float &invSigma2 = pKF->mvInvLevelSigma2[kpUn.octave];
                Eigen::Matrix3d Info = Eigen::Matrix3d::Identity() * invSigma2;
                e->setInformation(Info);

                g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                e->setRobustKernel(rk);
                rk->setDelta(thHuber3D);

                e->fx = pKF->fx;
                e->fy = pKF->fy;
                e->cx = pKF->cx;
                e->cy = pKF->cy;
                e->bf = pKF->mbf;

                optimizer.addEdge(e);

                vpEdgesStereo.push_back(e);
                vpEdgeKFStereo.push_back(pKF);
                vpMapPointEdgeStereo.push_back(pMPi);

                mpObsKFs[pKF]++;
            }
        }
    }

    if (pbStopFlag)
        if (*pbStopFlag)
            return;
    // 5. 优化
    optimizer.initializeOptimization();
    optimizer.optimize(5);

    bool bDoMore = true;

    if (pbStopFlag)
        if (*pbStopFlag)
            bDoMore = false;
    // 6. 剔除误差大的边
    map<unsigned long int, int> mWrongObsKF;
    if (bDoMore)
    {
        // Check inlier observations
        int badMonoMP = 0, badStereoMP = 0;
        for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
        {
            ORB_SLAM3::EdgeSE3ProjectXYZ *e = vpEdgesMono[i];
            MapPoint *pMP = vpMapPointEdgeMono[i];

            if (pMP->isBad())
                continue;

            if (e->chi2() > 5.991 || !e->isDepthPositive())
            {
                e->setLevel(1);
                badMonoMP++;
            }
            e->setRobustKernel(0);
        }

        for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
        {
            g2o::EdgeStereoSE3ProjectXYZ *e = vpEdgesStereo[i];
            MapPoint *pMP = vpMapPointEdgeStereo[i];

            if (pMP->isBad())
                continue;

            if (e->chi2() > 7.815 || !e->isDepthPositive())
            {
                e->setLevel(1);
                badStereoMP++;
            }

            e->setRobustKernel(0);
        }
        Verbose::PrintMess("[BA]: First optimization(Huber), there are " + to_string(badMonoMP) + " monocular and " + to_string(badStereoMP) + " stereo bad edges", Verbose::VERBOSITY_DEBUG);

        optimizer.initializeOptimization(0);
        optimizer.optimize(10);
    }

    // 7. 删除误差大的点与帧的连接关系
    vector<pair<KeyFrame *, MapPoint *>> vToErase;
    vToErase.reserve(vpEdgesMono.size() + vpEdgesStereo.size());
    set<MapPoint *> spErasedMPs;
    set<KeyFrame *> spErasedKFs;

    // Check inlier observations
    int badMonoMP = 0, badStereoMP = 0;
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        ORB_SLAM3::EdgeSE3ProjectXYZ *e = vpEdgesMono[i];
        MapPoint *pMP = vpMapPointEdgeMono[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > 5.991 || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFMono[i];
            vToErase.push_back(make_pair(pKFi, pMP));
            mWrongObsKF[pKFi->mnId]++;
            badMonoMP++;

            spErasedMPs.insert(pMP);
            spErasedKFs.insert(pKFi);
        }
    }

    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        g2o::EdgeStereoSE3ProjectXYZ *e = vpEdgesStereo[i];
        MapPoint *pMP = vpMapPointEdgeStereo[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > 7.815 || !e->isDepthPositive())
        {
            KeyFrame *pKFi = vpEdgeKFStereo[i];
            vToErase.push_back(make_pair(pKFi, pMP));
            mWrongObsKF[pKFi->mnId]++;
            badStereoMP++;

            spErasedMPs.insert(pMP);
            spErasedKFs.insert(pKFi);
        }
    }

    Verbose::PrintMess("[BA]: Second optimization, there are " + to_string(badMonoMP) + " monocular and " + to_string(badStereoMP) + " sterero bad edges", Verbose::VERBOSITY_DEBUG);

    // Get Map Mutex
    unique_lock<mutex> lock(pMainKF->GetMap()->mMutexMapUpdate);

    if (!vToErase.empty())
    {
        // 剔除链接关系，其他均为调试
        for (size_t i = 0; i < vToErase.size(); i++)
        {
            KeyFrame *pKFi = vToErase[i].first;
            MapPoint *pMPi = vToErase[i].second;
            pKFi->EraseMapPointMatch(pMPi);
            pMPi->EraseObservation(pKFi);
        }
    }
    // 没用，调试的
    for (unsigned int i = 0; i < vpMPs.size(); ++i)
    {
        MapPoint *pMPi = vpMPs[i];
        if (pMPi->isBad())
            continue;

        const map<KeyFrame *, tuple<int, int>> observations = pMPi->GetObservations();
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(); mit != observations.end(); mit++)
        {
            KeyFrame *pKF = mit->first;
            if (pKF->isBad() || pKF->mnId > maxKFid || pKF->mnBALocalForKF != pMainKF->mnId || !pKF->GetMapPoint(get<0>(mit->second)))
                continue;

            if (pKF->mvuRight[get<0>(mit->second)] < 0) // Monocular
            {
                mpObsFinalKFs[pKF]++;
            }
            else // RGBD or Stereo
            {
                mpObsFinalKFs[pKF]++;
            }
        }
    }

    // Recover optimized data
    // 8. 取出结果
    // Keyframes
    for (KeyFrame *pKFi : vpAdjustKF)
    {
        if (pKFi->isBad())
            continue;
        // 8.1 取出对应位姿
        g2o::VertexSE3Expmap *vSE3 = static_cast<g2o::VertexSE3Expmap *>(optimizer.vertex(pKFi->mnId));
        g2o::SE3Quat SE3quat = vSE3->estimate();
        Sophus::SE3f Tiw(SE3quat.rotation().cast<float>(), SE3quat.translation().cast<float>());

        // 统计调试用
        int numMonoBadPoints = 0, numMonoOptPoints = 0;
        int numStereoBadPoints = 0, numStereoOptPoints = 0;
        vector<MapPoint *> vpMonoMPsOpt, vpStereoMPsOpt; // 存放mp内点
        vector<MapPoint *> vpMonoMPsBad, vpStereoMPsBad; // 存放mp外点
        // 8.2 卡方检验，调试用的貌似没别的作用
        for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
        {
            ORB_SLAM3::EdgeSE3ProjectXYZ *e = vpEdgesMono[i];
            MapPoint *pMP = vpMapPointEdgeMono[i];
            KeyFrame *pKFedge = vpEdgeKFMono[i];

            if (pKFi != pKFedge)
            {
                continue;
            }

            if (pMP->isBad())
                continue;

            if (e->chi2() > 5.991 || !e->isDepthPositive())
            {
                numMonoBadPoints++;
                vpMonoMPsBad.push_back(pMP);
            }
            else
            {
                numMonoOptPoints++;
                vpMonoMPsOpt.push_back(pMP);
            }
        }

        for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
        {
            g2o::EdgeStereoSE3ProjectXYZ *e = vpEdgesStereo[i];
            MapPoint *pMP = vpMapPointEdgeStereo[i];
            KeyFrame *pKFedge = vpEdgeKFMono[i];

            if (pKFi != pKFedge)
            {
                continue;
            }

            if (pMP->isBad())
                continue;

            if (e->chi2() > 7.815 || !e->isDepthPositive())
            {
                numStereoBadPoints++;
                vpStereoMPsBad.push_back(pMP);
            }
            else
            {
                numStereoOptPoints++;
                vpStereoMPsOpt.push_back(pMP);
            }
        }

        pKFi->SetPose(Tiw);
    }

    // 更新点的坐标
    for (MapPoint *pMPi : vpMPs)
    {
        if (pMPi->isBad())
            continue;

        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMPi->mnId + maxKFid + 1));
        pMPi->SetWorldPos(vPoint->estimate().cast<float>());
        pMPi->UpdateNormalAndDepth();
    }
}

/**
 * @brief  LoopClosing::MergeLocal() 融合地图时使用，优化当前帧没有参与融合的元素，本质图优化
 * 优化目标： 1. vpNonFixedKFs; 2.vpNonCorrectedMPs
 * @param pCurKF                 mpCurrentKF 融合时当前关键帧
 * @param vpFixedKFs             vpMergeConnectedKFs 融合地图中的关键帧，也就是上面函数中的 vpFixedKF
 * @param vpFixedCorrectedKFs    vpLocalCurrentWindowKFs 当前地图中经过矫正的关键帧，也就是Optimizer::LocalBundleAdjustment中优化过的vpAdjustKF
 * @param vpNonFixedKFs          vpCurrentMapKFs 当前地图中剩余的关键帧，待优化
 * @param vpNonCorrectedMPs      vpCurrentMapMPs 当前地图中剩余的MP点，待优化
 */
void Optimizer::OptimizeEssentialGraph(
    KeyFrame *pCurKF, vector<KeyFrame *> &vpFixedKFs, vector<KeyFrame *> &vpFixedCorrectedKFs,
    vector<KeyFrame *> &vpNonFixedKFs, vector<MapPoint *> &vpNonCorrectedMPs)
{
    // 1. 优化器构建
    g2o::SparseOptimizer optimizer;
    optimizer.setVerbose(false);
    g2o::BlockSolver_7_3::LinearSolverType *linearSolver =
        new g2o::LinearSolverEigen<g2o::BlockSolver_7_3::PoseMatrixType>();
    g2o::BlockSolver_7_3 *solver_ptr = new g2o::BlockSolver_7_3(linearSolver);
    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

    solver->setUserLambdaInit(1e-16);
    optimizer.setAlgorithm(solver);

    Map *pMap = pCurKF->GetMap();
    const unsigned int nMaxKFid = pMap->GetMaxKFid();

    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vScw(nMaxKFid + 1);          // 存放每一帧优化前的sim3
    vector<g2o::Sim3, Eigen::aligned_allocator<g2o::Sim3>> vCorrectedSwc(nMaxKFid + 1); // 存放每一帧优化后的sim3，调试输出用，没有实际作用
    vector<g2o::VertexSim3Expmap *> vpVertices(nMaxKFid + 1);                           // 存放节点，没用，还占地方

    vector<bool> vpGoodPose(nMaxKFid + 1);
    vector<bool> vpBadPose(nMaxKFid + 1);

    const int minFeat = 100; // pKFi->GetCovisiblesByWeight(minFeat);  essentialgraph 阈值就是共视大于100

    // 2. 确定固定关键帧的节点
    // vpMergeConnectedKFs 融合地图中的关键帧
    for (KeyFrame *pKFi : vpFixedKFs)
    {
        if (pKFi->isBad())
            continue;

        g2o::VertexSim3Expmap *VSim3 = new g2o::VertexSim3Expmap();

        const int nIDi = pKFi->mnId;

        Sophus::SE3d Tcw = pKFi->GetPose().cast<double>();
        g2o::Sim3 Siw(Tcw.unit_quaternion(), Tcw.translation(), 1.0);

        vCorrectedSwc[nIDi] = Siw.inverse();
        VSim3->setEstimate(Siw);

        VSim3->setFixed(true);

        VSim3->setId(nIDi);
        VSim3->setMarginalized(false);
        VSim3->_fix_scale = true;

        optimizer.addVertex(VSim3);

        vpVertices[nIDi] = VSim3;

        vpGoodPose[nIDi] = true;
        vpBadPose[nIDi] = false;
    }
    Verbose::PrintMess("Opt_Essential: vpFixedKFs loaded", Verbose::VERBOSITY_DEBUG);

    set<unsigned long> sIdKF;
    // vpLocalCurrentWindowKFs 当前地图中经过矫正的关键帧
    for (KeyFrame *pKFi : vpFixedCorrectedKFs)
    {
        if (pKFi->isBad())
            continue;

        g2o::VertexSim3Expmap *VSim3 = new g2o::VertexSim3Expmap();

        const int nIDi = pKFi->mnId;

        Sophus::SE3d Tcw = pKFi->GetPose().cast<double>();
        g2o::Sim3 Siw(Tcw.unit_quaternion(), Tcw.translation(), 1.0);

        vCorrectedSwc[nIDi] = Siw.inverse();
        VSim3->setEstimate(Siw);

        Sophus::SE3d Tcw_bef = pKFi->mTcwBefMerge.cast<double>();
        vScw[nIDi] = g2o::Sim3(Tcw_bef.unit_quaternion(), Tcw_bef.translation(), 1.0);

        VSim3->setFixed(true);

        VSim3->setId(nIDi);
        VSim3->setMarginalized(false);

        optimizer.addVertex(VSim3);

        vpVertices[nIDi] = VSim3;

        sIdKF.insert(nIDi);

        vpGoodPose[nIDi] = true;
        vpBadPose[nIDi] = true;
    }

    // 3. 确定待优化的关键帧节点
    for (KeyFrame *pKFi : vpNonFixedKFs)
    {
        if (pKFi->isBad())
            continue;

        const int nIDi = pKFi->mnId;

        if (sIdKF.count(nIDi)) // It has already added in the corrected merge KFs
            continue;

        g2o::VertexSim3Expmap *VSim3 = new g2o::VertexSim3Expmap();

        Sophus::SE3d Tcw = pKFi->GetPose().cast<double>();
        g2o::Sim3 Siw(Tcw.unit_quaternion(), Tcw.translation(), 1.0);

        vScw[nIDi] = Siw;
        VSim3->setEstimate(Siw);

        VSim3->setFixed(false);

        VSim3->setId(nIDi);
        VSim3->setMarginalized(false);

        optimizer.addVertex(VSim3);

        vpVertices[nIDi] = VSim3;

        sIdKF.insert(nIDi);

        vpGoodPose[nIDi] = false;
        vpBadPose[nIDi] = true;
    }

    vector<KeyFrame *> vpKFs;
    vpKFs.reserve(vpFixedKFs.size() + vpFixedCorrectedKFs.size() + vpNonFixedKFs.size());
    vpKFs.insert(vpKFs.end(), vpFixedKFs.begin(), vpFixedKFs.end());
    vpKFs.insert(vpKFs.end(), vpFixedCorrectedKFs.begin(), vpFixedCorrectedKFs.end());
    vpKFs.insert(vpKFs.end(), vpNonFixedKFs.begin(), vpNonFixedKFs.end());
    set<KeyFrame *> spKFs(vpKFs.begin(), vpKFs.end());

    const Eigen::Matrix<double, 7, 7> matLambda = Eigen::Matrix<double, 7, 7>::Identity();

    // 4. 遍历所有帧
    for (KeyFrame *pKFi : vpKFs)
    {
        int num_connections = 0; // 统计与pKFi连接的数量
        const int nIDi = pKFi->mnId;

        g2o::Sim3 correctedSwi;
        g2o::Sim3 Swi;

        if (vpGoodPose[nIDi])
            correctedSwi = vCorrectedSwc[nIDi];
        if (vpBadPose[nIDi])
            Swi = vScw[nIDi].inverse();

        KeyFrame *pParentKFi = pKFi->GetParent();

        // Spanning tree edge
        // 4.1 找到pKFi的父帧且在这批关键帧里面，添加与其关联的sim3边，相对约束
        if (pParentKFi && spKFs.find(pParentKFi) != spKFs.end())
        {
            int nIDj = pParentKFi->mnId;

            g2o::Sim3 Sjw;
            bool bHasRelation = false;

            if (vpGoodPose[nIDi] && vpGoodPose[nIDj])
            {
                Sjw = vCorrectedSwc[nIDj].inverse();
                bHasRelation = true;
            }
            else if (vpBadPose[nIDi] && vpBadPose[nIDj])
            {
                Sjw = vScw[nIDj];
                bHasRelation = true;
            }

            if (bHasRelation)
            {
                g2o::Sim3 Sji = Sjw * Swi;

                g2o::EdgeSim3 *e = new g2o::EdgeSim3();
                e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDj)));
                e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                e->setMeasurement(Sji);

                e->information() = matLambda;
                optimizer.addEdge(e);
                num_connections++;
            }
        }

        // Loop edges
        // 4.2 添加回环的边
        const set<KeyFrame *> sLoopEdges = pKFi->GetLoopEdges();
        for (set<KeyFrame *>::const_iterator sit = sLoopEdges.begin(), send = sLoopEdges.end(); sit != send; sit++)
        {
            KeyFrame *pLKF = *sit;
            if (spKFs.find(pLKF) != spKFs.end() && pLKF->mnId < pKFi->mnId)
            {
                g2o::Sim3 Slw;
                bool bHasRelation = false;

                if (vpGoodPose[nIDi] && vpGoodPose[pLKF->mnId])
                {
                    Slw = vCorrectedSwc[pLKF->mnId].inverse();
                    bHasRelation = true;
                }
                else if (vpBadPose[nIDi] && vpBadPose[pLKF->mnId])
                {
                    Slw = vScw[pLKF->mnId];
                    bHasRelation = true;
                }

                if (bHasRelation)
                {
                    g2o::Sim3 Sli = Slw * Swi;
                    g2o::EdgeSim3 *el = new g2o::EdgeSim3();
                    el->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pLKF->mnId)));
                    el->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                    el->setMeasurement(Sli);
                    el->information() = matLambda;
                    optimizer.addEdge(el);
                    num_connections++;
                }
            }
        }

        // Covisibility graph edges
        // 4.3 建立essentialgraph（共视边）
        const vector<KeyFrame *> vpConnectedKFs = pKFi->GetCovisiblesByWeight(minFeat);
        for (vector<KeyFrame *>::const_iterator vit = vpConnectedKFs.begin(); vit != vpConnectedKFs.end(); vit++)
        {
            KeyFrame *pKFn = *vit;
            // 1.这个帧存在且不是pKFi的父帧，防止重复添加
            // 2.pKFn不为pKFi的子帧
            // 3.pKFn不为回环帧
            // 4.pKFn要在这批关键帧里面
            // 防止重复添加
            if (pKFn && pKFn != pParentKFi && !pKFi->hasChild(pKFn) && !sLoopEdges.count(pKFn) && spKFs.find(pKFn) != spKFs.end())
            {
                if (!pKFn->isBad() && pKFn->mnId < pKFi->mnId)
                {

                    g2o::Sim3 Snw = vScw[pKFn->mnId];
                    bool bHasRelation = false;

                    if (vpGoodPose[nIDi] && vpGoodPose[pKFn->mnId])
                    {
                        Snw = vCorrectedSwc[pKFn->mnId].inverse();
                        bHasRelation = true;
                    }
                    else if (vpBadPose[nIDi] && vpBadPose[pKFn->mnId])
                    {
                        Snw = vScw[pKFn->mnId];
                        bHasRelation = true;
                    }

                    if (bHasRelation)
                    {
                        g2o::Sim3 Sni = Snw * Swi;

                        g2o::EdgeSim3 *en = new g2o::EdgeSim3();
                        en->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFn->mnId)));
                        en->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(nIDi)));
                        en->setMeasurement(Sni);
                        en->information() = matLambda;
                        optimizer.addEdge(en);
                        num_connections++;
                    }
                }
            }
        }

        if (num_connections == 0)
        {
            Verbose::PrintMess("Opt_Essential: KF " + to_string(pKFi->mnId) + " has 0 connections", Verbose::VERBOSITY_DEBUG);
        }
    }

    // Optimize!
    // 5. 开始优化
    optimizer.initializeOptimization();
    optimizer.optimize(20);

    unique_lock<mutex> lock(pMap->mMutexMapUpdate);

    // SE3 Pose Recovering. Sim3:[sR t;0 1] -> SE3:[R t/s;0 1]
    // 6. 取出结果
    for (KeyFrame *pKFi : vpNonFixedKFs)
    {
        if (pKFi->isBad())
            continue;

        const int nIDi = pKFi->mnId;

        g2o::VertexSim3Expmap *VSim3 = static_cast<g2o::VertexSim3Expmap *>(optimizer.vertex(nIDi));
        g2o::Sim3 CorrectedSiw = VSim3->estimate();
        vCorrectedSwc[nIDi] = CorrectedSiw.inverse();
        double s = CorrectedSiw.scale();
        Sophus::SE3d Tiw(CorrectedSiw.rotation(), CorrectedSiw.translation() / s);

        pKFi->mTcwBefMerge = pKFi->GetPose();
        pKFi->mTwcBefMerge = pKFi->GetPoseInverse();
        pKFi->SetPose(Tiw.cast<float>());
    }

    // Correct points. Transform to "non-optimized" reference keyframe pose and transform back with optimized pose
    for (MapPoint *pMPi : vpNonCorrectedMPs)
    {
        if (pMPi->isBad())
            continue;

        KeyFrame *pRefKF = pMPi->GetReferenceKeyFrame();
        while (pRefKF->isBad())
        {
            if (!pRefKF)
            {
                Verbose::PrintMess("MP " + to_string(pMPi->mnId) + " without a valid reference KF", Verbose::VERBOSITY_DEBUG);
                break;
            }

            pMPi->EraseObservation(pRefKF);
            pRefKF = pMPi->GetReferenceKeyFrame();
        }

        if (vpBadPose[pRefKF->mnId])
        {
            // 流程就是转到相机坐标系，在用新的rt转到世界
            Sophus::SE3f TNonCorrectedwr = pRefKF->mTwcBefMerge;
            Sophus::SE3f Twr = pRefKF->GetPoseInverse();

            Eigen::Vector3f eigCorrectedP3Dw = Twr * TNonCorrectedwr.inverse() * pMPi->GetWorldPos();
            pMPi->SetWorldPos(eigCorrectedP3Dw);

            pMPi->UpdateNormalAndDepth();
        }
        else
        {
            cout << "ERROR: MapPoint has a reference KF from another map" << endl;
        }
    }
}

/**
 * @brief 这里面进行visual inertial ba
 * LoopClosing::MergeLocal2 中用到
 * 优化目标：相关帧的位姿，速度，偏置，还有涉及点的坐标，可以理解为跨地图的局部窗口优化
 * @param[in] pCurrKF 当前关键帧
 * @param[in] pMergeKF 融合帧
 * @param[in] pbStopFlag 是否优化
 * @param[in] pMap 当前地图
 * @param[out] corrPoses 所有的Sim3 矫正
 */
void Optimizer::MergeInertialBA(
    KeyFrame *pCurrKF, KeyFrame *pMergeKF, bool *pbStopFlag, Map *pMap, LoopClosing::KeyFrameAndPose &corrPoses)
{
    const int Nd = 6;
    const unsigned long maxKFid = pCurrKF->mnId;
    // 1. 准备所有被优化的关键帧, 完全固定的帧
    // 被优化的帧, 当前帧和融合匹配帧加起来一共12个
    vector<KeyFrame *> vpOptimizableKFs;
    vpOptimizableKFs.reserve(2 * Nd);

    // For cov KFS, inertial parameters are not optimized
    // 共视帧, 只优化位姿，不优化速度与偏置
    const int maxCovKF = 30;
    vector<KeyFrame *> vpOptimizableCovKFs;
    vpOptimizableCovKFs.reserve(maxCovKF);

    // Add sliding window for current KF
    // 当前关键帧先加到容器中
    vpOptimizableKFs.push_back(pCurrKF);
    // 后面用这个变量避免重复
    pCurrKF->mnBALocalForKF = pCurrKF->mnId;
    // 添加5个与当前关键帧相连的时序上相连的关键帧进容器
    // 1.1 一直放一直放，一直放到没有上一个关键帧，这里面包含了pCurrKF最近的6帧，从晚到早排列，不过有可能存不满
    for (int i = 1; i < Nd; i++)
    {
        if (vpOptimizableKFs.back()->mPrevKF)
        {
            // 用mPrevKF访问时序上的上一个关键帧
            vpOptimizableKFs.push_back(vpOptimizableKFs.back()->mPrevKF);
            vpOptimizableKFs.back()->mnBALocalForKF = pCurrKF->mnId;
        }
        else
            break;
    }

    // 1.2 如果 vpOptimizableKFs 中最早的一帧前面还有，往不更新惯导参数的序列中添加
    // 否则把最后一个取出来放到不更新惯导参数的序列中
    if (vpOptimizableKFs.back()->mPrevKF)
    {
        vpOptimizableCovKFs.push_back(vpOptimizableKFs.back()->mPrevKF);
        vpOptimizableKFs.back()->mPrevKF->mnBALocalForKF = pCurrKF->mnId;
    }
    else
    {
        // 如果没找到时序相连的帧,把被优化的窗口缩减一个给到固定的变量
        vpOptimizableCovKFs.push_back(vpOptimizableKFs.back());
        vpOptimizableKFs.pop_back();
    }

    // Add temporal neighbours to merge KF (previous and next KFs)
    // 2.  同样, 对于融合帧也把它和时序上的几个邻居加到可优化的容器里
    // 2.1 把匹配的融合关键帧也放进来准备一起优化
    vpOptimizableKFs.push_back(pMergeKF);
    pMergeKF->mnBALocalForKF = pCurrKF->mnId;

    // Previous KFs
    // 把融合帧时序上的邻居添加到可优化的容器里, 因为融合帧左右都有时序上的邻居,所以这里先取一半 Nd/2
    // 2.2 再放进来3个pMergeKF之前的关键帧，有可能放不满
    for (int i = 1; i < (Nd / 2); i++)
    {
        if (vpOptimizableKFs.back()->mPrevKF)
        {
            vpOptimizableKFs.push_back(vpOptimizableKFs.back()->mPrevKF);
            vpOptimizableKFs.back()->mnBALocalForKF = pCurrKF->mnId;
        }
        else
            break;
    }

    // We fix just once the old map
    // 记录与融合帧窗口时序上紧邻的帧为完全固定的帧
    // 2.3 类似于上面的做法如果有前一个关键帧放入lFixedKeyFrames，否则从vpOptimizableKFs取出，注意这里防止重复添加的标识又多了一个变量
    // 记录完全固定的帧
    list<KeyFrame *> lFixedKeyFrames;
    if (vpOptimizableKFs.back()->mPrevKF)
    {
        lFixedKeyFrames.push_back(vpOptimizableKFs.back()->mPrevKF);
        vpOptimizableKFs.back()->mPrevKF->mnBAFixedForKF = pCurrKF->mnId;
    }
    // 如果没找到,则从窗口内拿出一帧给到完全固定的帧
    else
    {
        vpOptimizableKFs.back()->mnBALocalForKF = 0;
        vpOptimizableKFs.back()->mnBAFixedForKF = pCurrKF->mnId;
        lFixedKeyFrames.push_back(vpOptimizableKFs.back());
        vpOptimizableKFs.pop_back();
    }

    // Next KFs
    // 2.4 再添加一个pMergeKF的下一个关键帧
    if (pMergeKF->mNextKF)
    {
        vpOptimizableKFs.push_back(pMergeKF->mNextKF);
        vpOptimizableKFs.back()->mnBALocalForKF = pCurrKF->mnId;
    }
    // 2.5 数量不够时，添加最后一个的下一帧。继续添加直到达到2Nd个可优化关键帧,或没有新的可以添加了
    while (vpOptimizableKFs.size() < (2 * Nd))
    {
        if (vpOptimizableKFs.back()->mNextKF)
        {
            vpOptimizableKFs.push_back(vpOptimizableKFs.back()->mNextKF);
            vpOptimizableKFs.back()->mnBALocalForKF = pCurrKF->mnId;
        }
        else
            break;
    }

    int N = vpOptimizableKFs.size();

    // Optimizable points seen by optimizable keyframes
    // 3. 帧弄完了该弄点了，将优化的帧的点存入lLocalMapPoints (所有被可优化关键帧看到的点)

    // 添加用来优化的地图点
    list<MapPoint *> lLocalMapPoints;

    // 统计了在这些帧中点被观测的次数
    map<MapPoint *, int> mLocalObs;
    // 遍历所有可优化的关键帧
    for (int i = 0; i < N; i++)
    {
        vector<MapPoint *> vpMPs = vpOptimizableKFs[i]->GetMapPointMatches();
        // 遍历每个关键帧所有的地图点
        for (vector<MapPoint *>::iterator vit = vpMPs.begin(), vend = vpMPs.end(); vit != vend; vit++)
        {
            // Using mnBALocalForKF we avoid redundance here, one MP can not be added several times to lLocalMapPoints
            MapPoint *pMP = *vit;
            if (pMP)
                if (!pMP->isBad())
                    // 用这个变量记录是否重复添加
                    if (pMP->mnBALocalForKF != pCurrKF->mnId)
                    {
                        mLocalObs[pMP] = 1;
                        lLocalMapPoints.push_back(pMP);
                        pMP->mnBALocalForKF = pCurrKF->mnId;  // 防止重复添加
                    }
                    else
                    {
                        mLocalObs[pMP]++;
                    }
        }
    }

    std::vector<std::pair<MapPoint *, int>> pairs;
    pairs.reserve(mLocalObs.size());
    for (auto itr = mLocalObs.begin(); itr != mLocalObs.end(); ++itr)
        pairs.push_back(*itr);
    sort(pairs.begin(), pairs.end(), sortByVal);

    // 4. 把pCurrKF的共视帧加进来 只优化位姿，不优化速度与偏置
    // 固定所有观察到地图点,但没有被加到优化变量中的关键帧
    // Fixed Keyframes. Keyframes that see Local MapPoints but that are not Local Keyframes
    int i = 0;
    // 遍历所有的pair<地图点指针,观测次数>
    for (vector<pair<MapPoint *, int>>::iterator lit = pairs.begin(), lend = pairs.end(); lit != lend; lit++, i++)
    {
        map<KeyFrame *, tuple<int, int>> observations = lit->first->GetObservations();
        if (i >= maxCovKF)
            break;
        for (map<KeyFrame *, tuple<int, int>>::iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (pKFi->mnBALocalForKF != pCurrKF->mnId && pKFi->mnBAFixedForKF != pCurrKF->mnId) // If optimizable or already included...
            {
                pKFi->mnBALocalForKF = pCurrKF->mnId;
                if (!pKFi->isBad())
                {
                    vpOptimizableCovKFs.push_back(pKFi);
                    break;
                }
            }
        }
    }

    // 5. 构建优化器
    g2o::SparseOptimizer optimizer;
    g2o::BlockSolverX::LinearSolverType *linearSolver;
    linearSolver = new g2o::LinearSolverEigen<g2o::BlockSolverX::PoseMatrixType>();

    g2o::BlockSolverX *solver_ptr = new g2o::BlockSolverX(linearSolver);

    g2o::OptimizationAlgorithmLevenberg *solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);

    solver->setUserLambdaInit(1e3);

    optimizer.setAlgorithm(solver);
    optimizer.setVerbose(false);

    // Set Local KeyFrame vertices
    N = vpOptimizableKFs.size();
    // 5.1 设置所有的可优化关键帧顶点
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];

        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(false);
        // 位姿
        optimizer.addVertex(VP);

        if (pKFi->bImu)
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(false);
            optimizer.addVertex(VV);
            VertexGyroBias *VG = new VertexGyroBias(pKFi);
            VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
            VG->setFixed(false);
            optimizer.addVertex(VG);
            VertexAccBias *VA = new VertexAccBias(pKFi);
            VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
            VA->setFixed(false);
            optimizer.addVertex(VA);
        }
    }

    // Set Local cov keyframes vertices
    // 5.2 优化位姿，速度与偏置
    int Ncov = vpOptimizableCovKFs.size();
    for (int i = 0; i < Ncov; i++)
    {
        KeyFrame *pKFi = vpOptimizableCovKFs[i];

        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(false);
        optimizer.addVertex(VP);

        if (pKFi->bImu)
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(false);
            optimizer.addVertex(VV);
            VertexGyroBias *VG = new VertexGyroBias(pKFi);
            VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
            VG->setFixed(false);
            optimizer.addVertex(VG);
            VertexAccBias *VA = new VertexAccBias(pKFi);
            VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
            VA->setFixed(false);
            optimizer.addVertex(VA);
        }
    }

    // Set Fixed KeyFrame vertices
    // 5.3 设置所有完全固定的关键帧顶点
    for (list<KeyFrame *>::iterator lit = lFixedKeyFrames.begin(), lend = lFixedKeyFrames.end(); lit != lend; lit++)
    {
        KeyFrame *pKFi = *lit;
        VertexPose *VP = new VertexPose(pKFi);
        VP->setId(pKFi->mnId);
        VP->setFixed(true);
        optimizer.addVertex(VP);

        if (pKFi->bImu)
        {
            VertexVelocity *VV = new VertexVelocity(pKFi);
            VV->setId(maxKFid + 3 * (pKFi->mnId) + 1);
            VV->setFixed(true);
            optimizer.addVertex(VV);
            VertexGyroBias *VG = new VertexGyroBias(pKFi);
            VG->setId(maxKFid + 3 * (pKFi->mnId) + 2);
            VG->setFixed(true);
            optimizer.addVertex(VG);
            VertexAccBias *VA = new VertexAccBias(pKFi);
            VA->setId(maxKFid + 3 * (pKFi->mnId) + 3);
            VA->setFixed(true);
            optimizer.addVertex(VA);
        }
    }

    // 6 设置所有的inertial的边
    // Create intertial constraints
    // 预积分边
    vector<EdgeInertial *> vei(N, (EdgeInertial *)NULL);
    // 角速度bias边
    vector<EdgeGyroRW *> vegr(N, (EdgeGyroRW *)NULL);
    // 加速地bias边
    vector<EdgeAccRW *> vear(N, (EdgeAccRW *)NULL);

    // 6.1 第一阶段优化vpOptimizableKFs里面的帧，遍历所有可优化的关键帧
    for (int i = 0; i < N; i++)
    {
        // cout << "inserting inertial edge " << i << endl;
        KeyFrame *pKFi = vpOptimizableKFs[i];

        if (!pKFi->mPrevKF)
        {
            Verbose::PrintMess("NOT INERTIAL LINK TO PREVIOUS FRAME!!!!", Verbose::VERBOSITY_NORMAL);
            continue;
        }
        if (pKFi->bImu && pKFi->mPrevKF->bImu && pKFi->mpImuPreintegrated)
        {
            pKFi->mpImuPreintegrated->SetNewBias(pKFi->mPrevKF->GetImuBias());
            // 位姿
            g2o::HyperGraph::Vertex *VP1 = optimizer.vertex(pKFi->mPrevKF->mnId);
            // 速度
            g2o::HyperGraph::Vertex *VV1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 1);
            // 角速度bias
            g2o::HyperGraph::Vertex *VG1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 2);
            // 加速度bias
            g2o::HyperGraph::Vertex *VA1 = optimizer.vertex(maxKFid + 3 * (pKFi->mPrevKF->mnId) + 3);
            // 同上
            g2o::HyperGraph::Vertex *VP2 = optimizer.vertex(pKFi->mnId);
            g2o::HyperGraph::Vertex *VV2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1);
            g2o::HyperGraph::Vertex *VG2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2);
            g2o::HyperGraph::Vertex *VA2 = optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3);

            if (!VP1 || !VV1 || !VG1 || !VA1 || !VP2 || !VV2 || !VG2 || !VA2)
            {
                cerr << "Error " << VP1 << ", " << VV1 << ", " << VG1 << ", " << VA1 << ", " << VP2 << ", " << VV2 << ", " << VG2 << ", " << VA2 << endl;
                continue;
            }

            vei[i] = new EdgeInertial(pKFi->mpImuPreintegrated);
            // 设置顶点 2*Pose + 2*Velocity + 1 角速度bias + 1 加速度bias
            vei[i]->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP1));
            vei[i]->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV1));
            vei[i]->setVertex(2, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VG1));
            vei[i]->setVertex(3, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VA1));
            vei[i]->setVertex(4, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VP2));
            vei[i]->setVertex(5, dynamic_cast<g2o::OptimizableGraph::Vertex *>(VV2));

            // TODO Uncomment
            // 设置优化参数
            g2o::RobustKernelHuber *rki = new g2o::RobustKernelHuber;
            vei[i]->setRobustKernel(rki);
            rki->setDelta(sqrt(16.92));
            // 添加边
            optimizer.addEdge(vei[i]);

            // 角速度bias的边
            vegr[i] = new EdgeGyroRW();
            // 设置顶点两个角速度bias
            vegr[i]->setVertex(0, VG1);
            vegr[i]->setVertex(1, VG2);
            // 设置infomation matrix
            Eigen::Matrix3d InfoG = pKFi->mpImuPreintegrated->C.block<3, 3>(9, 9).cast<double>().inverse();
            vegr[i]->setInformation(InfoG);
            optimizer.addEdge(vegr[i]);
            // 设置加速度的边
            vear[i] = new EdgeAccRW();
            vear[i]->setVertex(0, VA1);
            vear[i]->setVertex(1, VA2);
            // 设置information matrix
            Eigen::Matrix3d InfoA = pKFi->mpImuPreintegrated->C.block<3, 3>(12, 12).cast<double>().inverse();
            vear[i]->setInformation(InfoA);
            optimizer.addEdge(vear[i]);
        }
        else
            Verbose::PrintMess("ERROR building inertial edge", Verbose::VERBOSITY_NORMAL);
    }

    Verbose::PrintMess("end inserting inertial edges", Verbose::VERBOSITY_NORMAL);

    // Set MapPoint vertices
    // 6.2 设置所有地图的顶点
    // 设置地图点顶点
    const int nExpectedSize = (N + Ncov + lFixedKeyFrames.size()) * lLocalMapPoints.size();

    // 对于双目单目设置不同
    // Mono
    vector<EdgeMono *> vpEdgesMono;
    vpEdgesMono.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFMono;
    vpEdgeKFMono.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeMono;
    vpMapPointEdgeMono.reserve(nExpectedSize);

    // Stereo
    vector<EdgeStereo *> vpEdgesStereo;
    vpEdgesStereo.reserve(nExpectedSize);

    vector<KeyFrame *> vpEdgeKFStereo;
    vpEdgeKFStereo.reserve(nExpectedSize);

    vector<MapPoint *> vpMapPointEdgeStereo;
    vpMapPointEdgeStereo.reserve(nExpectedSize);

    const float thHuberMono = sqrt(5.991);
    const float chi2Mono2 = 5.991;
    const float thHuberStereo = sqrt(7.815);
    const float chi2Stereo2 = 7.815;

    const unsigned long iniMPid = maxKFid * 5;

    // 遍历所有被可优化关键帧观测到的的地图点
    // 7. 添加重投影的边
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        MapPoint *pMP = *lit;
        if (!pMP)
            continue;

        g2o::VertexSBAPointXYZ *vPoint = new g2o::VertexSBAPointXYZ();
        vPoint->setEstimate(pMP->GetWorldPos().cast<double>());

        unsigned long id = pMP->mnId + iniMPid + 1;
        vPoint->setId(id);
        vPoint->setMarginalized(true);
        // 添加顶点
        optimizer.addVertex(vPoint);

        const map<KeyFrame *, tuple<int, int>> observations = pMP->GetObservations();

        // Create visual constraints
        // 添加重投影边
        for (map<KeyFrame *, tuple<int, int>>::const_iterator mit = observations.begin(), mend = observations.end(); mit != mend; mit++)
        {
            KeyFrame *pKFi = mit->first;

            if (!pKFi)
                continue;

            if ((pKFi->mnBALocalForKF != pCurrKF->mnId) && (pKFi->mnBAFixedForKF != pCurrKF->mnId))
                continue;

            if (pKFi->mnId > maxKFid)
            {
                continue;
            }

            if (optimizer.vertex(id) == NULL || optimizer.vertex(pKFi->mnId) == NULL)
                continue;

            if (!pKFi->isBad())
            {
                // 3D点的观测
                const cv::KeyPoint &kpUn = pKFi->mvKeysUn[get<0>(mit->second)];
                // 如果是单目观测
                if (pKFi->mvuRight[get<0>(mit->second)] < 0) // Monocular observation
                {
                    // 投影
                    Eigen::Matrix<double, 2, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y;

                    EdgeMono *e = new EdgeMono();
                    // 设置边的顶点
                    // 3D点
                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    // 关键帧位姿
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);
                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];
                    // 设置信息矩阵
                    e->setInformation(Eigen::Matrix2d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberMono);
                    // 添加边
                    optimizer.addEdge(e);
                    vpEdgesMono.push_back(e);
                    vpEdgeKFMono.push_back(pKFi);
                    vpMapPointEdgeMono.push_back(pMP);
                }
                // 双目
                else // stereo observation
                {
                    const float kp_ur = pKFi->mvuRight[get<0>(mit->second)];
                    Eigen::Matrix<double, 3, 1> obs;
                    obs << kpUn.pt.x, kpUn.pt.y, kp_ur;

                    EdgeStereo *e = new EdgeStereo();

                    e->setVertex(0, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(id)));
                    e->setVertex(1, dynamic_cast<g2o::OptimizableGraph::Vertex *>(optimizer.vertex(pKFi->mnId)));
                    e->setMeasurement(obs);
                    const float &invSigma2 = pKFi->mvInvLevelSigma2[kpUn.octave];
                    e->setInformation(Eigen::Matrix3d::Identity() * invSigma2);

                    g2o::RobustKernelHuber *rk = new g2o::RobustKernelHuber;
                    e->setRobustKernel(rk);
                    rk->setDelta(thHuberStereo);

                    optimizer.addEdge(e);
                    vpEdgesStereo.push_back(e);
                    vpEdgeKFStereo.push_back(pKFi);
                    vpMapPointEdgeStereo.push_back(pMP);
                }
            }
        }
    }
    // 如果要停止就直接返回
    if (pbStopFlag)
        if (*pbStopFlag)
            return;
    // 8. 开始优化
    optimizer.initializeOptimization();
    optimizer.optimize(8);

    // 更具优化结果挑选删除外点
    vector<pair<KeyFrame *, MapPoint *>> vToErase;
    vToErase.reserve(vpEdgesMono.size() + vpEdgesStereo.size());

    // Check inlier observations
    // Mono

    // 9. 处理外点
    // 更具卡方检测来记录要删除的外点
    for (size_t i = 0, iend = vpEdgesMono.size(); i < iend; i++)
    {
        EdgeMono *e = vpEdgesMono[i];
        MapPoint *pMP = vpMapPointEdgeMono[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > chi2Mono2)
        {
            KeyFrame *pKFi = vpEdgeKFMono[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    // Stereo
    for (size_t i = 0, iend = vpEdgesStereo.size(); i < iend; i++)
    {
        EdgeStereo *e = vpEdgesStereo[i];
        MapPoint *pMP = vpMapPointEdgeStereo[i];

        if (pMP->isBad())
            continue;

        if (e->chi2() > chi2Stereo2)
        {
            KeyFrame *pKFi = vpEdgeKFStereo[i];
            vToErase.push_back(make_pair(pKFi, pMP));
        }
    }

    // Get Map Mutex and erase outliers
    // 移除外点
    unique_lock<mutex> lock(pMap->mMutexMapUpdate);
    if (!vToErase.empty())
    {
        for (size_t i = 0; i < vToErase.size(); i++)
        {
            KeyFrame *pKFi = vToErase[i].first;
            MapPoint *pMPi = vToErase[i].second;
            pKFi->EraseMapPointMatch(pMPi);
            pMPi->EraseObservation(pKFi);
        }
    }

    // Recover optimized data
    // 10. 取数
    // 根据优化的结果,修改每个被优化的变量
    // Keyframes
    // 对于每个可优化关键帧
    for (int i = 0; i < N; i++)
    {
        KeyFrame *pKFi = vpOptimizableKFs[i];

        // 修改位姿
        VertexPose *VP = static_cast<VertexPose *>(optimizer.vertex(pKFi->mnId));
        Sophus::SE3f Tcw(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
        pKFi->SetPose(Tcw);

        // 在corrPoses记录每个关键帧融合后的位姿
        Sophus::SE3d Tiw = pKFi->GetPose().cast<double>();
        g2o::Sim3 g2oSiw(Tiw.unit_quaternion(), Tiw.translation(), 1.0);
        corrPoses[pKFi] = g2oSiw;

        if (pKFi->bImu)
        {
            // 速度
            VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1));
            // 修改速度
            pKFi->SetVelocity(VV->estimate().cast<float>());
            // 陀螺仪bias
            VertexGyroBias *VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2));
            // 加速度bias
            VertexAccBias *VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3));
            Vector6d b;
            // 修改bias
            b << VG->estimate(), VA->estimate();
            pKFi->SetNewBias(IMU::Bias(b[3], b[4], b[5], b[0], b[1], b[2]));
        }
    }
    // 遍历所有的共视帧
    for (int i = 0; i < Ncov; i++)
    {
        KeyFrame *pKFi = vpOptimizableCovKFs[i];

        VertexPose *VP = static_cast<VertexPose *>(optimizer.vertex(pKFi->mnId));
        Sophus::SE3f Tcw(VP->estimate().Rcw[0].cast<float>(), VP->estimate().tcw[0].cast<float>());
        pKFi->SetPose(Tcw);

        Sophus::SE3d Tiw = pKFi->GetPose().cast<double>();
        g2o::Sim3 g2oSiw(Tiw.unit_quaternion(), Tiw.translation(), 1.0);
        corrPoses[pKFi] = g2oSiw;

        if (pKFi->bImu)
        {
            VertexVelocity *VV = static_cast<VertexVelocity *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 1));
            pKFi->SetVelocity(VV->estimate().cast<float>());
            VertexGyroBias *VG = static_cast<VertexGyroBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 2));
            VertexAccBias *VA = static_cast<VertexAccBias *>(optimizer.vertex(maxKFid + 3 * (pKFi->mnId) + 3));
            Vector6d b;
            b << VG->estimate(), VA->estimate();
            pKFi->SetNewBias(IMU::Bias(b[3], b[4], b[5], b[0], b[1], b[2]));
        }
    }

    // Points
    // 对于所有的地图点
    for (list<MapPoint *>::iterator lit = lLocalMapPoints.begin(), lend = lLocalMapPoints.end(); lit != lend; lit++)
    {
        // 跟新位置和normal等信息
        MapPoint *pMP = *lit;
        g2o::VertexSBAPointXYZ *vPoint = static_cast<g2o::VertexSBAPointXYZ *>(optimizer.vertex(pMP->mnId + iniMPid + 1));
        pMP->SetWorldPos(vPoint->estimate().cast<float>());
        pMP->UpdateNormalAndDepth();
    }

    pMap->IncreaseChangeIndex();
}

} // namespace ORB_SLAM