[人工智能]视觉SLAM十四讲学习笔记——第十三讲 实践：主要源码注释

? ? ? ? 对于整个工程的分析可以参考上一篇博客

? ? ? ? ? ? ? ? ? ? ? ?https://blog.csdn.net/weixin_61294345/article/details/121679881

????????这里就分享一下两个重要的源码注释:

1.frontend.cpp

//
// Created by gaoxiang on 19-5-2.
//

#include <opencv2/opencv.hpp>

#include "myslam/algorithm.h"
#include "myslam/backend.h"
#include "myslam/config.h"
#include "myslam/feature.h"
#include "myslam/frontend.h"
#include "myslam/g2o_types.h"
#include "myslam/map.h"
#include "myslam/viewer.h"

namespace myslam {

Frontend::Frontend() {
    gftt_ =
        cv::GFTTDetector::create(Config::Get<int>("num_features"), 0.01, 20);
    num_features_init_ = Config::Get<int>("num_features_init");
    num_features_ = Config::Get<int>("num_features");
}

//添加一帧并计算结果
bool Frontend::AddFrame(myslam::Frame::Ptr frame) {
    current_frame_ = frame;

    switch (status_) {
        case FrontendStatus::INITING:
            ///初始化
            StereoInit();
            break;
        case FrontendStatus::TRACKING_GOOD:
        case FrontendStatus::TRACKING_BAD:
            ///跟踪
            Track();
            break;
        case FrontendStatus::LOST:
            Reset();
            break;
    }

    last_frame_ = current_frame_;
    return true;
}

///正常状态下的跟踪
bool Frontend::Track() {
    //用上一帧位姿获得当前位姿估计的初始值
    if (last_frame_) {
        current_frame_->SetPose(relative_motion_ * last_frame_->Pose());
    }
    //LK光流匹配上一帧
    int num_track_last = TrackLastFrame();
    //G2O图优化估计当前帧位姿
    tracking_inliers_ = EstimateCurrentPose();
    //跟踪点大于50个则状态较好
    if (tracking_inliers_ > num_features_tracking_) {
        // 修改标志位
        status_ = FrontendStatus::TRACKING_GOOD;
    }
    //小于50大于20跟踪效果差
    else if (tracking_inliers_ > num_features_tracking_bad_) {
        //修改标志位
        status_ = FrontendStatus::TRACKING_BAD;
    }
    //小于20 认为跟踪丢失
    else {
        // 丢失
        status_ = FrontendStatus::LOST;
    }
    //将当前帧设置为关键帧
    InsertKeyframe();
    relative_motion_ = current_frame_->Pose() * last_frame_->Pose().inverse();
    //在可视化地图中添加当前帧
    if (viewer_) viewer_->AddCurrentFrame(current_frame_);
    return true;
}

///插入关键帧
bool Frontend::InsertKeyframe() {
    // 关键点足够多，还不需要修改关键帧
    if (tracking_inliers_ >= num_features_needed_for_keyframe_) {
        return false;
    }
    // 将当前帧设置为关键帧
    current_frame_->SetKeyFrame();
    //向地图内增加一个关键帧
    map_->InsertKeyFrame(current_frame_);

    LOG(INFO) << "Set frame " << current_frame_->id_ << " as keyframe "
              << current_frame_->keyframe_id_;
    //将新增的关键帧内的地图点
    SetObservationsForKeyFrame();

    //出现关键帧后需要重新匹配
    //提取当前帧（左）关键点
    DetectFeatures();

    // 光流跟踪匹配右视角
    FindFeaturesInRight();
    // 三维重建
    TriangulateNewPoints();
    // 出发后端：更新地图
    backend_->UpdateMap();

    if (viewer_) viewer_->UpdateMap();

    return true;
}

///将关键帧的特征点点添加到地图
void Frontend::SetObservationsForKeyFrame() {
    for (auto &feat : current_frame_->features_left_) {
        auto mp = feat->map_point_.lock();
        if (mp) mp->AddObservation(feat);
    }
}

///三角化新的关键点
int Frontend::TriangulateNewPoints() {
    std::vector<SE3> poses{camera_left_->pose(), camera_right_->pose()};
    SE3 current_pose_Twc = current_frame_->Pose().inverse();
    int cnt_triangulated_pts = 0;
    for (size_t i = 0; i < current_frame_->features_left_.size(); ++i) {
        if (current_frame_->features_left_[i]->map_point_.expired() &&
            current_frame_->features_right_[i] != nullptr) {
            // 左图的特征点未关联地图点且存在右图匹配点，尝试三角化
            std::vector<Vec3> points{
                camera_left_->pixel2camera(
                    Vec2(current_frame_->features_left_[i]->position_.pt.x,
                         current_frame_->features_left_[i]->position_.pt.y)),
                camera_right_->pixel2camera(
                    Vec2(current_frame_->features_right_[i]->position_.pt.x,
                         current_frame_->features_right_[i]->position_.pt.y))};
            Vec3 pworld = Vec3::Zero();

            if (triangulation(poses, points, pworld) && pworld[2] > 0) {
                auto new_map_point = MapPoint::CreateNewMappoint();
                pworld = current_pose_Twc * pworld;
                new_map_point->SetPos(pworld);
                new_map_point->AddObservation(
                    current_frame_->features_left_[i]);
                new_map_point->AddObservation(
                    current_frame_->features_right_[i]);

                current_frame_->features_left_[i]->map_point_ = new_map_point;
                current_frame_->features_right_[i]->map_point_ = new_map_point;
                map_->InsertMapPoint(new_map_point);
                cnt_triangulated_pts++;
            }
        }
    }
    LOG(INFO) << "new landmarks: " << cnt_triangulated_pts;
    return cnt_triangulated_pts;
}

///估计当前帧位姿
int Frontend::EstimateCurrentPose() {
    // 图优化：单节点+多条一元边
    typedef g2o::BlockSolver_6_3 BlockSolverType;
    typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType>
        LinearSolverType;
    auto solver = new g2o::OptimizationAlgorithmLevenberg(
        g2o::make_unique<BlockSolverType>(
            g2o::make_unique<LinearSolverType>()));
    g2o::SparseOptimizer optimizer;
    optimizer.setAlgorithm(solver);

    // 节点:一个节点（位姿）
    VertexPose *vertex_pose = new VertexPose();
    vertex_pose->setId(0);
    vertex_pose->setEstimate(current_frame_->Pose());
    optimizer.addVertex(vertex_pose);

    // 相机内参
    Mat33 K = camera_left_->K();

    // 边：每对对应点都是
    int index = 1;
    std::vector<EdgeProjectionPoseOnly *> edges;
    std::vector<Feature::Ptr> features;
    for (size_t i = 0; i < current_frame_->features_left_.size(); ++i) {
        auto mp = current_frame_->features_left_[i]->map_point_.lock();
        if (mp) {
            features.push_back(current_frame_->features_left_[i]);
            EdgeProjectionPoseOnly *edge =
                new EdgeProjectionPoseOnly(mp->pos_, K);
            edge->setId(index);
            edge->setVertex(0, vertex_pose);
            edge->setMeasurement(
                toVec2(current_frame_->features_left_[i]->position_.pt));
            edge->setInformation(Eigen::Matrix2d::Identity());
            edge->setRobustKernel(new g2o::RobustKernelHuber);
            edges.push_back(edge);
            optimizer.addEdge(edge);
            index++;
        }
    }

    // 找到异常值
    const double chi2_th = 5.991;
    int cnt_outlier = 0;
    for (int iteration = 0; iteration < 4; ++iteration) {
        vertex_pose->setEstimate(current_frame_->Pose());
        optimizer.initializeOptimization();
        optimizer.optimize(10);
        cnt_outlier = 0;

        // 计算异常
        for (size_t i = 0; i < edges.size(); ++i) {
            auto e = edges[i];
            if (features[i]->is_outlier_) {
                e->computeError();
            }
            //阈值：5.991
            if (e->chi2() > chi2_th) {
                features[i]->is_outlier_ = true;//标记
                e->setLevel(1);
                cnt_outlier++;
            } else {
                features[i]->is_outlier_ = false;
                e->setLevel(0);
            };

            if (iteration == 2) {
                e->setRobustKernel(nullptr);
            }
        }
    }

    LOG(INFO) << "Outlier/Inlier in pose estimating: " << cnt_outlier << "/"
              << features.size() - cnt_outlier;
    // 记录位姿及异常的匹配结果
    current_frame_->SetPose(vertex_pose->estimate());

    LOG(INFO) << "Current Pose = \n" << current_frame_->Pose().matrix();

    for (auto &feat : features) {
        if (feat->is_outlier_) {
            feat->map_point_.reset();
            feat->is_outlier_ = false;  // maybe we can still use it in future
        }
    }
    //计算成功匹配的数量
    return features.size() - cnt_outlier;
}

//跟踪上一帧
int Frontend::TrackLastFrame() {
    // 使用LK光流匹配上一帧图像的点
    std::vector<cv::Point2f> kps_last, kps_current;
    for (auto &kp : last_frame_->features_left_) {
        if (kp->map_point_.lock()) {
            // use project point
            auto mp = kp->map_point_.lock();
            auto px =
                camera_left_->world2pixel(mp->pos_, current_frame_->Pose());
            kps_last.push_back(kp->position_.pt);
            kps_current.push_back(cv::Point2f(px[0], px[1]));
        } else {
            kps_last.push_back(kp->position_.pt);
            kps_current.push_back(kp->position_.pt);
        }
    }

    std::vector<uchar> status;
    Mat error;
    cv::calcOpticalFlowPyrLK(
        last_frame_->left_img_, current_frame_->left_img_, kps_last,
        kps_current, status, error, cv::Size(11, 11), 3,
        cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30,
                         0.01),
        cv::OPTFLOW_USE_INITIAL_FLOW);

    int num_good_pts = 0;

    for (size_t i = 0; i < status.size(); ++i) {
        if (status[i]) {
            cv::KeyPoint kp(kps_current[i], 7);
            Feature::Ptr feature(new Feature(current_frame_, kp));
            feature->map_point_ = last_frame_->features_left_[i]->map_point_;
            current_frame_->features_left_.push_back(feature);
            num_good_pts++;
        }
    }
    //返回成功匹配的点对数量
    LOG(INFO) << "Find " << num_good_pts << " in the last image.";
    return num_good_pts;
}


///初始化
bool Frontend::StereoInit() {
    //提取初始图像左视角关键点数量
    int num_features_left = DetectFeatures();
    //LK光流跟踪后结果
    int num_coor_features = FindFeaturesInRight();
    //匹配结果少于100,初始化失败
    if (num_coor_features < num_features_init_) {
        return false;
    }

    bool build_map_success = BuildInitMap();
    if (build_map_success) {
        status_ = FrontendStatus::TRACKING_GOOD;
        if (viewer_) {
            //添加当前帧
            viewer_->AddCurrentFrame(current_frame_);
            //更新地图
            viewer_->UpdateMap();
        }
        return true;
    }
    return false;
}
/**
*提取关键点（左视角图像）
*return 关键点数量
**/
int Frontend::DetectFeatures() {
    cv::Mat mask(current_frame_->left_img_.size(), CV_8UC1, 255);
    for (auto &feat : current_frame_->features_left_) {
        //绘制边框
        cv::rectangle(mask, feat->position_.pt - cv::Point2f(10, 10),
                      feat->position_.pt + cv::Point2f(10, 10), 0, CV_FILLED);
    }
    //GFTT角点
    std::vector<cv::KeyPoint> keypoints;
    gftt_->detect(current_frame_->left_img_, keypoints, mask);
    int cnt_detected = 0;
    for (auto &kp : keypoints) {
        //作为一个关键点Feature类型存储
        current_frame_->features_left_.push_back(
            Feature::Ptr(new Feature(current_frame_, kp)));
        cnt_detected++;
    }
    //返回关键点数量
    LOG(INFO) << "Detect " << cnt_detected << " new features";
    return cnt_detected;
}
/**
*当前帧的右侧图像中查找相应的特征(右视角)
*return 关键点数量
**/
int Frontend::FindFeaturesInRight() {
    // 用LK光流估计右侧图像中的点
    std::vector<cv::Point2f> kps_left, kps_right;
    for (auto &kp : current_frame_->features_left_) {
        kps_left.push_back(kp->position_.pt);
        //检查是否上锁
        auto mp = kp->map_point_.lock();
        if (mp) {
            // 使用投影点作为初始估计值
            auto px =
                camera_right_->world2pixel(mp->pos_, current_frame_->Pose());
            kps_right.push_back(cv::Point2f(px[0], px[1]));
        } else {
            // 使用统一坐标
            kps_right.push_back(kp->position_.pt);
        }
    }

    std::vector<uchar> status;
    Mat error;
    //opencv自带的光流跟踪函数
    cv::calcOpticalFlowPyrLK(
        current_frame_->left_img_, current_frame_->right_img_, kps_left,
        kps_right, status, error, cv::Size(11, 11), 3,
        cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30,
                         0.01),
        cv::OPTFLOW_USE_INITIAL_FLOW);

    int num_good_pts = 0;
    //统计跟踪结果，并存储
    for (size_t i = 0; i < status.size(); ++i) {
        if (status[i]) {
            cv::KeyPoint kp(kps_right[i], 7);
            Feature::Ptr feat(new Feature(current_frame_, kp));
            feat->is_on_left_image_ = false;
            current_frame_->features_right_.push_back(feat);
            num_good_pts++;
        } else {
            current_frame_->features_right_.push_back(nullptr);
        }
    }
    LOG(INFO) << "Find " << num_good_pts << " in the right image.";
    return num_good_pts;
}
/**
* 使用单个图像构建初始地图
* @return true if succeed
*/
bool Frontend::BuildInitMap() {
    std::vector<SE3> poses{camera_left_->pose(), camera_right_->pose()};
    size_t cnt_init_landmarks = 0;
    for (size_t i = 0; i < current_frame_->features_left_.size(); ++i) {
        if (current_frame_->features_right_[i] == nullptr) continue;
        // 利用三角测量创建路标点
        std::vector<Vec3> points{
            camera_left_->pixel2camera(
                Vec2(current_frame_->features_left_[i]->position_.pt.x,
                     current_frame_->features_left_[i]->position_.pt.y)),
            camera_right_->pixel2camera(
                Vec2(current_frame_->features_right_[i]->position_.pt.x,
                     current_frame_->features_right_[i]->position_.pt.y))};
        Vec3 pworld = Vec3::Zero();
        //三角化
        if (triangulation(poses, points, pworld) && pworld[2] > 0) {
            //创建地图存储数据
            auto new_map_point = MapPoint::CreateNewMappoint();
            new_map_point->SetPos(pworld);
            new_map_point->AddObservation(current_frame_->features_left_[i]);
            new_map_point->AddObservation(current_frame_->features_right_[i]);
            current_frame_->features_left_[i]->map_point_ = new_map_point;
            current_frame_->features_right_[i]->map_point_ = new_map_point;
            cnt_init_landmarks++;
            map_->InsertMapPoint(new_map_point);
        }
    }
    current_frame_->SetKeyFrame();
    map_->InsertKeyFrame(current_frame_);
    backend_->UpdateMap();

    LOG(INFO) << "Initial map created with " << cnt_init_landmarks
              << " map points";

    return true;
}

///跟踪失败：重置
bool Frontend::Reset() {
    LOG(INFO) << "Reset is not implemented. ";
    return true;
}

}  // namespace myslam

2.backend.cpp:

//
// Created by gaoxiang on 19-5-2.
//

#include "myslam/backend.h"
#include "myslam/algorithm.h"
#include "myslam/feature.h"
#include "myslam/g2o_types.h"
#include "myslam/map.h"
#include "myslam/mappoint.h"

namespace myslam {

/// 构造函数 开启线程
Backend::Backend() {
    backend_running_.store(true);
    backend_thread_ = std::thread(std::bind(&Backend::BackendLoop, this));
}

void Backend::UpdateMap() {
    std::unique_lock<std::mutex> lock(data_mutex_);
    map_update_.notify_one();///唤醒阻塞的线程
}

///关闭后端
void Backend::Stop() {
    backend_running_.store(false);
    map_update_.notify_one();
    backend_thread_.join();
}

void Backend::BackendLoop() {
    while (backend_running_.load()) {
        std::unique_lock<std::mutex> lock(data_mutex_);
        map_update_.wait(lock);

        /// 后端仅优化激活的Frames和Landmarks
        Map::KeyframesType active_kfs = map_->GetActiveKeyFrames();
        Map::LandmarksType active_landmarks = map_->GetActiveMapPoints();
        Optimize(active_kfs, active_landmarks);
    }
}

void Backend::Optimize(Map::KeyframesType &keyframes,
                       Map::LandmarksType &landmarks) {
    // 初始设置
    typedef g2o::BlockSolver_6_3 BlockSolverType;
    typedef g2o::LinearSolverCSparse<BlockSolverType::PoseMatrixType>
        LinearSolverType;
    auto solver = new g2o::OptimizationAlgorithmLevenberg(
        g2o::make_unique<BlockSolverType>(
            g2o::make_unique<LinearSolverType>()));
    g2o::SparseOptimizer optimizer;
    optimizer.setAlgorithm(solver);

    // 添加pose顶点，使用Keyframe id
    std::map<unsigned long, VertexPose *> vertices;
    unsigned long max_kf_id = 0;
    // 共有7个关键帧
    for (auto &keyframe : keyframes) {
        auto kf = keyframe.second;
        VertexPose *vertex_pose = new VertexPose();  // camera vertex_pose
        vertex_pose->setId(kf->keyframe_id_);
        vertex_pose->setEstimate(kf->Pose());
        optimizer.addVertex(vertex_pose);
        if (kf->keyframe_id_ > max_kf_id) {
            max_kf_id = kf->keyframe_id_;
        }

        vertices.insert({kf->keyframe_id_, vertex_pose});
    }

    // 添加路标顶点，使用路标id索引
    std::map<unsigned long, VertexXYZ *> vertices_landmarks;

    // K 和左右外参
    Mat33 K = cam_left_->K();
    SE3 left_ext = cam_left_->pose();
    SE3 right_ext = cam_right_->pose();

    // edges
    int index = 1;
    double chi2_th = 5.991;  // robust kernel 阈值
    std::map<EdgeProjection *, Feature::Ptr> edges_and_features;
    //遍历没一个路标点
    for (auto &landmark : landmarks) {
        if (landmark.second->is_outlier_) continue;//去除异常点
        unsigned long landmark_id = landmark.second->id_;
        auto observations = landmark.second->GetObs();
        //遍历每个路标点的观测帧
        for (auto &obs : observations) {
            if (obs.lock() == nullptr) continue;
            auto feat = obs.lock();
            if (feat->is_outlier_ || feat->frame_.lock() == nullptr) continue;

            auto frame = feat->frame_.lock();
            EdgeProjection *edge = nullptr;
            //构造带有地图和位姿的二元边
            if (feat->is_on_left_image_) {
                edge = new EdgeProjection(K, left_ext);
            } else {
                edge = new EdgeProjection(K, right_ext);
            }

            // 如果landmark还没有被加入优化，则新加一个顶点
            if (vertices_landmarks.find(landmark_id) ==
                vertices_landmarks.end()) {
                VertexXYZ *v = new VertexXYZ;
                v->setEstimate(landmark.second->Pos());
                v->setId(landmark_id + max_kf_id + 1);
                v->setMarginalized(true);
                vertices_landmarks.insert({landmark_id, v});
                optimizer.addVertex(v);
            }
            //设置边：每个路标点和其任意一个观测帧的位姿都要建立一条边
            edge->setId(index);
            edge->setVertex(0, vertices.at(frame->keyframe_id_));    // pose
            edge->setVertex(1, vertices_landmarks.at(landmark_id));  // landmark
            edge->setMeasurement(toVec2(feat->position_.pt));
            edge->setInformation(Mat22::Identity());
            auto rk = new g2o::RobustKernelHuber();
            rk->setDelta(chi2_th);
            edge->setRobustKernel(rk);
            edges_and_features.insert({edge, feat});

            optimizer.addEdge(edge);

            index++;
        }
    }

    // 优化后去除异常数据
    optimizer.initializeOptimization();
    optimizer.optimize(10);//迭代10次

    int cnt_outlier = 0, cnt_inlier = 0;
    int iteration = 0;
    while (iteration < 5) {
        cnt_outlier = 0;
        cnt_inlier = 0;
        // 根据阈值判断异常值
        for (auto &ef : edges_and_features) {
            if (ef.first->chi2() > chi2_th) {
                cnt_outlier++;
            } else {
                cnt_inlier++;
            }
        }
        //计算正常点的比例
        double inlier_ratio = cnt_inlier / double(cnt_inlier + cnt_outlier);
        //调整筛选阈值，直到合格
        if (inlier_ratio > 0.5) {
            break;
        } else {
            chi2_th *= 2;
            iteration++;
        }
    }
    //记录结果是否异常
    for (auto &ef : edges_and_features) {
        if (ef.first->chi2() > chi2_th) {
            ef.second->is_outlier_ = true;
            // 移除异常结果
            ef.second->map_point_.lock()->RemoveObservation(ef.second);
        } else {
            ef.second->is_outlier_ = false;
        }
    }

    LOG(INFO) << "Outlier/Inlier in optimization: " << cnt_outlier << "/"
              << cnt_inlier;

    // 存储相机位姿及路标点的位置
    for (auto &v : vertices) {
        keyframes.at(v.first)->SetPose(v.second->estimate());
    }
    for (auto &v : vertices_landmarks) {
        landmarks.at(v.first)->SetPos(v.second->estimate());
    }
}

}  // namespace myslam