逻辑框架——要素图

逻辑框架——流程图——>初始化状态

逻辑框架——流程图——>正常运转状态

头文件——/slam14/project/0.2/include/myslam/visual_odometry.h

/*
 * <one line to give the program's name and a brief idea of what it does.>
 * Copyright (C) 2016  <copyright holder> <email>
 *
 * This program 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.
 *
 * This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 *
 */

#ifndef VISUALODOMETRY_H
#define VISUALODOMETRY_H

#include "myslam/common_include.h"
#include "myslam/map.h"

#include <opencv2/features2d/features2d.hpp>

namespace myslam 
{
class VisualOdometry
{
public:
    typedef shared_ptr<VisualOdometry> Ptr;

    //枚举表征VO状态，初始化、正常、丢失
    enum VOState {
        INITIALIZING=-1,
        OK=0,
        LOST
    };

    //VO状态、地图（关键帧和特征点）、参考帧、当前帧
    VOState     state_;     // current VO status
    Map::Ptr    map_;       // map with all frames and map points
    Frame::Ptr  ref_;       // reference frame 
    Frame::Ptr  curr_;      // current frame 
    
    //orb、当前帧特征点KeyPoint、当前帧描述子、参考帧3D坐标Point3f、参考帧描述子、匹配关系
    cv::Ptr<cv::ORB> orb_;  // orb detector and computer 
    // 当前帧的关键点，描述子存放位置
    vector<cv::KeyPoint>    keypoints_curr_;    // keypoints in current frame
    Mat                     descriptors_curr_;  // descriptor in current frame 
    // 引用帧关键点，描述子存放位置
    vector<cv::Point3f>     pts_3d_ref_;        // 3d points in reference frame 
    Mat                     descriptors_ref_;   // descriptor in reference frame 
    // 当前帧vs引用帧匹配结果存放位置
    vector<cv::DMatch>      feature_matches_;
    
    //位姿估计T、内点数(即为匹配出的关键点数量)、丢失数
    SE3 T_c_r_estimated_;  // the estimated pose of current frame 当前帧相对于参考帧的T
    int num_inliers_;        // number of inlier features in icp
    int num_lost_;           // number of lost times
    
    //参数，特征数量、尺度、好匹配比率、最大持续丢失时间、最小内点数
    // parameters 
    int num_of_features_;   // number of features
    double scale_factor_;   // scale in image pyramid=图像金字塔比例尺
    int level_pyramid_;     // number of pyramid levels=金字塔层数
    float match_ratio_;      // ratio for selecting  good matches = good matches 占比？
    int max_num_lost_;      // max number of continuous lost times=最大连续丢失帧数
    int min_inliers_;       // minimum inliers=
    

    //关键帧筛选标准：最小旋转量&最小平移量，只要T满足其中一个即可
    double key_frame_min_rot;   // minimal rotation of two key-frames
    double key_frame_min_trans; // minimal translation of two key-frames
    
public: // functions 
    VisualOdometry();
    ~VisualOdometry();
    //核心，添加新关键帧
    bool addFrame( Frame::Ptr frame );      // add a new frame 
    
protected:  
    // inner operation 
    // 内部处理函数，特征匹配
    void extractKeyPoints();
    void computeDescriptors(); 
    void featureMatching();
    void poseEstimationPnP(); 
    void setRef3DPoints();
    // 关键帧的功能函数
    void addKeyFrame();
    bool checkEstimatedPose(); 
    bool checkKeyFrame();
    
};
}

#endif // VISUALODOMETRY_H

源文件——/slam14/project/0.2/src/visual_odometry.cpp

/*
 * <one line to give the program's name and a brief idea of what it does.>
 * Copyright (C) 2016  <copyright holder> <email>
 *
 * This program 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.
 *
 * This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 *
 */

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <algorithm>
#include <boost/timer.hpp>

#include "myslam/config.h"
#include "myslam/visual_odometry.h"

namespace myslam
{

VisualOdometry::VisualOdometry() :
    state_ ( INITIALIZING ), ref_ ( nullptr ), curr_ ( nullptr ), map_ ( new Map ), num_lost_ ( 0 ), num_inliers_ ( 0 )
{
    num_of_features_    = Config::get<int> ( "number_of_features" );// 特征点数量
    scale_factor_       = Config::get<double> ( "scale_factor" ); // scale in image pyramid=图像金字塔比例尺
    level_pyramid_      = Config::get<int> ( "level_pyramid" );// number of pyramid levels=金字塔层数
    match_ratio_        = Config::get<float> ( "match_ratio" );// ？？？？？ratio for selecting  good matches = good matches 占比？
    max_num_lost_       = Config::get<float> ( "max_num_lost" );// max number of continuous lost times=最大连续丢失帧数
    min_inliers_        = Config::get<int> ( "min_inliers" );// 最少的内点数量？？？？？？？
    key_frame_min_rot   = Config::get<double> ( "keyframe_rotation" );// ？？？？？关键帧旋转？？？？？？
    key_frame_min_trans = Config::get<double> ( "keyframe_translation" );// # ？？？？关键帧变换？？？？？？
    orb_ = cv::ORB::create ( num_of_features_, scale_factor_, level_pyramid_ );
}

VisualOdometry::~VisualOdometry()
{

}




bool VisualOdometry::addFrame ( Frame::Ptr frame )
{
    switch ( state_ )
    {
        //1. 初始化状态，第一帧
    case INITIALIZING://状态:初始化
    {
        state_ = OK;
        curr_ = ref_ = frame;//当前帧和参考帧都是第一帧，实际上当前帧指向NULL也是行的。
        map_->insertKeyFrame ( frame );//将此帧插入地图中
        // extract features from first frame 
        //提取关键点和描述子
        extractKeyPoints();//初始帧（当前帧）提取特征点
        computeDescriptors();// 初始帧（当前帧）计算描述子
        //当前帧转化成引用帧(说实话，我觉得这个函数没有写好，语义不是很清楚，语义介于“引用的坐标2D-->3D”和“利用当前帧生成引用帧”)
        setRef3DPoints();// compute the 3d position of features in ref frame 
        break;
    }
    case OK:
    {
        curr_ = frame;
        extractKeyPoints();//当前帧提取特征点
        computeDescriptors();//当前帧计算描述子
        //特征匹配并计算相机位姿 T
        featureMatching();//当前帧vs引用帧，结果放入feature_matches_中
        poseEstimationPnP();//pnp方法进行位姿估计(说明：未利用当前帧的深度信息)
        //位姿估计好
        if ( checkEstimatedPose() == true ) // a good estimation
        {
            //当前位姿 Tcw = Tcr*Trw 
            curr_->T_c_w_ = T_c_r_estimated_ * ref_->T_c_w_;  // T_c_w = T_c_r*T_r_w 
            ref_ = curr_;//当前帧为参考帧
            setRef3DPoints();//参考帧特征点的3D坐标,补全depth数据
            num_lost_ = 0;//num_lost重置为0。
            //检查是否为关键帧
            if ( checkKeyFrame() == true ) // is a key-frame
            {
                addKeyFrame();
            }
        }
        else // bad estimation due to various reasons
        //位姿估计坏，丢失点计数+1，判断是否大于最大丢失数，是则VO状态切换为lost
        {
            num_lost_++;
            if ( num_lost_ > max_num_lost_ )
            {
                state_ = LOST;
            }
            return false;
        }
        break;
    }
    case LOST://3. 匹配失败状态
    {
        cout<<"vo has lost."<<endl;
        break;
    }
    }

    return true;
}




// 当前帧提取特征点
void VisualOdometry::extractKeyPoints()
{
    orb_->detect ( curr_->color_, keypoints_curr_ );//从curr_->color_中检测出keypoint放入keypoints_curr_
}

// 当前帧计算描述子
void VisualOdometry::computeDescriptors()
{
    orb_->compute ( curr_->color_, keypoints_curr_, descriptors_curr_ );//计算curr_->color_中检测出的keypoint的descriptor
}

// 特征点匹配
void VisualOdometry::featureMatching()
{
    // 关键点匹配：match desp_ref and desp_curr, use OpenCV's brute force match 
    vector<cv::DMatch> matches;//matches----->匹配结果存放地点(筛选前))
    cv::BFMatcher matcher ( cv::NORM_HAMMING );
    matcher.match ( descriptors_ref_, descriptors_curr_, matches );


    // 关键点筛选：select the best matches
    //找到matches数组中最小距离，赋值给min_dist
    float min_dis = std::min_element (
                        matches.begin(), matches.end(),
                        [] ( const cv::DMa tch& m1, const cv::DMatch& m2 )
    {
        return m1.distance < m2.distance;
    } )->distance;
    feature_matches_.clear();//清除上次匹配结果
    //根据距离筛选特征点
    for ( cv::DMatch& m : matches )
    {
        if ( m.distance < max<float> ( min_dis*match_ratio_, 30.0 ) )
        {
            feature_matches_.push_back(m);//符合条件的匹配放入feature_matches_
        }
    }
    cout<<"good matches: "<<feature_matches_.size()<<endl;
}

// 引用帧3D点设置
//pnp需要参考帧3D,当前帧2D，所以当前帧迭代为参考帧时，需要加上depth数据
// 每轮循环结束，都需要把当前帧变成参考帧，这时候就需要把当前帧的坐标的2D形式转化成3D形式
void VisualOdometry::setRef3DPoints()
{
    // select the features with depth measurements 
    pts_3d_ref_.clear();
    descriptors_ref_ = Mat();
    //遍历当前关键点数组遍历
    for ( size_t i=0; i<keypoints_curr_.size(); i++ )
    {
        //找到depth数据
        double d = ref_->findDepth(keypoints_curr_[i]);               
        if ( d > 0)
        {
            //像素坐标到相机坐标系3D坐标
            Vector3d p_cam = ref_->camera_->pixel2camera(//这一块，用curr_->camera也是可以的，因为相机是一样的
                Vector2d(keypoints_curr_[i].pt.x, keypoints_curr_[i].pt.y), d
            );
            //构造Point3f，3D坐标
            pts_3d_ref_.push_back( cv::Point3f( p_cam(0,0), p_cam(1,0), p_cam(2,0) ));
            //参考帧描述子，将当前帧描述子按行放进去。
            descriptors_ref_.push_back(descriptors_curr_.row(i));
        }
    }
}


// 使用pnp进行位置估计
// PnP估计相机位姿T
// 使用pts_3d_ref_ 、keypoints_curr_、feature_matches_、相机参数----->estimate出相机的T
void VisualOdometry::poseEstimationPnP()
{
    // construct the 3d 2d observations
    //参考帧3D坐标和当前帧2D坐标（需要.pt像素坐标）
    vector<cv::Point3f> pts3d;
    vector<cv::Point2f> pts2d;
    
    for ( cv::DMatch m:feature_matches_ )
    {
        pts3d.push_back( pts_3d_ref_[m.queryIdx] );//query=ref
        pts2d.push_back( keypoints_curr_[m.trainIdx].pt );//train=curr
    }
    
    //相机内参不变
    Mat K = ( cv::Mat_<double>(3,3)<<
        ref_->camera_->fx_, 0, ref_->camera_->cx_,
        0, ref_->camera_->fy_, ref_->camera_->cy_,
        0,0,1
    );


    //旋转、平移、内点数组
    Mat rvec, tvec, inliers;
    //核心函数
    cv::solvePnPRansac( pts3d, pts2d, K, Mat(), rvec, tvec, false, 100, 4.0, 0.99, inliers );
    // 猜测内点数，应该是匹配出的特征点中真正被位姿估计函数使用的那些点
    num_inliers_ = inliers.rows;
    cout<<"pnp inliers: "<<num_inliers_<<endl;

    //由旋转和平移构造出当前帧相对于参考帧的T
    T_c_r_estimated_ = SE3(
        SO3(rvec.at<double>(0,0), rvec.at<double>(1,0), rvec.at<double>(2,0)), 
        Vector3d( tvec.at<double>(0,0), tvec.at<double>(1,0), tvec.at<double>(2,0))
    );
}

// 位置估计检查，
//位姿检验模块，匹配点不能太少，运动不能太大
bool VisualOdometry::checkEstimatedPose()
{
    // check if the estimated pose is good
    //匹配点太少，false
    if ( num_inliers_ < min_inliers_ )
    {
        cout<<"reject because inlier is too small: "<<num_inliers_<<endl;
        return false;
    }
    // if the motion is too large, it is probably wrong
    //T的模太大，false
    Sophus::Vector6d d = T_c_r_estimated_.log();
    if ( d.norm() > 5.0 )
    {
        cout<<"reject because motion is too large: "<<d.norm()<<endl;
        return false;
    }
    return true;
}

// 检查关键帧，T中R或t比较大都是关键帧
bool VisualOdometry::checkKeyFrame()
{
    Sophus::Vector6d d = T_c_r_estimated_.log();
    Vector3d trans = d.head<3>();
    Vector3d rot = d.tail<3>();
    if ( rot.norm() >key_frame_min_rot || trans.norm() >key_frame_min_trans )
        return true;
    return false;
}

// 添加关键帧
void VisualOdometry::addKeyFrame()
{
    cout<<"adding a key-frame"<<endl;
    map_->insertKeyFrame ( curr_ );
}

}

非常糙的初步版本，后面有时间再优化！