《视觉SLAM十四讲精品总结》10.2: 基本的VO，特征提取和匹配

根据输入的图像，计算相机运动T和特征点位置3D坐标。

两两帧之间，只关心运动，不关心结构，把估计位姿串起来得到运动轨迹。

visual_odometry.h

#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_;   
    Map::Ptr    map_;    
    Frame::Ptr  ref_;      
    Frame::Ptr  curr_;      
    //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_;
    vector<cv::Point3f>     pts_3d_ref_;      
    vector<cv::KeyPoint>    keypoints_curr_;   
    Mat                     descriptors_curr_;  
    Mat                     descriptors_ref_;  
    vector<cv::DMatch>      feature_matches_;
    //位姿估计T、内点数、丢失数
    SE3 T_c_r_estimated_;  // the estimated pose of current frame 
    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
    int num_of_features_;   // number of features
    double scale_factor_;   // scale in image pyramid
    int level_pyramid_;     // number of pyramid levels
    float match_ratio_;      // ratio for selecting  good matches
    int max_num_lost_;      // max number of continuous lost times
    int min_inliers_;       // minimum inliers
    //关键帧标准，最小旋转和平移
    double key_frame_min_rot;   
    double key_frame_min_trans; 
    
public: 
	//构造函数析构函数
    VisualOdometry();
    ~VisualOdometry();
    //核心，添加新关键帧
    bool addFrame( Frame::Ptr frame );      // add a new frame 
    
protected:  
    //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

visual_odometry.cpp

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

namespace myslam
{
	//构造函数，相机位姿初始化VOstate、当前帧、参考帧、地图、丢失点、内联点
VisualOdometry::VisualOdometry() :
    state_ ( INITIALIZING ), ref_ ( nullptr ), curr_ ( nullptr ), map_ ( new Map ), num_lost_ ( 0 ), num_inliers_ ( 0 )
{
	//特征数量、尺度、好匹配比率、最大持续丢失时间、最小内点数、关键帧旋转平移，orb初始化
    num_of_features_    = Config::get<int> ( "number_of_features" );
    scale_factor_       = Config::get<double> ( "scale_factor" );
    level_pyramid_      = Config::get<int> ( "level_pyramid" );
    match_ratio_        = Config::get<float> ( "match_ratio" );
    max_num_lost_       = Config::get<float> ( "max_num_lost" );
    min_inliers_        = Config::get<int> ( "min_inliers" );
    key_frame_min_rot   = Config::get<double> ( "keyframe_rotation" );
    key_frame_min_trans = Config::get<double> ( "keyframe_translation" );
    orb_ = cv::ORB::create ( num_of_features_, scale_factor_, level_pyramid_ );
}
VisualOdometry::~VisualOdometry(){}
//核心！！！添加关键帧
bool VisualOdometry::addFrame ( Frame::Ptr frame )
{

    switch ( state_ )
    {
		//1. 初始化状态，第一帧
    case INITIALIZING:
    {
        state_ = OK;
		//当前帧和参考帧都是第一帧
        curr_ = ref_ = frame;
		//将此帧插入地图中
        map_->insertKeyFrame ( frame );
        //提取关键点和描述子
        extractKeyPoints();
        computeDescriptors();
        //参考帧特征点的3D坐标,补全depth数据
        setRef3DPoints();
        break;
    }
		//2. 匹配成功状态
    case OK:
    {
        curr_ = frame;
        extractKeyPoints();
        computeDescriptors();
		//特征匹配并计算相机位姿 T
        featureMatching();
        poseEstimationPnP();
		//位姿估计好
        if ( checkEstimatedPose() == true )
        {
			//当前位姿 Tcw = Tcr*Trw 
            curr_->T_c_w_ = T_c_r_estimated_ * ref_->T_c_w_; 
			//当前帧为参考帧
            ref_ = curr_;
			//参考帧特征点的3D坐标,补全depth数据
            setRef3DPoints();
            num_lost_ = 0;
			//检查是否为关键帧
            if ( checkKeyFrame() == true ) // is a key-frame
            {
                addKeyFrame();
            }
        }
		//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
        else
        {
            num_lost_++;
            if ( num_lost_ > max_num_lost_ )
            {
                state_ = LOST;
            }
            return false;
        }
        break;
    }
		//3. 匹配失败状态
    case LOST:
    {
        cout<<"vo has lost."<<endl;
        break;
    }
    }
    return true;
}
//提取特征点
void VisualOdometry::extractKeyPoints()
{
    orb_->detect ( curr_->color_, keypoints_curr_ );
}
//计算描述子
void VisualOdometry::computeDescriptors()
{
    orb_->compute ( curr_->color_, keypoints_curr_, descriptors_curr_ );
}
//特征匹配
void VisualOdometry::featureMatching()
{
    vector<cv::DMatch> matches;
    cv::BFMatcher matcher ( cv::NORM_HAMMING );
    matcher.match ( descriptors_ref_, descriptors_curr_, 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::DMatch& m1, const cv::DMatch& m2 )
    {
        return m1.distance < m2.distance;
    } )->distance;
	//根据距离筛选特征点
    feature_matches_.clear();
    for ( cv::DMatch& m : matches )
    {
        if ( m.distance < max<float> ( min_dis*match_ratio_, 30.0 ) )
        {
            feature_matches_.push_back(m);
        }
    }
    cout<<"good matches: "<<feature_matches_.size()<<endl;
}
//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
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(
                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));
        }
    }
}
//PnP估计相机位姿T
void VisualOdometry::poseEstimationPnP()
{
    vector<cv::Point3f> pts3d;
    vector<cv::Point2f> pts2d;
    //参考帧3D坐标和当前帧2D坐标（需要.pt像素坐标）
    for ( cv::DMatch m:feature_matches_ )
    {
        pts3d.push_back( pts_3d_ref_[m.queryIdx] );
        pts2d.push_back( keypoints_curr_[m.trainIdx].pt );
    }
    
    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;
    cv::solvePnPRansac( pts3d, pts2d, K, Mat(), rvec, tvec, false, 100, 4.0, 0.99, inliers );
   //内点数为内点行数
	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))
    );
}
//位姿检验模块，匹配点不能太少，运动不能太大
bool VisualOdometry::checkEstimatedPose()
{
    //匹配点太少，false
    if ( num_inliers_ < min_inliers_ )
    {
        cout<<"reject because inlier is too small: "<<num_inliers_<<endl;
        return false;
    }
    //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_ );
}

}