关于高博士在《视觉SLAM十四讲》中ch7部分ORB检测算法代码的勘误

/*源代码的运行过程发现出不来结果，无独有偶，发现网上也有很多大神出现过这种错误，改了之后是可以运行的，因此修改了一下高博士的部分代码，贴出来分享一下！*/

文件名：pose_estimation_3d2d.cpp   此处的错误是在ptr指针（线性方程求解器和矩阵块求解器）

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

void find_feature_matches (
    const Mat& img_1, const Mat& img_2,
    std::vector& keypoints_1,
    std::vector& keypoints_2,
    std::vector< DMatch >& matches );

// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );

void bundleAdjustment (
    const vector points_3d,
    const vector points_2d,
    const Mat& K,
    Mat& R, Mat& t
);

int main ( int argc, char** argv )
{
    if ( argc != 5 )
    {
        cout<<"usage: pose_estimation_3d2d img1 img2 depth1 depth2"< keypoints_1, keypoints_2;
    vector matches;
    find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
    cout<<"一共找到了"< ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    vector pts_3d;
    vector pts_2d;
    for ( DMatch m:matches )
    {
        ushort d = d1.ptr (int ( keypoints_1[m.queryIdx].pt.y )) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
        if ( d == 0 )   // bad depth
            continue;
        float dd = d/5000.0;
        Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
        pts_3d.push_back ( Point3f ( p1.x*dd, p1.y*dd, dd ) );
        pts_2d.push_back ( keypoints_2[m.trainIdx].pt );
    }

    cout<<"3d-2d pairs: "<& keypoints_1,
                            std::vector& keypoints_2,
                            std::vector< DMatch >& matches )
{
    //-- 初始化
    Mat descriptors_1, descriptors_2;
    // used in OpenCV3
    Ptr detector = ORB::create();
    Ptr descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr detector = FeatureDetector::create ( "ORB" );
    // Ptr descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr matcher  = DescriptorMatcher::create ( "BruteForce-Hamming" );
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector match;
    // BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, match );

    //-- 第四步:匹配点对筛选
    double min_dist=10000, max_dist=0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = match[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            matches.push_back ( match[i] );
        }
    }
}

Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
    return Point2d
           (
               ( p.x - K.at ( 0,2 ) ) / K.at ( 0,0 ),
               ( p.y - K.at ( 1,2 ) ) / K.at ( 1,1 )
           );
}

void bundleAdjustment (
    const vector< Point3f > points_3d,
    const vector< Point2f > points_2d,
    const Mat& K,
    Mat& R, Mat& t )
{
    // 初始化g2o
    typedef g2o::BlockSolver< g2o::BlockSolverTraits<6,3> > Block;  // pose 维度为 6, landmark 维度为 3
    //Block::LinearSolverType* linearSolver = new g2o::LinearSolverCSparse(); // 线性方程求解器
    std::unique_ptr linearSolver ( new g2o::LinearSolverCSparse());
    //Block* solver_ptr = new Block ( linearSolver );     // 矩阵块求解器
    std::unique_ptr solver_ptr ( new Block ( std::move(linearSolver)));     // 矩阵块求解器
        g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( std::move(solver_ptr));

    g2o::SparseOptimizer optimizer;

    optimizer.setAlgorithm ( solver );

    // vertex
    g2o::VertexSE3Expmap* pose = new g2o::VertexSE3Expmap(); // camera pose
    Eigen::Matrix3d R_mat;
    R_mat <<
          R.at ( 0,0 ), R.at ( 0,1 ), R.at ( 0,2 ),
               R.at ( 1,0 ), R.at ( 1,1 ), R.at ( 1,2 ),
               R.at ( 2,0 ), R.at ( 2,1 ), R.at ( 2,2 );
    pose->setId ( 0 );
    pose->setEstimate ( g2o::SE3Quat (
                            R_mat,
                            Eigen::Vector3d ( t.at ( 0,0 ), t.at ( 1,0 ), t.at ( 2,0 ) )
                        ) );
    optimizer.addVertex ( pose );

    int index = 1;
    for ( const Point3f p:points_3d )   // landmarks
    {
        g2o::VertexSBAPointXYZ* point = new g2o::VertexSBAPointXYZ();
        point->setId ( index++ );
        point->setEstimate ( Eigen::Vector3d ( p.x, p.y, p.z ) );
        point->setMarginalized ( true ); // g2o 中必须设置 marg 参见第十讲内容
        optimizer.addVertex ( point );
    }

    // parameter: camera intrinsics
    g2o::CameraParameters* camera = new g2o::CameraParameters (
        K.at ( 0,0 ), Eigen::Vector2d ( K.at ( 0,2 ), K.at ( 1,2 ) ), 0
    );
    camera->setId ( 0 );
    optimizer.addParameter ( camera );

    // edges
    index = 1;
    for ( const Point2f p:points_2d )
    {
        g2o::EdgeProjectXYZ2UV* edge = new g2o::EdgeProjectXYZ2UV();
        edge->setId ( index );
        edge->setVertex ( 0, dynamic_cast ( optimizer.vertex ( index ) ) );
        edge->setVertex ( 1, pose );
        edge->setMeasurement ( Eigen::Vector2d ( p.x, p.y ) );
        edge->setParameterId ( 0,0 );
        edge->setInformation ( Eigen::Matrix2d::Identity() );
        optimizer.addEdge ( edge );
        index++;
    }

    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
    optimizer.setVerbose ( true );
    optimizer.initializeOptimization();
    optimizer.optimize ( 100 );
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration time_used = chrono::duration_cast> ( t2-t1 );
    cout<<"optimization costs time: "<#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace std;
using namespace cv;

void find_feature_matches (
    const Mat& img_1, const Mat& img_2,
    std::vector& keypoints_1,
    std::vector& keypoints_2,
    std::vector< DMatch >& matches );

// 像素坐标转相机归一化坐标
Point2d pixel2cam ( const Point2d& p, const Mat& K );

void pose_estimation_3d3d (
    const vector& pts1,
    const vector& pts2,
    Mat& R, Mat& t
);

void bundleAdjustment(
    const vector& points_3d,
    const vector& points_2d,
    Mat& R, Mat& t
);

// g2o edge
class EdgeProjectXYZRGBDPoseOnly : public g2o::BaseUnaryEdge<3, Eigen::Vector3d, g2o::VertexSE3Expmap>
{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    EdgeProjectXYZRGBDPoseOnly( const Eigen::Vector3d& point ) : _point(point) {}

    virtual void computeError()
    {
        const g2o::VertexSE3Expmap* pose = static_cast ( _vertices[0] );
        // measurement is p, point is p'
        _error = _measurement - pose->estimate().map( _point );
    }

    virtual void linearizeOplus()
    {
        g2o::VertexSE3Expmap* pose = static_cast(_vertices[0]);
        g2o::SE3Quat T(pose->estimate());
        Eigen::Vector3d xyz_trans = T.map(_point);
        double x = xyz_trans[0];
        double y = xyz_trans[1];
        double z = xyz_trans[2];

        _jacobianOplusXi(0,0) = 0;
        _jacobianOplusXi(0,1) = -z;
        _jacobianOplusXi(0,2) = y;
        _jacobianOplusXi(0,3) = -1;
        _jacobianOplusXi(0,4) = 0;
        _jacobianOplusXi(0,5) = 0;

        _jacobianOplusXi(1,0) = z;
        _jacobianOplusXi(1,1) = 0;
        _jacobianOplusXi(1,2) = -x;
        _jacobianOplusXi(1,3) = 0;
        _jacobianOplusXi(1,4) = -1;
        _jacobianOplusXi(1,5) = 0;

        _jacobianOplusXi(2,0) = -y;
        _jacobianOplusXi(2,1) = x;
        _jacobianOplusXi(2,2) = 0;
        _jacobianOplusXi(2,3) = 0;
        _jacobianOplusXi(2,4) = 0;
        _jacobianOplusXi(2,5) = -1;
    }

    bool read ( istream& in ) {}
    bool write ( ostream& out ) const {}
protected:
    Eigen::Vector3d _point;
};

int main ( int argc, char** argv )
{
    if ( argc != 5 )
    {
        cout<<"usage: pose_estimation_3d3d img1 img2 depth1 depth2"< keypoints_1, keypoints_2;
    vector matches;
    find_feature_matches ( img_1, img_2, keypoints_1, keypoints_2, matches );
    cout<<"一共找到了"< ( 3,3 ) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1 );
    vector pts1, pts2;

    for ( DMatch m:matches )
    {
        ushort d1 = depth1.ptr ( int ( keypoints_1[m.queryIdx].pt.y ) ) [ int ( keypoints_1[m.queryIdx].pt.x ) ];
        ushort d2 = depth2.ptr ( int ( keypoints_2[m.trainIdx].pt.y ) ) [ int ( keypoints_2[m.trainIdx].pt.x ) ];
        if ( d1==0 || d2==0 )   // bad depth
            continue;
        Point2d p1 = pixel2cam ( keypoints_1[m.queryIdx].pt, K );
        Point2d p2 = pixel2cam ( keypoints_2[m.trainIdx].pt, K );
        float dd1 = float ( d1 ) /5000.0;
        float dd2 = float ( d2 ) /5000.0;
        pts1.push_back ( Point3f ( p1.x*dd1, p1.y*dd1, dd1 ) );
        pts2.push_back ( Point3f ( p2.x*dd2, p2.y*dd2, dd2 ) );
    }

    cout<<"3d-3d pairs: "< descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr matcher  = DescriptorMatcher::create("BruteForce-Hamming");
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect ( img_1,keypoints_1 );
    detector->detect ( img_2,keypoints_2 );

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute ( img_1, keypoints_1, descriptors_1 );
    descriptor->compute ( img_2, keypoints_2, descriptors_2 );

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector match;
   // BFMatcher matcher ( NORM_HAMMING );
    matcher->match ( descriptors_1, descriptors_2, match );

    //-- 第四步:匹配点对筛选
    double min_dist=10000, max_dist=0;

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        double dist = match[i].distance;
        if ( dist < min_dist ) min_dist = dist;
        if ( dist > max_dist ) max_dist = dist;
    }

    printf ( "-- Max dist : %f \n", max_dist );
    printf ( "-- Min dist : %f \n", min_dist );

    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.
    for ( int i = 0; i < descriptors_1.rows; i++ )
    {
        if ( match[i].distance <= max ( 2*min_dist, 30.0 ) )
        {
            matches.push_back ( match[i] );
        }
    }
}

Point2d pixel2cam ( const Point2d& p, const Mat& K )
{
    return Point2d
           (
               ( p.x - K.at ( 0,2 ) ) / K.at ( 0,0 ),
               ( p.y - K.at ( 1,2 ) ) / K.at ( 1,1 )
           );
}

void pose_estimation_3d3d (
    const vector& pts1,
    const vector& pts2,
    Mat& R, Mat& t
)
{
    Point3f p1, p2;     // center of mass
    int N = pts1.size();
    for ( int i=0; i     q1 ( N ), q2 ( N ); // remove the center
    for ( int i=0; i#include 
#include 
#include 
#include 
using namespace std;
using namespace cv;

int main(int argc, char** argv)
{

    if(argc!=3)//判断命令行输入对错
    {
        cout<<"usage: feature_extraction img1 img2"< keypoints_1, keypoints_2;

    //创建两张图像的描述子，类型是Mat类型
    Mat descriptors_1, descriptors_2;

    //创建一个ORB类型指针orb，ORB类是继承自Feature2D类
    //class CV_EXPORTS_W ORB : public Feature2D
    //这里看一下create()源码：参数较多，不介绍。
    //creat()方法所有参数都有默认值，返回static　Ptr类型。
    /*
    CV_WRAP static Ptr create(int nfeatures=500,
                                   float scaleFactor=1.2f,
                                   int nlevels=8,
                                   int edgeThreshold=31,
                                   int firstLevel=0,
                                   int WTA_K=2,
                                   int scoreType=ORB::HARRIS_SCORE,
                                   int patchSize=31,
                                   int fastThreshold=20);
    */
    //所以这里的语句就是创建一个Ptr类型的orb，用于接收ORB类中create()函数的返回值
    Ptr orb = ORB::create();

    //第一步：检测Oriented FAST角点位置.
    //detect是Feature2D中的方法，orb是子类指针，可以调用
    //看一下detect()方法的原型参数：需要检测的图像，关键点数组，第三个参数为默认值
    /*
    CV_WRAP virtual void detect( InputArray image,
                                 CV_OUT std::vector& keypoints,
                                 InputArray mask=noArray() );
    */
    orb->detect(img_1, keypoints_1);
    orb->detect(img_2, keypoints_2);


    //第二步：根据角点位置计算BRIEF描述子
    //compute是Feature2D中的方法，orb是子类指针，可以调用
    //看一下compute()原型参数：图像，图像的关键点数组，Mat类型的描述子
    /*
    CV_WRAP virtual void compute( InputArray image,
                                  CV_OUT CV_IN_OUT std::vector& keypoints,
                                  OutputArray descriptors );
    */
    orb->compute(img_1, keypoints_1, descriptors_1);
    orb->compute(img_2, keypoints_2, descriptors_2);

    //定义输出检测特征点的图片。
    Mat outimg1;
    //drawKeypoints()函数原型参数：原图，原图关键点，带有关键点的输出图像，后面两个为默认值
    /*
    CV_EXPORTS_W void drawKeypoints( InputArray image,
                                     const std::vector& keypoints,
                                     InputOutputArray outImage,
                                     const Scalar& color=Scalar::all(-1),
                                     int flags=DrawMatchesFlags::DEFAULT );
    */
    //注意看，这里并没有用到描述子，描述子的作用是用于后面的关键点筛选。
    drawKeypoints(img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT);

    imshow("ORB detectors",outimg1);


    //第三步：对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离

    //创建一个匹配点数组，用于承接匹配出的DMatch，其实叫match_points_array更为贴切。matches类型为数组，元素类型为DMatch
    vector matches;

    //创建一个BFMatcher匹配器，BFMatcher类构造函数如下：两个参数都有默认值，但是第一个距离类型下面使用的并不是默认值，而是汉明距离
    //CV_WRAP BFMatcher( int normType=NORM_L2, bool crossCheck=false );
    BFMatcher matcher (NORM_HAMMING);

    //调用matcher的match方法进行匹配,这里用到了描述子，没有用关键点。
    //匹配出来的结果写入上方定义的matches[]数组中
    matcher.match(descriptors_1, descriptors_2, matches);

    //第四步：遍历matches[]数组，找出匹配点的最大距离和最小距离，用于后面的匹配点筛选。
    //这里的距离是上方求出的汉明距离数组，汉明距离表征了两个匹配的相似程度，所以也就找出了最相似和最不相似的两组点之间的距离。
    double min_dist=0, max_dist=0;//定义距离

    for (int i = 0; i < descriptors_1.rows; ++i)//遍历
    {
        double dist = matches[i].distance;
        if(distmax_dist) max_dist = dist;
    }

    printf("Max dist: %f\n", max_dist);
    printf("Min dist: %f\n", min_dist);

    //第五步：根据最小距离，对匹配点进行筛选，
    //当描述自之间的距离大于两倍的min_dist，即认为匹配有误，舍弃掉。
    //但是有时最小距离非常小，比如趋近于0了，所以这样就会导致min_dist到2*min_dist之间没有几个匹配。
    // 所以，在2*min_dist小于30的时候，就取30当上限值，小于30即可，不用2*min_dist这个值了
    std::vector good_matches;
    for (int j = 0; j < descriptors_1.rows; ++j)
    {
        if (matches[j].distance <= max(2*min_dist, 30.0))
            good_matches.push_back(matches[j]);
    }

    //第六步：绘制匹配结果

    Mat img_match;//所有匹配点图
    //这里看一下drawMatches()原型参数，简单用法就是：图1，图1关键点，图2，图2关键点，匹配数组，承接图像，后面的有默认值
    /*
    CV_EXPORTS_W void drawMatches( InputArray img1,
                                   const std::vector& keypoints1,
                                   InputArray img2,
                                   const std::vector& keypoints2,
                                   const std::vector& matches1to2,
                                   InputOutputArray outImg,
                                   const Scalar& matchColor=Scalar::all(-1),
                                   const Scalar& singlePointColor=Scalar::all(-1),
                                   const std::vector& matchesMask=std::vector(),
                                   int flags=DrawMatchesFlags::DEFAULT );
    */

    drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match);
    imshow("all th matches", img_match);

    Mat img_goodmatch;//筛选后的匹配点图
    drawMatches(img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch);
    imshow("matches", img_goodmatch);

    waitKey(0);

    return 0;
}

最后附两张结果图！加油！！