《视觉SLAM十四讲》第十章g2o代码的简化

       在学习SLAM过程中，第十章的解决大规模BA问题的g2o代码比较难读懂，不太适合我这种slam小白，于是我写了一个简化的版本，不过基本是参考书上的代码写的，尽最大的可能写的简单一些，同时也是在学习g2o的用法。数据集可以在这里下载：http://grail.cs.washington.edu/projects/bal/

文件会出现这种格式：

前三行为旋转向量，四五六行为平移向量，剩下为f , k1 , k2
1.5741515942940262e-02  
-1.2790936163850642e-02  
-4.4008498081980789e-03  
-3.4093839577186584e-02  
-1.0751387104921525e-01  
1.1202240291236032e+00  
3.9975152639358436e+02  
-3.1770643852803579e-07  
5.8820490534594022e-13  

 代码附上：

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

#include "g2o/core/base_vertex.h"
#include "g2o/core/base_binary_edge.h"

#include "g2o/stuff/sampler.h"
#include "g2o/core/sparse_optimizer.h"
#include "g2o/core/block_solver.h"
#include "g2o/core/solver.h"
#include "g2o/core/robust_kernel_impl.h"
#include "g2o/core/batch_stats.h"
#include "g2o/core/optimization_algorithm_levenberg.h"
#include "g2o/core/optimization_algorithm_dogleg.h"
#include "g2o/solvers/cholmod/linear_solver_cholmod.h"
#include "g2o/solvers/dense/linear_solver_dense.h"
#include "g2o/solvers/eigen/linear_solver_eigen.h"
#include "g2o/solvers/pcg/linear_solver_pcg.h"
#include "g2o/types/sba/types_six_dof_expmap.h"
#include "g2o/solvers/structure_only/structure_only_solver.h"
#include 


using namespace Eigen;
using namespace std;
using namespace cv;
using Sophus::SE3;
using Sophus::SO3;

//定义求解器 pose：九维 路标点：三维
typedef g2o::BlockSolver > BalBlockSolver;
typedef Eigen::Matrix< double, 9, 1 > Vector9d;


void World2Camera(const Vector9d camera, const Eigen::Vector3d P_w, Eigen::Vector3d& P_c);
void SolveBALProblem(const string filename);
inline void CamProjectionWithDistortion(const Vector9d camera, const Vector3d point, Vector2d& u);


//BAL相机顶点
class VertexCameraBAL : public g2o::BaseVertex<9,Vector9d>
{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    VertexCameraBAL() {}
    virtual void setToOriginImpl() {}

    virtual void oplusImpl ( const double* update )
    {
        Vector9d::ConstMapType v ( update );
        _estimate += v;
    }

    virtual bool read ( std::istream& in ) { return false;}
    virtual bool write ( std::ostream& out ) const {return false;}
};

//BAL 空间点顶点
class VertexPointBAL : public g2o::BaseVertex<3, Eigen::Vector3d>
{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    VertexPointBAL() {}
    virtual void setToOriginImpl() {}

    virtual void oplusImpl ( const double* update )
    {
        Eigen::Vector3d::ConstMapType v ( update );
        _estimate += v;
    }

    virtual bool read ( std::istream& in ) { return false;}
    virtual bool write ( std::ostream& out ) const {return false;}
};

//BAL 边
class EdgeObservationBAL : public g2o::BaseBinaryEdge<2, Eigen::Vector2d,VertexCameraBAL, VertexPointBAL>
{
public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    EdgeObservationBAL() {}

    virtual bool read ( std::istream& in ) {return false;}
    virtual bool write ( std::ostream& out ) const {return false;}

    //使用G20数值导,不像书上调用ceres的自动求导功能.
    virtual void computeError() override
    {
        //这里将第0个顶点，相机位姿取出来。
        const VertexCameraBAL* cam = static_cast ( vertex ( 0 ) );
        //这里将第1个顶点，空间点位置取出来。
        const VertexPointBAL* point = static_cast ( vertex ( 1 ) );

        Eigen::Vector2d u;
        CamProjectionWithDistortion(cam->estimate(), point->estimate(), u );
        _error[0] = u[0] - _measurement[0];
        _error[1] = u[1] - _measurement[1];

    }
};

//加载BAL的文本文件
class LoadBALProblem
{
public:
    LoadBALProblem( string filename ):filename_(filename) {}
    ~LoadBALProblem()
    {
        delete[] point_index_;
        delete[] camera_index_;
        delete[] observations_;
        delete[] observations_cameras;
        delete[] observations_points;
    }
    //返回相机数量,空间点数量，观测点数量
    int num_cameras()        const{ return num_cameras_;     }
    int num_points()         const{ return num_points_;      }
    int num_observations()   const{ return num_observations_;}

    double num_observations_cameras(int index)   { return observations_cameras[index];}
    double num_observations_points(int index)   { return observations_points[index];}
    double num_observations_uv(int index)   { return observations_[index];}

    int point_index(int index)   { return point_index_[index];}
    int camera_index(int index)   { return camera_index_[index];}

    //读取数据
    void ReadData();
    //将pose和point生成点云.
    void WriteToPLYFile(const std::string& filename);
    //当优化完成,重新将数据写入数组,覆盖原来未被优化的数据
    double* ReWritePoses(){return observations_cameras;}
    double* ReWritePoints(){return observations_points;}

    //坐标系操作,相机坐标转换到世界坐标
    void Camera2World(const cv::Mat angleAxis, const Eigen::Vector3d P_c, Eigen::Vector3d& P_w);

private:
    int num_cameras_;
    int num_points_;
    int num_observations_;
    int observations_cameras_;
    int observations_points_;
    string filename_;

    int* point_index_;
    int* camera_index_;
    double* observations_;
    double* observations_cameras;
    double* observations_points;
};


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

    const string filename = "../problem-16-22106-pre.txt";
    //const string filename = "../problem-52-64053-pre.txt";
    SolveBALProblem(filename);

    return 0;
}

/***
 * 解决BAL问题
 * @param filename 将.txt文件传进来,最后将优化的后点云输出
 */
void SolveBALProblem(const string filename)
{
    //生成初始数据
    LoadBALProblem loadBALProblem(filename);
    loadBALProblem.ReadData();

    //生成初始为未被优化的ply点云文件
    loadBALProblem.WriteToPLYFile("../old.ply");

    //创建一个稀疏优化器对象
    g2o::SparseOptimizer optimizer;

    //使用稀疏求解器
    g2o::LinearSolver* linearSolver =
            new g2o::LinearSolverCholmod();
    dynamic_cast* >(linearSolver)->setBlockOrdering(true);

    //矩阵块求解器
    BalBlockSolver* solver_ptr = new BalBlockSolver(linearSolver);

    //使用LM算法
    g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(solver_ptr);
    //优化器构建完成
    optimizer.setAlgorithm(solver);
    //打开调试输出
    optimizer.setVerbose(true);

    //增加pose顶点
    const int num_cam = loadBALProblem.num_cameras();
    for(int i = 0; i < num_cam; i++)
    {
        Vector9d temVecCamera;
        for (int j = 0; j < 9; j++)
        {
            temVecCamera[j] = loadBALProblem.num_observations_cameras(9*i+j);
        }
        VertexCameraBAL* pCamera = new VertexCameraBAL();
        pCamera->setEstimate(temVecCamera);
        pCamera->setId(i);

        optimizer.addVertex(pCamera);

    }

    //增加路标顶点
    const int point_num = loadBALProblem.num_points();
    for(int i = 0; i < point_num; i++)
    {
        Vector3d temVecPoint;
        for (int j = 0; j < 3; j++)
        {
            temVecPoint[j] = loadBALProblem.num_observations_points(3*i+j);
        }
        VertexPointBAL* pPoint = new VertexPointBAL();
        pPoint->setEstimate(temVecPoint);

        //这里加上pose的数量,避免跟pose的ID重复
        pPoint->setId(i + num_cam);

        pPoint->setMarginalized(true);
        optimizer.addVertex(pPoint);
    }
    //增加边
    const int  num_observations =loadBALProblem.num_observations();
    for(int i = 0; i < num_observations; ++i)
    {
        EdgeObservationBAL* bal_edge = new EdgeObservationBAL();

        //得到相机ID和空间点ID
        const int camera_id = loadBALProblem.camera_index(i);
        const int point_id = loadBALProblem.point_index(i) + num_cam;

        //使用了鲁棒核函数
        g2o::RobustKernelHuber* rk = new g2o::RobustKernelHuber;
        rk->setDelta(1.0);
        bal_edge->setRobustKernel(rk);


        bal_edge->setVertex(0,dynamic_cast(optimizer.vertex(camera_id)));
        bal_edge->setVertex(1,dynamic_cast(optimizer.vertex(point_id)));
        bal_edge->setInformation(Eigen::Matrix2d::Identity());
        bal_edge->setMeasurement(Eigen::Vector2d(loadBALProblem.num_observations_uv(2*i + 0),
                                                 loadBALProblem.num_observations_uv(2*i + 1)));

        optimizer.addEdge(bal_edge);
    }

    optimizer.initializeOptimization();
    optimizer.setVerbose(true);
    optimizer.optimize(20);

    double* cameras = loadBALProblem.ReWritePoses();
    for(int i = 0; i < num_cam; i++)
    {
        VertexCameraBAL* pCamera = dynamic_cast(optimizer.vertex(i));
        Vector9d NewCameraVec = pCamera->estimate();
        memcpy(cameras + i * 9, NewCameraVec.data(), sizeof(double) * 9);
    }

    double* points = loadBALProblem.ReWritePoints();
    for(int j = 0; j < point_num; j++)
    {
        VertexPointBAL* pPoint = dynamic_cast(optimizer.vertex(j + num_cam));
        Eigen::Vector3d NewPointVec = pPoint->estimate();
        memcpy(points + j * 3, NewPointVec.data(), sizeof(double) * 3);
    }

    loadBALProblem.WriteToPLYFile("../new.ply");
    cout<<"finished..." <(1,3)<(0,0),Rcw.at(0,1),Rcw.at(0,2), camera[3],
            Rcw.at(1,0),Rcw.at(1,1),Rcw.at(1,2), camera[4],
            Rcw.at(2,0),Rcw.at(2,1),Rcw.at(2,2), camera[5],
            0,0,0,1;

    //这里的非齐次坐标的变换要注意
    Vector4d Pw(P_w[0],P_w[1],P_w[2],1);
    Vector4d P = T*Pw;
    P_c[0] = P[0];
    P_c[1] = P[1];
    P_c[2] = P[2];
}

/***
 * 去畸变，世界坐标下的空间点转换成相机坐标系下的空间,最后变成像素坐标的像素点,基本跟书上的一致
 * 思想如下：
 * P  =  R * X + t       (conversion from world to camera coordinates)
 * p  = -P / P.z         (perspective division) 这里有个负号需要注意
 * p' =  f * r(p) * p    (conversion to pixel coordinates)
 * r(p) = 1.0 + k1 * ||p||^2 + k2 * ||p||^4.
 * @param camera 相机的9的数据，实际上只用到前面6个数据
 * @param point 世界坐标系下的空间点
 * @param u 像素坐标的像素点
 */
inline void CamProjectionWithDistortion(const Vector9d camera, const Vector3d point, Vector2d& u)
{

    Eigen::Vector3d p;
    World2Camera(camera, point, p);

    // Compute the center fo distortion
    double xp = -p[0]/p[2];
    double yp = -p[1]/p[2];

    // Apply second and fourth order radial distortion
    const double k1 = camera[7];
    const double k2 = camera[8];

    double r2 = xp*xp + yp*yp;
    double distortion = 1.0 + r2 * k1 + k2*r2*r2 ;

    const double f = camera[6];
    u[0] = f * distortion * xp;
    u[1] = f * distortion * yp;

}

/***
 * 实现将pose转换到世界坐标系
 * @param angleAxis 旋转向量
 * @param P_c pose的t
 * @param P_w 世界坐标系pose的t
 */
void LoadBALProblem::Camera2World( const cv::Mat angleAxis, const Eigen::Vector3d P_c, Eigen::Vector3d& P_w)
{
    cv::Mat Rcw;
    //这里调用opencv将旋转向量转换成旋转矩阵的API
    cv::Rodrigues(angleAxis,Rcw);
    Eigen::Matrix4d Tcw;
    Tcw << Rcw.at(0,0),Rcw.at(0,1),Rcw.at(0,2),P_c[0],
            Rcw.at(1,0),Rcw.at(1,1),Rcw.at(1,2),P_c[1],
            Rcw.at(2,0),Rcw.at(2,1),Rcw.at(2,2),P_c[2],
            0,0,0,1;
    Eigen::Matrix4d Twc;
    //Twc = Tcw^-1
    Twc = Tcw.inverse();
    P_w[0] = Twc(0,3);
    P_w[1] = Twc(1,3);
    P_w[2] = Twc(2,3);
}

/***
 *生成点云文件,基本跟书上一样
 * @param filename
 */
void LoadBALProblem::WriteToPLYFile(const std::string& filename)
{
    std::ofstream of(filename.c_str());

    of<< "ply"
      << '\n' << "format ascii 1.0"
      << '\n' << "element vertex " << num_cameras_ + num_points_
      << '\n' << "property float x"
      << '\n' << "property float y"
      << '\n' << "property float z"
      << '\n' << "property uchar red"
      << '\n' << "property uchar green"
      << '\n' << "property uchar blue"
      << '\n' << "end_header" << std::endl;

    for(int i = 0; i < num_cameras(); ++i)
    {


        Eigen::Vector3d P_c;
        Eigen::Vector3d P_w;

        cv::Mat angleAxis = (cv::Mat_(1,3)<>num_cameras_;
    fin>>num_points_;
    fin>>num_observations_;

    //输出第一行数据,这里包括，相机的姿态数量,空间点的数量,观测数据数量
    std::cout << "pose number: " << num_cameras_ <>camera_index_[i];
        fin>>point_index_[i];
        fin>>observations_[2*i];
        fin>>observations_[2*i+1];
    }

    //这里读取本文文件里一行一个数字的内容,相机的参数有9个，分别为R,t,f,k1,k2,这个R是旋转向量,空间为三个一组
    observations_cameras_ = 9*num_cameras_;
    observations_points_ = 3*num_points_;
    observations_cameras = new double[observations_cameras_];
    observations_points = new double[observations_points_];

    //读取相机初始数据
    for (int i = 0; i < observations_cameras_; ++i)
    {
        fin>>observations_cameras[i];
    }

    //读取空间初始数据
    for (int i = 0; i < observations_points_; ++i)
    {
        fin>>observations_points[i];
    }
}

CMakeLists文件：

cmake_minimum_required( VERSION 2.8 )
project( BAL )

set( CMAKE_BUILD_TYPE "Release" )
set( CMAKE_CXX_FLAGS "-std=c++11 -O3" )

list( APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake_modules )

# 寻找G2O Cholmod eigen3
find_package( G2O REQUIRED )
find_package( Cholmod )
include_directories( 
    ${G2O_INCLUDE_DIRS} ${CHOLMOD_INCLUDE_DIR}
    "/usr/include/eigen3"
)

# sophus
find_package( Sophus REQUIRED )
include_directories( ${Sophus_INCLUDE_DIRS} )

#OpenCV
find_package( OpenCV REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )


add_executable( BAL main.cpp )

target_link_libraries( BAL

        g2o_types_slam2d
        g2o_types_slam3d
        g2o_core
        g2o_stuff
        g2o_types_sclam2d
        ${CHOLMOD_LIBRARIES}
        ${Sophus_LIBRARIES}
        ${OpenCV_LIBRARIES}
        )

终端运行效果如下：

 

使用meshlab 打开ply文件 命令为meshlab xxx.ply

下一篇博客会实际写出雅克比矩阵，而不是使用g2o帮住求导。

点这里到达下一篇https://blog.csdn.net/JohnnyYeh/article/details/82315543

如果本文有什么错误的地方，请联系我，我及时修改。

转载请注明出处：http://blog.csdn.net/johnnyyeh/article/details/79310655