• 视觉十四讲:第六讲_g2o图优化


    g2o是一个基于图优化的库,图优化是把优化问题表现为一种图的方式。一个图由若干个顶点和边组成。
    顶点表示优化变量,边表示误差项。
    g2o的使用步骤:
    1.定义顶点和边的类型;
    2.构建图;
    3.选择优化算法;
    4.调用g2o进行优化

    #include <iostream>
    #include <g2o/core/g2o_core_api.h>
    #include <g2o/core/base_vertex.h>
    #include <g2o/core/base_unary_edge.h>
    #include <g2o/core/block_solver.h>
    #include <g2o/core/optimization_algorithm_levenberg.h>
    #include <g2o/core/optimization_algorithm_gauss_newton.h>
    #include <g2o/core/optimization_algorithm_dogleg.h>
    #include <g2o/solvers/dense/linear_solver_dense.h>
    #include <Eigen/Core>
    #include <opencv2/core/core.hpp>
    #include <cmath>
    #include <chrono>
    
    using namespace std;
    
    // 曲线模型的顶点,模板参数:优化变量维度和数据类型
    class CurveFittingVertex : public g2o::BaseVertex<3, Eigen::Vector3d> {
    
    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    
      // 初始化
      virtual void setToOriginImpl() override {
        _estimate << 0, 0, 0;
      }
    
      // 更新估计值
      virtual void oplusImpl(const double *update) override {
        _estimate += Eigen::Vector3d(update);
      }
    
      // 存盘和读盘:留空
      virtual bool read(istream &in) {}
    
      virtual bool write(ostream &out) const {}
    };
    
    // 误差模型 模板参数:观测值维度,类型,连接顶点类型
    class CurveFittingEdge : public g2o::BaseUnaryEdge<1, double, CurveFittingVertex> {
    public:
      EIGEN_MAKE_ALIGNED_OPERATOR_NEW
      //可传入变量
      CurveFittingEdge(double x) : BaseUnaryEdge(), _x(x) {}
    
      // 计算曲线模型误差
      virtual void computeError() override {
        //获取最新的估计值
        const CurveFittingVertex *v = static_cast<const CurveFittingVertex *> (_vertices[0]);
        //估计值赋值
        const Eigen::Vector3d abc = v->estimate();
        //计算误差
        _error(0, 0) = _measurement - std::exp(abc(0, 0) * _x * _x + abc(1, 0) * _x + abc(2, 0));
      }
    
      // 计算雅可比矩阵
      virtual void linearizeOplus() override {
        //获取最新的估计值
        const CurveFittingVertex *v = static_cast<const CurveFittingVertex *> (_vertices[0]);
        //估计值赋值
        const Eigen::Vector3d abc = v->estimate();
        //雅克比矩阵赋值
        double y = exp(abc[0] * _x * _x + abc[1] * _x + abc[2]);
        _jacobianOplusXi[0] = -_x * _x * y;
        _jacobianOplusXi[1] = -_x * y;
        _jacobianOplusXi[2] = -y;
      }
    
      virtual bool read(istream &in) {}
    
      virtual bool write(ostream &out) const {}
    
    public:
      double _x;  // x 值, y 值为 _measurement
    };
    
    int main(int argc, char **argv) {
      double ar = 1.0, br = 2.0, cr = 1.0;         // 真实参数值
      double ae = 2.0, be = -1.0, ce = 5.0;        // 估计参数值
      int N = 100;                                 // 数据点
      double w_sigma = 1.0;                        // 噪声Sigma值
      double inv_sigma = 1.0 / w_sigma;
      cv::RNG rng;                                 // OpenCV随机数产生器
    
      vector<double> x_data, y_data;      // 数据
      for (int i = 0; i < N; i++) {
        double x = i / 100.0;
        x_data.push_back(x);
        y_data.push_back(exp(ar * x * x + br * x + cr) + rng.gaussian(w_sigma * w_sigma));
      }
    
      // 构建图优化,先设定g2o
      typedef g2o::BlockSolver<g2o::BlockSolverTraits<3, 1>> BlockSolverType;  // 每个误差项优化变量维度为3,误差值维度为1
      typedef g2o::LinearSolverDense<BlockSolverType::PoseMatrixType> LinearSolverType; // 线性求解器类型
    
      // 梯度下降方法,可以从GN, LM, DogLeg 中选
      auto solver = new g2o::OptimizationAlgorithmGaussNewton(
        g2o::make_unique<BlockSolverType>(g2o::make_unique<LinearSolverType>()));
      g2o::SparseOptimizer optimizer;     // 图模型
      optimizer.setAlgorithm(solver);   // 设置求解器
      optimizer.setVerbose(true);       // 打开调试输出
    
      // 往图中增加顶点
      CurveFittingVertex *v = new CurveFittingVertex();
      v->setEstimate(Eigen::Vector3d(ae, be, ce));
      v->setId(0);
      optimizer.addVertex(v);
    
      // 往图中增加边
      for (int i = 0; i < N; i++) {
        CurveFittingEdge *edge = new CurveFittingEdge(x_data[i]);
        edge->setId(i);
        edge->setVertex(0, v);                // 设置连接的顶点
        edge->setMeasurement(y_data[i]);      // 观测数值
        edge->setInformation(Eigen::Matrix<double, 1, 1>::Identity() * 1 / (w_sigma * w_sigma)); // 信息矩阵:协方差矩阵之逆
        optimizer.addEdge(edge);
      }
    
      // 执行优化
      cout << "start optimization" << endl;
      chrono::steady_clock::time_point t1 = chrono::steady_clock::now();
      optimizer.initializeOptimization();
      optimizer.optimize(10);   //迭代次数10次
      chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
      chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>>(t2 - t1);
      cout << "solve time cost = " << time_used.count() << " seconds. " << endl;
    
      // 输出优化值
      Eigen::Vector3d abc_estimate = v->estimate();
      cout << "estimated model: " << abc_estimate.transpose() << endl;
    
      return 0;
    }
    

    CMakeLists.txt:

    cmake_minimum_required(VERSION 2.8)
    project(ch6)
    
    set(CMAKE_BUILD_TYPE Release)
    set(CMAKE_CXX_FLAGS "-std=c++14 -O3")
    
    list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
    
    # OpenCV
    find_package(OpenCV REQUIRED)
    include_directories(${OpenCV_INCLUDE_DIRS})
    
    # Ceres
    find_package(Ceres REQUIRED)
    include_directories(${CERES_INCLUDE_DIRS})
    
    # g2o
    find_package(G2O REQUIRED)
    include_directories(${G2O_INCLUDE_DIRS})
    
    # Eigen
    include_directories("/usr/include/eigen3")
    
    add_executable(gaussNewton gaussNewton.cpp)
    target_link_libraries(gaussNewton ${OpenCV_LIBS})
    
    add_executable(ceresCurveFitting ceresCurveFitting.cpp)
    target_link_libraries(ceresCurveFitting ${OpenCV_LIBS} ${CERES_LIBRARIES})
    
    add_executable(g2oCurveFitting g2oCurveFitting.cpp)
    target_link_libraries(g2oCurveFitting ${OpenCV_LIBS} g2o_core g2o_stuff)
    
    

    g2o需要新建一个cmake文件,建立一个FindG2O.cmake的文件:

    # Find the header files
    
    FIND_PATH(G2O_INCLUDE_DIR g2o/core/base_vertex.h
      ${G2O_ROOT}/include
      $ENV{G2O_ROOT}/include
      $ENV{G2O_ROOT}
      /usr/local/include
      /usr/include
      /opt/local/include
      /sw/local/include
      /sw/include
      NO_DEFAULT_PATH
      )
    
    # Macro to unify finding both the debug and release versions of the
    # libraries; this is adapted from the OpenSceneGraph FIND_LIBRARY
    # macro.
    
    MACRO(FIND_G2O_LIBRARY MYLIBRARY MYLIBRARYNAME)
    
      FIND_LIBRARY("${MYLIBRARY}_DEBUG"
        NAMES "g2o_${MYLIBRARYNAME}_d"
        PATHS
        ${G2O_ROOT}/lib/Debug
        ${G2O_ROOT}/lib
        $ENV{G2O_ROOT}/lib/Debug
        $ENV{G2O_ROOT}/lib
        NO_DEFAULT_PATH
        )
    
      FIND_LIBRARY("${MYLIBRARY}_DEBUG"
        NAMES "g2o_${MYLIBRARYNAME}_d"
        PATHS
        ~/Library/Frameworks
        /Library/Frameworks
        /usr/local/lib
        /usr/local/lib64
        /usr/lib
        /usr/lib64
        /opt/local/lib
        /sw/local/lib
        /sw/lib
        )
      
      FIND_LIBRARY(${MYLIBRARY}
        NAMES "g2o_${MYLIBRARYNAME}"
        PATHS
        ${G2O_ROOT}/lib/Release
        ${G2O_ROOT}/lib
        $ENV{G2O_ROOT}/lib/Release
        $ENV{G2O_ROOT}/lib
        NO_DEFAULT_PATH
        )
    
      FIND_LIBRARY(${MYLIBRARY}
        NAMES "g2o_${MYLIBRARYNAME}"
        PATHS
        ~/Library/Frameworks
        /Library/Frameworks
        /usr/local/lib
        /usr/local/lib64
        /usr/lib
        /usr/lib64
        /opt/local/lib
        /sw/local/lib
        /sw/lib
        )
      
      IF(NOT ${MYLIBRARY}_DEBUG)
        IF(MYLIBRARY)
          SET(${MYLIBRARY}_DEBUG ${MYLIBRARY})
        ENDIF(MYLIBRARY)
      ENDIF( NOT ${MYLIBRARY}_DEBUG)
      
    ENDMACRO(FIND_G2O_LIBRARY LIBRARY LIBRARYNAME)
    
    # Find the core elements
    FIND_G2O_LIBRARY(G2O_STUFF_LIBRARY stuff)
    FIND_G2O_LIBRARY(G2O_CORE_LIBRARY core)
    
    # Find the CLI library
    FIND_G2O_LIBRARY(G2O_CLI_LIBRARY cli)
    
    # Find the pluggable solvers
    FIND_G2O_LIBRARY(G2O_SOLVER_CHOLMOD solver_cholmod)
    FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE solver_csparse)
    FIND_G2O_LIBRARY(G2O_SOLVER_CSPARSE_EXTENSION csparse_extension)
    FIND_G2O_LIBRARY(G2O_SOLVER_DENSE solver_dense)
    FIND_G2O_LIBRARY(G2O_SOLVER_PCG solver_pcg)
    FIND_G2O_LIBRARY(G2O_SOLVER_SLAM2D_LINEAR solver_slam2d_linear)
    FIND_G2O_LIBRARY(G2O_SOLVER_STRUCTURE_ONLY solver_structure_only)
    FIND_G2O_LIBRARY(G2O_SOLVER_EIGEN solver_eigen)
    
    # Find the predefined types
    FIND_G2O_LIBRARY(G2O_TYPES_DATA types_data)
    FIND_G2O_LIBRARY(G2O_TYPES_ICP types_icp)
    FIND_G2O_LIBRARY(G2O_TYPES_SBA types_sba)
    FIND_G2O_LIBRARY(G2O_TYPES_SCLAM2D types_sclam2d)
    FIND_G2O_LIBRARY(G2O_TYPES_SIM3 types_sim3)
    FIND_G2O_LIBRARY(G2O_TYPES_SLAM2D types_slam2d)
    FIND_G2O_LIBRARY(G2O_TYPES_SLAM3D types_slam3d)
    
    # G2O solvers declared found if we found at least one solver
    SET(G2O_SOLVERS_FOUND "NO")
    IF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN)
      SET(G2O_SOLVERS_FOUND "YES")
    ENDIF(G2O_SOLVER_CHOLMOD OR G2O_SOLVER_CSPARSE OR G2O_SOLVER_DENSE OR G2O_SOLVER_PCG OR G2O_SOLVER_SLAM2D_LINEAR OR G2O_SOLVER_STRUCTURE_ONLY OR G2O_SOLVER_EIGEN)
    
    # G2O itself declared found if we found the core libraries and at least one solver
    SET(G2O_FOUND "NO")
    IF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND)
      SET(G2O_FOUND "YES")
    ENDIF(G2O_STUFF_LIBRARY AND G2O_CORE_LIBRARY AND G2O_INCLUDE_DIR AND G2O_SOLVERS_FOUND)
    
    
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  • 原文地址:https://www.cnblogs.com/penuel/p/12986058.html
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