g2o使用編程步驟流程

1.整個代碼以後圍繞着optimizer對象編程。首先實例化一個g2o::SparseOptimizer類的 optimizer優化器，

g2o::SparseOptimizer optimizer;

SparseOptimizer是一個虛類，繼承了OptimizableGraph  -> HyperGraph；

SparseOptimizer主要成員：

 protected:
    bool* _forceStopFlag; //迭代過程中可以停止
    bool _verbose;// 冗餘信息

    VertexContainer _ivMap;
    VertexContainer _activeVertices;   ///< sorted according to VertexIDCompare
    EdgeContainer _activeEdges;        ///< sorted according to EdgeIDCompare
    BatchStatisticsContainer _batchStatistics;   ///< global statistics of the optimizer, e.g., timing, num-non-zeros
    bool _computeBatchStatistics;

外部程序需要關係的幾個函數接口

virtual bool initializeOptimization(HyperGraph::VertexSet& vset, int level=0);
    virtual bool updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset);
	int optimize(int iterations, bool online = false);
	void setVerbose(bool verbose);
	void setAlgorithm(OptimizationAlgorithm*)

setAlgorithm()需要傳遞一個優化算法求解器一般有兩種

1.solver = new g2o::OptimizationAlgorithmGaussNewton 

2.solver = new g2o::OptimizationAlgorithmLevenberg

 optimizer.initializeOptimization(); // 初始化
  optimizer.setVerbose(verbose);// 設置 優化過程輸出信息

  optimizer.optimize(maxIterations); //開始優化 需要迭代次數`

下面是一個曲線擬合的代碼例子  曲線是： y= a * exp(-lambda * t) + b 這裏a = 2,b = 0.4,lambda = 0.2

#include <Eigen/Core>
#include <iostream>

#include "g2o/stuff/sampler.h"
#include "g2o/stuff/command_args.h"
#include "g2o/core/sparse_optimizer.h"
#include "g2o/core/block_solver.h"
#include "g2o/core/solver.h"
#include "g2o/core/optimization_algorithm_levenberg.h"
#include "g2o/core/base_vertex.h"
#include "g2o/core/base_unary_edge.h"
#include "g2o/solvers/dense/linear_solver_dense.h"

using namespace std;

/**
 * \brief the params, a, b, and lambda for a * exp(-lambda * t) + b
 */
class VertexParams : public g2o::BaseVertex<3, Eigen::Vector3d>
{
  public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
    VertexParams()
    {
    }

    virtual bool read(std::istream& /*is*/)
    {
      cerr << __PRETTY_FUNCTION__ << " not implemented yet" << endl;
      return false;
    }

    virtual bool write(std::ostream& /*os*/) const
    {
      cerr << __PRETTY_FUNCTION__ << " not implemented yet" << endl;
      return false;
    }

    virtual void setToOriginImpl()
    {
      cerr << __PRETTY_FUNCTION__ << " not implemented yet" << endl;
    }

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

/**
 * \brief measurement for a point on the curve
 *
 * Here the measurement is the point which is lies on the curve.
 * The error function computes the difference between the curve
 * and the point.
 */
class EdgePointOnCurve : public g2o::BaseUnaryEdge<1, Eigen::Vector2d, VertexParams>
{
  public:
    EIGEN_MAKE_ALIGNED_OPERATOR_NEW
    EdgePointOnCurve()
    {
    }
    virtual bool read(std::istream& /*is*/)
    {
      cerr << __PRETTY_FUNCTION__ << " not implemented yet" << endl;
      return false;
    }
    virtual bool write(std::ostream& /*os*/) const
    {
      cerr << __PRETTY_FUNCTION__ << " not implemented yet" << endl;
      return false;
    }

    void computeError()
    {
      const VertexParams* params = static_cast<const VertexParams*>(vertex(0));
      const double& a = params->estimate()(0);
      const double& b = params->estimate()(1);
      const double& lambda = params->estimate()(2);
      double fval = a * exp(-lambda * measurement()(0)) + b;
      _error(0) = fval - measurement()(1);
    }
};

int main(int argc, char** argv)
{
  int numPoints;
  int maxIterations;
  bool verbose;
  std::vector<int> gaugeList;
  string dumpFilename;
  g2o::CommandArgs arg;
  arg.param("dump", dumpFilename, "", "dump the points into a file");
  arg.param("numPoints", numPoints, 50, "number of points sampled from the curve");
  arg.param("i", maxIterations, 10, "perform n iterations");
  arg.param("v", verbose, false, "verbose output of the optimization process");

  arg.parseArgs(argc, argv);

  // generate random data
  double a = 2.;
  double b = 0.4;
  double lambda = 0.2;
  Eigen::Vector2d* points = new Eigen::Vector2d[numPoints];//數組 分配了numPoints 


  //產生數據集
  for (int i = 0; i < numPoints; ++i) 
  {
    double x = g2o::Sampler::uniformRand(0, 10);// 隨機產生 x數據
    double y = a * exp(-lambda * x) + b; // 按擬合曲線計算 對應的Y值
    // add Gaussian noise
    y += g2o::Sampler::gaussRand(0, 0.02);
    points[i].x() = x;
    points[i].y() = y;
  }

  if (dumpFilename.size() > 0) {
    ofstream fout(dumpFilename.c_str());
    for (int i = 0; i < numPoints; ++i)
      fout << points[i].transpose() << endl;
  }

  // some handy typedefs
  typedef g2o::BlockSolver< g2o::BlockSolverTraits<Eigen::Dynamic, Eigen::Dynamic> >  MyBlockSolver;
  typedef g2o::LinearSolverDense<MyBlockSolver::PoseMatrixType> MyLinearSolver;

  // setup the solver
  g2o::SparseOptimizer optimizer;
  optimizer.setVerbose(false);

  g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg(
  g2o::make_unique<MyBlockSolver>(g2o::make_unique<MyLinearSolver>()));

  optimizer.setAlgorithm(solver);

  // build the optimization problem given the points
  // 1. add the parameter vertex 
  // params 添加要 求解的參數params  這個參數
  VertexParams* params = new VertexParams();
  params->setId(0);
  params->setEstimate(Eigen::Vector3d(1,1,1)); // some initial value for the params
  optimizer.addVertex(params);


  
  // 2. add the points we measured to be on the curve
  // 添加測量值
  for (int i = 0; i < numPoints; ++i) 
 {
    EdgePointOnCurve* e = new EdgePointOnCurve; //二元邊
    e->setInformation(Eigen::Matrix<double, 1, 1>::Identity());
    e->setVertex(0, params);
    e->setMeasurement(points[i]);
    optimizer.addEdge(e);
  }

  // perform the optimization
  optimizer.initializeOptimization();
  optimizer.setVerbose(verbose);
  optimizer.optimize(maxIterations);

  if (verbose)
    cout << endl;

  // print out the result
  cout << "Target curve" << endl;
  cout << "a * exp(-lambda * x) + b" << endl;
  cout << "Iterative least squares solution" << endl;
  cout << "a      = " << params->estimate()(0) << endl;
  cout << "b      = " << params->estimate()(1) << endl;
  cout << "lambda = " << params->estimate()(2) << endl;
  cout << endl;

  // clean up
  delete[] points;

  return 0;
}