slam.rar_G2O_点云特征_点云特征提取_视觉SLAM_视觉SLAM十四讲

#include <iostream> #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 virtual void setToOriginImpl() // 重置 { _estimate << 0,0,0; } virtual void oplusImpl( const double* update ) // 更新 { _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 CurveFittingEdge( double x ): BaseUnaryEdge(), _x(x) {} // 计算曲线模型误差 void computeError() { const CurveFittingVertex* v = static_cast<const CurveFittingVertex*> (_vertices[0]); const Eigen::Vector3d abc = v->estimate(); _error(0,0) = _measurement - std::exp( abc(0,0)*_x*_x + abc(1,0)*_x + abc(2,0) ) ; } 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 a=1.0, b=2.0, c=1.0; // 真实参数值 int N=100; // 数据点 double w_sigma=1.0; // 噪声Sigma值 cv::RNG rng; // OpenCV随机数产生器 double abc[3] = {0,0,0}; // abc参数的估计值 vector<double> x_data, y_data; // 数据 cout<<"generating data: "<<endl; for ( int i=0; i<N; i++ ) { double x = i/100.0; x_data.push_back ( x ); y_data.push_back ( exp ( a*x*x + b*x + c ) + rng.gaussian ( w_sigma ) ); cout<<x_data[i]<<" "<<y_data[i]<<endl; } // 构建图优化，先设定g2o typedef g2o::BlockSolver< g2o::BlockSolverTraits<3,1> > Block; // 每个误差项优化变量维度为3，误差值维度为1 Block::LinearSolverType* linearSolver = new g2o::LinearSolverDense<Block::PoseMatrixType>(); // 线性方程求解器 Block* solver_ptr = new Block( linearSolver ); // 矩阵块求解器 // 梯度下降方法，从GN, LM, DogLeg 中选 g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg( solver_ptr ); // g2o::OptimizationAlgorithmGaussNewton* solver = new g2o::OptimizationAlgorithmGaussNewton( solver_ptr ); // g2o::OptimizationAlgorithmDogleg* solver = new g2o::OptimizationAlgorithmDogleg( solver_ptr ); 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(0,0,0) ); 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(100); 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; }