bool g2o::OptimizableGraph::Edge::resolveParameters(): edge not registered with a graph

[Felaim]

this is my test code,but it has something wrong, i have no idea how to solve this.

#include #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 #include using namespace std; 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;

}

[zackLiuzz]

I have met the same problem, have you sovled this?

[mac137]

Same problem here.