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pose_graph_gtsam.cpp
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#include <iostream>
#include <fstream>
#include <string>
#include <Eigen/Core>
#include <sophus/se3.h>
#include <sophus/so3.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
using namespace std;
using Sophus::SE3;
using Sophus::SO3;
/************************************************
* 本程序演示如何用 gtsam 进行位姿图优化
* sphere.g2o 是人工生成的一个 Pose graph，我们来优化它。
* 与 g2o 相似，在 gtsam 中添加的是因子，相当于误差
* **********************************************/
int main ( int argc, char** argv )
{
if ( argc != 2 )
{
cout<<"Usage: pose_graph_gtsam sphere.g2o"<<endl;
return 1;
}
ifstream fin ( argv[1] );
if ( !fin )
{
cout<<"file "<<argv[1]<<" does not exist."<<endl;
return 1;
}
gtsam::NonlinearFactorGraph::shared_ptr graph ( new gtsam::NonlinearFactorGraph ); // gtsam的因子图
gtsam::Values::shared_ptr initial ( new gtsam::Values ); // 初始值
// 从g2o文件中读取节点和边的信息
int cntVertex=0, cntEdge = 0;
cout<<"reading from g2o file"<<endl;
while ( !fin.eof() )
{
string tag;
fin>>tag;
if ( tag == "VERTEX_SE3:QUAT" )
{
// 顶点
gtsam::Key id;
fin>>id;
double data[7];
for ( int i=0; i<7; i++ ) fin>>data[i];
// 转换至gtsam的Pose3
gtsam::Rot3 R = gtsam::Rot3::Quaternion ( data[6], data[3], data[4], data[5] );
gtsam::Point3 t ( data[0], data[1], data[2] );
initial->insert ( id, gtsam::Pose3 ( R,t ) ); // 添加初始值
cntVertex++;
}
else if ( tag == "EDGE_SE3:QUAT" )
{
// 边，对应到因子图中的因子
gtsam::Matrix m = gtsam::I_6x6; // 信息矩阵
gtsam::Key id1, id2;
fin>>id1>>id2;
double data[7];
for ( int i=0; i<7; i++ ) fin>>data[i];
gtsam::Rot3 R = gtsam::Rot3::Quaternion ( data[6], data[3], data[4], data[5] );
gtsam::Point3 t ( data[0], data[1], data[2] );
for ( int i=0; i<6; i++ )
for ( int j=i; j<6; j++ )
{
double mij;
fin>>mij;
m ( i,j ) = mij;
m ( j,i ) = mij;
}
// g2o的信息矩阵定义方式与gtsam不同，这里对它进行修改
gtsam::Matrix mgtsam = gtsam::I_6x6;
mgtsam.block<3,3> ( 0,0 ) = m.block<3,3> ( 3,3 ); // cov rotation
mgtsam.block<3,3> ( 3,3 ) = m.block<3,3> ( 0,0 ); // cov translation
mgtsam.block<3,3> ( 0,3 ) = m.block<3,3> ( 0,3 ); // off diagonal
mgtsam.block<3,3> ( 3,0 ) = m.block<3,3> ( 3,0 ); // off diagonal
gtsam::SharedNoiseModel model = gtsam::noiseModel::Gaussian::Information ( mgtsam ); // 高斯噪声模型
gtsam::NonlinearFactor::shared_ptr factor (
new gtsam::BetweenFactor<gtsam::Pose3> ( id1, id2, gtsam::Pose3 ( R,t ), model ) // 添加一个因子
);
graph->push_back ( factor );
cntEdge++;
}
if ( !fin.good() )
break;
}
cout<<"read total "<<cntVertex<<" vertices, "<<cntEdge<<" edges."<<endl;
// 固定第一个顶点，在gtsam中相当于添加一个先验因子
gtsam::NonlinearFactorGraph graphWithPrior = *graph;
gtsam::noiseModel::Diagonal::shared_ptr priorModel =
gtsam::noiseModel::Diagonal::Variances (
( gtsam::Vector ( 6 ) <<1e-6, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6 ).finished()
);
gtsam::Key firstKey = 0;
for ( const gtsam::Values::ConstKeyValuePair& key_value: *initial )
{
cout<<"Adding prior to g2o file "<<endl;
graphWithPrior.add ( gtsam::PriorFactor<gtsam::Pose3> (
key_value.key, key_value.value.cast<gtsam::Pose3>(), priorModel )
);
break;
}
// 开始因子图优化，配置优化选项
cout<<"optimizing the factor graph"<<endl;
// 我们使用 LM 优化
gtsam::LevenbergMarquardtParams params_lm;
params_lm.setVerbosity("ERROR");
params_lm.setMaxIterations(20);
params_lm.setLinearSolverType("MULTIFRONTAL_QR");
gtsam::LevenbergMarquardtOptimizer optimizer_LM( graphWithPrior, *initial, params_lm );
// 你可以尝试下 GN
// gtsam::GaussNewtonParams params_gn;
// params_gn.setVerbosity("ERROR");
// params_gn.setMaxIterations(20);
// params_gn.setLinearSolverType("MULTIFRONTAL_QR");
// gtsam::GaussNewtonOptimizer optimizer ( graphWithPrior, *initial, params_gn );
gtsam::Values result = optimizer_LM.optimize();
cout<<"Optimization complete"<<endl;
cout<<"initial error: "<<graph->error ( *initial ) <<endl;
cout<<"final error: "<<graph->error ( result ) <<endl;
cout<<"done. write to g2o ... "<<endl;
// 写入 g2o 文件，同样伪装成 g2o 中的顶点和边，以便用 g2o_viewer 查看。
// 顶点咯
ofstream fout ( "result_gtsam.g2o" );
for ( const gtsam::Values::ConstKeyValuePair& key_value: result )
{
gtsam::Pose3 pose = key_value.value.cast<gtsam::Pose3>();
gtsam::Point3 p = pose.translation();
gtsam::Quaternion q = pose.rotation().toQuaternion();
fout<<"VERTEX_SE3:QUAT "<<key_value.key<<" "
<<p.x() <<" "<<p.y() <<" "<<p.z() <<" "
<<q.x()<<" "<<q.y()<<" "<<q.z()<<" "<<q.w()<<" "<<endl;
}
// 边咯
for ( gtsam::NonlinearFactor::shared_ptr factor: *graph )
{
gtsam::BetweenFactor<gtsam::Pose3>::shared_ptr f = dynamic_pointer_cast<gtsam::BetweenFactor<gtsam::Pose3>>( factor );
if ( f )
{
gtsam::SharedNoiseModel model = f->noiseModel();
gtsam::noiseModel::Gaussian::shared_ptr gaussianModel = dynamic_pointer_cast<gtsam::noiseModel::Gaussian>( model );
if ( gaussianModel )
{
// write the edge information
gtsam::Matrix info = gaussianModel->R().transpose() * gaussianModel->R();
gtsam::Pose3 pose = f->measured();
gtsam::Point3 p = pose.translation();
gtsam::Quaternion q = pose.rotation().toQuaternion();
fout<<"EDGE_SE3:QUAT "<<f->key1()<<" "<<f->key2()<<" "
<<p.x() <<" "<<p.y() <<" "<<p.z() <<" "
<<q.x()<<" "<<q.y()<<" "<<q.z()<<" "<<q.w()<<" ";
gtsam::Matrix infoG2o = gtsam::I_6x6;
infoG2o.block(0,0,3,3) = info.block(3,3,3,3); // cov translation
infoG2o.block(3,3,3,3) = info.block(0,0,3,3); // cov rotation
infoG2o.block(0,3,3,3) = info.block(0,3,3,3); // off diagonal
infoG2o.block(3,0,3,3) = info.block(3,0,3,3); // off diagonal
for ( int i=0; i<6; i++ )
for ( int j=i; j<6; j++ )
{
fout<<infoG2o(i,j)<<" ";
}
fout<<endl;
}
}
}
fout.close();
cout<<"done."<<endl;
}