curve_fit.cpp File Reference

#include <Eigen/Core>
#include <iostream>
#include "g2o/core/auto_differentiation.h"
#include "g2o/core/base_unary_edge.h"
#include "g2o/core/base_vertex.h"
#include "g2o/core/optimization_algorithm_factory.h"
#include "g2o/core/sparse_optimizer.h"
#include "g2o/stuff/command_args.h"
#include "g2o/stuff/sampler.h"

Include dependency graph for curve_fit.cpp:

Go to the source code of this file.

Classes
class	VertexParams
	the params, a, b, and lambda for a * exp(-lambda * t) + b More...
class	EdgePointOnCurve
	measurement for a point on the curve More...

Functions
	G2O_USE_OPTIMIZATION_LIBRARY (dense)
int	main (int argc, char **argv)

Function Documentation

G2O_USE_OPTIMIZATION_LIBRARY()

G2O_USE_OPTIMIZATION_LIBRARY

(

dense

)

main()

int main	(	int	argc,
		char **	argv
	)

Definition at line 96 of file curve_fit.cpp.

                                {
  int numPoints;
  int maxIterations;
  bool verbose;
  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
  g2o::Sampler::seedRand();
  double a = 2.;
  double b = 0.4;
  double lambda = 0.2;
  Eigen::Vector2d* points = new Eigen::Vector2d[numPoints];
  for (int i = 0; i < numPoints; ++i) {
    double x = g2o::Sampler::uniformRand(0, 10);
    double y = a * exp(-lambda * x) + b;
    // 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;
  }
 
  // setup the solver
  g2o::SparseOptimizer optimizer;
  optimizer.setVerbose(false);
 
  // allocate the solver
  g2o::OptimizationAlgorithmProperty solverProperty;
  optimizer.setAlgorithm(
      g2o::OptimizationAlgorithmFactory::instance()->construct("lm_dense",
                                                               solverProperty));
 
  // build the optimization problem given the points
  // 1. add the parameter vertex
  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
  for (int i = 0; i < numPoints; ++i) {
    EdgePointOnCurve* e = new EdgePointOnCurve;
    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;
}

References g2o::OptimizableGraph::addEdge(), g2o::OptimizableGraph::addVertex(), g2o::BaseVertex< D, T >::estimate(), g2o::Sampler::gaussRand(), g2o::SparseOptimizer::initializeOptimization(), g2o::OptimizationAlgorithmFactory::instance(), g2o::SparseOptimizer::optimize(), g2o::CommandArgs::param(), g2o::CommandArgs::parseArgs(), g2o::Sampler::seedRand(), g2o::SparseOptimizer::setAlgorithm(), g2o::BaseVertex< D, T >::setEstimate(), g2o::OptimizableGraph::Vertex::setId(), g2o::BaseEdge< D, E >::setInformation(), g2o::BaseEdge< D, E >::setMeasurement(), g2o::SparseOptimizer::setVerbose(), g2o::HyperGraph::Edge::setVertex(), and g2o::Sampler::uniformRand().