gtsam/examples/SFMExample_bal.cpp

/* ----------------------------------------------------------------------------

 * GTSAM Copyright 2010, Georgia Tech Research Corporation,
 * Atlanta, Georgia 30332-0415
 * All Rights Reserved
 * Authors: Frank Dellaert, et al. (see THANKS for the full author list)

 * See LICENSE for the license information

 * -------------------------------------------------------------------------- */

/**
 * @file    SFMExample.cpp
 * @brief   Solve a structure-from-motion problem from a "Bundle Adjustment in the Large" file
 * @author  Frank Dellaert
 */

// For an explanation of headers, see SFMExample.cpp
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/GeneralSFMFactor.h>
#include <gtsam/slam/dataset.h> // for loading BAL datasets !
#include <vector>

using namespace std;
using namespace gtsam;
using symbol_shorthand::C;
using symbol_shorthand::P;

// We will be using a projection factor that ties a SFM_Camera to a 3D point.
// An SFM_Camera is defined in datase.h as a camera with unknown Cal3Bundler calibration
// and has a total of 9 free parameters
typedef GeneralSFMFactor<SfM_Camera,Point3> MyFactor;

/* ************************************************************************* */
int main (int argc, char* argv[]) {

  // Find default file, but if an argument is given, try loading a file
  string filename = findExampleDataFile("dubrovnik-3-7-pre");
  if (argc>1) filename = string(argv[1]);

  // Load the SfM data from file
  SfM_data mydata;
  assert(readBAL(filename, mydata));
  cout << boost::format("read %1% tracks on %2% cameras\n") % mydata.number_tracks() % mydata.number_cameras();

  // Create a factor graph
  NonlinearFactorGraph graph;

  // We share *one* noiseModel between all projection factors
  noiseModel::Isotropic::shared_ptr noise =
      noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v

  // Add measurements to the factor graph
  size_t j = 0;
  BOOST_FOREACH(const SfM_Track& track, mydata.tracks) {
    BOOST_FOREACH(const SfM_Measurement& m, track.measurements) {
      size_t i = m.first;
      Point2 uv = m.second;
      graph.push_back(MyFactor(uv, noise, C(i), P(j))); // note use of shorthand symbols C and P
    }
    j += 1;
  }

  // Add a prior on pose x1. This indirectly specifies where the origin is.
  // and a prior on the position of the first landmark to fix the scale
  graph.push_back(PriorFactor<SfM_Camera>(C(0), mydata.cameras[0],  noiseModel::Isotropic::Sigma(9, 0.1)));
  graph.push_back(PriorFactor<Point3>    (P(0), mydata.tracks[0].p, noiseModel::Isotropic::Sigma(3, 0.1)));

  // Create initial estimate
  Values initial;
  size_t i = 0; j = 0;
  BOOST_FOREACH(const SfM_Camera& camera, mydata.cameras) initial.insert(C(i++), camera);
  BOOST_FOREACH(const SfM_Track& track, mydata.tracks)    initial.insert(P(j++), track.p);

  /* Optimize the graph and print results */
  Values result;
  try {
    LevenbergMarquardtParams params;
    params.setVerbosity("ERROR");
    LevenbergMarquardtOptimizer lm(graph, initial, params);
    result = lm.optimize();
  } catch (exception& e) {
    cout << e.what();
  }
  cout << "final error: " << graph.error(result) << endl;

  return 0;
}
/* ************************************************************************* */