gtsam/gtsam_unstable/examples/SmartProjectionFactorExample_kitti_nonbatch.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 SmartProjectionFactorExample_kitti.cpp
 * @brief Example usage of SmartProjectionFactor using real dataset in a non-batch fashion
 * @date August, 2013
 * @author Zsolt Kira
 */

// Use a map to store landmark/smart factor pairs
#include <gtsam/base/FastMap.h>

// Both relative poses and recovered trajectory poses will be stored as Pose3 objects
#include <gtsam/geometry/Pose3.h>

// Each variable in the system (poses and landmarks) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use Symbols
#include <gtsam/inference/Symbol.h>

// We want to use iSAM2 to solve the range-SLAM problem incrementally
#include <gtsam/nonlinear/ISAM2.h>

// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
// and initial guesses for any new variables used in the added factors
#include <gtsam/nonlinear/Values.h>

// We will use a non-linear solver to batch-initialize from the first 150 frames
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>

// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics SLAM problems.
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/ProjectionFactor.h>
#include <gtsam_unstable/slam/SmartProjectionFactor.h>
#include <gtsam_unstable/slam/SmartProjectionFactorsCreator.h>

// Standard headers, added last, so we know headers above work on their own
#include <boost/foreach.hpp>
#include <boost/assign.hpp>
#include <boost/assign/std/vector.hpp>
#include <fstream>
#include <iostream>

using namespace std;
using namespace gtsam;
using namespace boost::assign;
namespace NM = gtsam::noiseModel;

using symbol_shorthand::X;
using symbol_shorthand::L;

typedef PriorFactor<Pose3> Pose3Prior;
typedef SmartProjectionFactor<Pose3, Point3, Cal3_S2> SmartFactor;
typedef SmartProjectionFactorsCreator<Pose3, Point3, Cal3_S2> SmartFactorsCreator;
typedef GenericProjectionFactor<Pose3, Point3, Cal3_S2> ProjectionFactor;
typedef FastMap<Key, boost::shared_ptr<SmartProjectionFactorState> > SmartFactorToStateMap;
typedef FastMap<Key, boost::shared_ptr<SmartFactor> > SmartFactorMap;
typedef FastMap<Key, std::vector<boost::shared_ptr<ProjectionFactor> > > ProjectionFactorMap;
typedef FastMap<Key, int> OrderingMap;

bool debug = false;

//// Helper functions taken from VO code
// Loaded all pose values into list
Values::shared_ptr loadPoseValues(const string& filename) {

  Values::shared_ptr values(new Values());
  bool addNoise = false;
  std::cout << "PARAM Noise: " << addNoise << std::endl; 
  // Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
  Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/100, 0., -M_PI/100), gtsam::Point3(0.3,0.1,0.3));

  // read in camera poses
  string full_filename = filename;
  ifstream fin;
  fin.open(full_filename.c_str());

  int pose_id;
  while (fin >> pose_id) {
    double pose_matrix[16];
    for (int i = 0; i < 16; i++) {
      fin >> pose_matrix[i];
    }

    if (addNoise) {
      values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)).compose(noise_pose));
    } else {
      values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
    }
  }

  fin.close();
  return values;
}

// Load specific pose values that are in key list
Values::shared_ptr loadPoseValues(const string& filename, list<Key> keys) {
  Values::shared_ptr values(new Values());
  std::list<Key>::iterator kit;

  // read in camera poses
  string full_filename = filename;
  ifstream fin;
  fin.open(full_filename.c_str());

  int pose_id;
  while (fin >> pose_id) {
    double pose_matrix[16];
    for (int i = 0; i < 16; i++) {
      fin >> pose_matrix[i];
    }
    kit = find (keys.begin(), keys.end(), X(pose_id));
    if (kit != keys.end()) {
      //cout << " Adding " << X(pose_id) << endl;
      values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
    }
  }

  fin.close();
  return values;
}

// Load calibration info
Cal3_S2::shared_ptr loadCalibration(const string& filename) {
  string full_filename = filename;
  ifstream fin;
  fin.open(full_filename.c_str());

  // try loading from parent directory as backup
  if(!fin) {
    cerr << "Could not load " << full_filename;
    exit(1);
  }

  double fx, fy, s, u, v, b;
  fin >> fx >> fy >> s >> u >> v >> b;
  fin.close();

  Cal3_S2::shared_ptr K(new Cal3_S2(fx, fy, s, u, v));
  return K;
}

void writeValues(string directory_, const Values& values){
  string filename = directory_ + "out_camera_poses.txt";
  ofstream fout;
  fout.open(filename.c_str());
  fout.precision(20);

  // write out camera poses
  BOOST_FOREACH(Values::ConstFiltered<Pose3>::value_type key_value, values.filter<Pose3>()) {
    fout << Symbol(key_value.key).index();
    const gtsam::Matrix& matrix= key_value.value.matrix();
    for (size_t row=0; row < 4; ++row) {
      for (size_t col=0; col < 4; ++col) {
        fout << " " << matrix(row, col);
      }
    }
    fout << endl;
  }
  fout.close();

  if(values.filter<Point3>().size() > 0) {
    // write landmarks
    filename = directory_ + "landmarks.txt";
    fout.open(filename.c_str());

    BOOST_FOREACH(Values::ConstFiltered<Point3>::value_type key_value, values.filter<Point3>()) {
      fout << Symbol(key_value.key).index();
      fout << " " << key_value.value.x();
      fout << " " << key_value.value.y();
      fout << " " << key_value.value.z();
      fout << endl;
    }
    fout.close();

  }
}

void addTriangulatedLandmarks(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr loadedValues,
    gtsam::Values::shared_ptr graphValues, boost::shared_ptr<Cal3_S2> K, ProjectionFactorMap &projectionFactors,
    vector<Key> &cameraPoseKeys, vector<Key> &landmarkKeys) {
  std::vector<boost::shared_ptr<ProjectionFactor> > projectionFactorVector;
  std::vector<boost::shared_ptr<ProjectionFactor> >::iterator vfit;
  Point3 point;
  Pose3 cameraPose;

  ProjectionFactorMap::iterator pfit;

  if (debug)  graphValues->print("graphValues \n");
  if (debug) std::cout << " # END VALUES: " << std::endl;

  // Iterate through all landmarks
  if (debug) std::cout << " PROJECTION FACTOR GROUPED: " << projectionFactors.size();
  int numProjectionFactors = 0;
  int numProjectionFactorsAdded = 0;
  int numFailures = 0;
  for (pfit = projectionFactors.begin(); pfit != projectionFactors.end(); pfit++) {

    projectionFactorVector = (*pfit).second;

    std::vector<Pose3> cameraPoses;
    std::vector<Point2> measured;

    // Iterate through projection factors
    for (vfit = projectionFactorVector.begin(); vfit != projectionFactorVector.end(); vfit++) {
      numProjectionFactors++;

      if (debug) std::cout << "ProjectionFactor: " << std::endl;
      if (debug) (*vfit)->print("ProjectionFactor");

      // Iterate through poses
      cameraPoses.push_back( loadedValues->at<Pose3>((*vfit)->key1() ) );
      measured.push_back( (*vfit)->measured() );
    }

    // Triangulate landmark based on set of poses and measurements
    if (debug) std::cout << "Triangulating: " << std::endl;
    try {
      point = triangulatePoint3(cameraPoses, measured, *K);
      if (debug) std::cout << "Triangulation succeeded: " << point << std::endl;
    } catch( TriangulationUnderconstrainedException& e) {
      if (debug) std::cout << "Triangulation failed because of unconstrained exception" << std::endl;
      if (debug) {
        BOOST_FOREACH(const Pose3& pose, cameraPoses) {
          std::cout << " Pose: " << pose << std::endl;
        }
      }
      numFailures++;
      continue;
    } catch( TriangulationCheiralityException& e) {
      if (debug) std::cout << "Triangulation failed because of unconstrained exception" << std::endl;
      if (debug) {
        std::cout << "Triangulation failed because of cheirality exception" << std::endl;
        BOOST_FOREACH(const Pose3& pose, cameraPoses) {
          std::cout << " Pose: " << pose << std::endl;
        }
      }
      numFailures++;
      continue;
    }

    // Add projection factors and pose values
    for (vfit = projectionFactorVector.begin(); vfit != projectionFactorVector.end(); vfit++) {
      numProjectionFactorsAdded++;
      if (debug) std::cout << "Adding factor " << std::endl;
      if (debug) (*vfit)->print("Projection Factor");
      graph.push_back( (*vfit) );

      if (!graphValues->exists<Pose3>( (*vfit)->key1()) && loadedValues->exists<Pose3>((*vfit)->key1())) {
        graphValues->insert((*vfit)->key1(), loadedValues->at<Pose3>((*vfit)->key1()));
        cameraPoseKeys.push_back( (*vfit)->key1() );
      }
    }

    // Add landmark value
    if (debug) std::cout << "Adding value " << std::endl;
    graphValues->insert( projectionFactorVector[0]->key2(), point); // add point;
    landmarkKeys.push_back( projectionFactorVector[0]->key2() );

  }
  if (1||debug) std::cout << " # PROJECTION FACTORS CALCULATED: " << numProjectionFactors;
  if (1||debug) std::cout << " # PROJECTION FACTORS ADDED: " << numProjectionFactorsAdded;
  if (1||debug) std::cout << " # FAILURES: " << numFailures;
}

void optimizeGraphLM(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result, boost::shared_ptr<Ordering> &ordering) {
  // Optimization parameters
  LevenbergMarquardtParams params;
  params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
  params.verbosity = NonlinearOptimizerParams::ERROR;
  params.lambdaInitial = 1;
  params.lambdaFactor = 10;
  // Profile a single iteration
//  params.maxIterations = 1;
  params.maxIterations = 100;
  std::cout << " LM max iterations: " << params.maxIterations << std::endl;
  // // params.relativeErrorTol = 1e-5;
  params.absoluteErrorTol = 1.0;
  params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
  params.verbosity = NonlinearOptimizerParams::ERROR;
  params.linearSolverType = SuccessiveLinearizationParams::MULTIFRONTAL_CHOLESKY;

  cout << "Graph size: " << graph.size() << endl;
  cout << "Number of variables: " << graphValues->size() << endl;
  std::cout << " OPTIMIZATION " << std::endl;

  std::cout << "\n\n=================================================\n\n";
  if (debug) {
    graph.print("thegraph");
  }
  std::cout << "\n\n=================================================\n\n";

  if (ordering && ordering->size() > 0) {
    if (debug) {
      std::cout << "Have an ordering\n" << std::endl;
      BOOST_FOREACH(const Key& key, *ordering) {
        std::cout << key << " ";
      }
      std::cout << std::endl;
    }

    params.ordering = *ordering;

    //for (int i = 0; i < 3; i++) {
      LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
      gttic_(GenericProjectionFactorExample_kitti);
      result = optimizer.optimize();
      gttoc_(GenericProjectionFactorExample_kitti);
      tictoc_finishedIteration_();
    //}
  } else {
    std::cout << "Using COLAMD ordering\n" << std::endl;
    //boost::shared_ptr<Ordering> ordering2(new Ordering()); ordering = ordering2;

    //for (int i = 0; i < 3; i++) {
      LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
      // params = optimizer.ensureHasOrdering(params, graph);
      gttic_(SmartProjectionFactorExample_kitti);
      result = optimizer.optimize();
      gttoc_(SmartProjectionFactorExample_kitti);
      tictoc_finishedIteration_();
    //}

    //*ordering = params.ordering;
    std::cout << "Graph size: " << graph.size() << " ORdering: " << params.ordering->size() << std::endl;
    ordering = boost::make_shared<Ordering>(*(new Ordering()));
    *ordering = *params.ordering;

  }
}

void optimizeGraphGN(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
  GaussNewtonParams params;
  //params.maxIterations = 1;
  params.verbosity = NonlinearOptimizerParams::DELTA;

  GaussNewtonOptimizer optimizer(graph, *graphValues, params);
  gttic_(SmartProjectionFactorExample_kitti);
  result = optimizer.optimize();
  gttoc_(SmartProjectionFactorExample_kitti);
  tictoc_finishedIteration_();

}

void optimizeGraphISAM2(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
  ISAM2 isam;
  gttic_(SmartProjectionFactorExample_kitti);
  isam.update(graph, *graphValues);
  result = isam.calculateEstimate();
  gttoc_(SmartProjectionFactorExample_kitti);
  tictoc_finishedIteration_();
}

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

  unsigned int maxNumLandmarks = 389007; //100000000; // 309393 // (loop_closure_merged) //37106 //(reduced kitti);
  unsigned int maxNumPoses = 1e+6;

  // Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
  bool useSmartProjectionFactor = true;
  bool useTriangulation = true;
  bool useLM = true; 
  int landmarkFirstOrderingMethod = 1; // 0 - COLAMD, 1 - landmark first, then COLAMD on poses (constrained ordering)

  double KittiLinThreshold = -1.0; // 0.005; //
  double KittiRankTolerance = 1.0;

  bool incrementalFlag = false;
  int optSkip = 200; // we optimize the graph every optSkip poses

  std::cout << "PARAM SmartFactor: " << useSmartProjectionFactor << std::endl;
  std::cout << "PARAM Triangulation: " << useTriangulation << std::endl;
  std::cout << "PARAM LM: " << useLM << std::endl;
  std::cout << "PARAM KittiLinThreshold (negative is disabled): " << KittiLinThreshold << std::endl;

  // Get home directory and dataset
  string HOME = getenv("HOME");
  //string input_dir = HOME + "/data/KITTI_00_200/";
  string input_dir = HOME + "/data/kitti/loop_closures_merged/"; // 399997 landmarks, 4541 poses
  //string input_dir = HOME + "/data/kitti_00_full_dirty/";

  static SharedNoiseModel pixel_sigma(noiseModel::Unit::Create(2));
  static SharedNoiseModel prior_model(noiseModel::Diagonal::Sigmas(Vector_(6, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01)));
  //static SharedNoiseModel prior_model(noiseModel::Diagonal::Sigmas(Vector_(6, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9, 1e-9)));
  NonlinearFactorGraph graphSmart, graphProjection;

  // Load calibration
  //Cal3_S2::shared_ptr K(new Cal3_S2(718.856, 718.856, 0.0, 607.1928, 185.2157));
  boost::shared_ptr<Cal3_S2> K = loadCalibration(input_dir+"calibration.txt");
  K->print("Calibration");

  // Read in kitti dataset
  ifstream fin;
  fin.open((input_dir+"stereo_factors.txt").c_str());
  if(!fin) {
    cerr << "Could not open stereo_factors.txt" << endl;
    exit(1);
  }

  // Load all values, add priors
  gtsam::Values::shared_ptr graphSmartValues(new gtsam::Values());
  gtsam::Values::shared_ptr graphProjectionValues(new gtsam::Values());
  gtsam::Values::shared_ptr loadedValues = loadPoseValues(input_dir+"camera_poses.txt");
  //graph.push_back(Pose3Prior(X(0),loadedValues->at<Pose3>(X(0)), prior_model));
  //graph.push_back(Pose3Prior(X(1),loadedValues->at<Pose3>(X(1)), prior_model));

  // read all measurements tracked by VO stereo
  cout << "Loading stereo_factors.txt" << endl;
  unsigned int count = 0;
  Key currentLandmark = 0;
  unsigned int numLandmarks = 0, numPoses = 0;
  Key r, l;
  double uL, uR, v, x, y, z;
  std::vector<Key> landmarkKeys, cameraPoseKeys;
  std::vector<Key> views;
  std::vector<Point2> measurements;
  Values values;
  ProjectionFactorMap projectionFactors;
  Values result;
  int totalNumMeasurements = 0;
  bool optimized = false;
  boost::shared_ptr<Ordering> ordering(new Ordering());
  SmartFactorsCreator smartCreator(pixel_sigma, K, KittiRankTolerance, KittiLinThreshold);

  // main loop: reads measurements and adds factors (also performs optimization if desired)
  // r >> pose id
  // l >> landmark id
  // (uL >> uR) >> measurement (xaxis pixel measurement in left and right camera - since we do monocular, we only use uL)
  // v >> measurement (yaxis pixel measurement)
  // (x >> y >> z) 3D initialization for the landmark (not used in this code)
  while (fin >> r >> l >> uL >> uR >> v >> x >> y >> z) {
    if (debug) fprintf(stderr,"Landmark %ld\n", l);
    if (debug) fprintf(stderr,"Line %d: %d landmarks, (max landmarks %d), %d poses, max poses %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);

    // 1: add values and factors to the graph
    // 1.1: add factors
    // SMART FACTORS ..
    if (useSmartProjectionFactor) {

      smartCreator.add(L(l), X(r), Point2(uL,v), graphSmart);

      // Add initial pose value if pose does not exist
      if (!graphSmartValues->exists<Pose3>(X(r)) && loadedValues->exists<Pose3>(X(r))) {
        graphSmartValues->insert(X(r), loadedValues->at<Pose3>(X(r)));
        numPoses++;
        optimized = false;
      }

    } else {
      // or STANDARD PROJECTION FACTORS
      // Create projection factor
      ProjectionFactor::shared_ptr projectionFactor(new ProjectionFactor(Point2(uL,v), pixel_sigma, X(r), L(l), K));

      // Check if landmark exists in mapping
      ProjectionFactorMap::iterator pfit = projectionFactors.find(L(l));
      if (pfit != projectionFactors.end()) {
        if (debug) fprintf(stderr,"Adding measurement to existing landmark\n");

        // Add projection factor to list of projection factors associated with this landmark
        (*pfit).second.push_back(projectionFactor);

      } else {
        if (debug) fprintf(stderr,"New landmark (%d)\n", pfit != projectionFactors.end());

        // Create a new vector of projection factors
        std::vector<ProjectionFactor::shared_ptr> projectionFactorVector;
        projectionFactorVector.push_back(projectionFactor);

        // Insert projection factor to NEW list of projection factors associated with this landmark
        projectionFactors.insert( make_pair(L(l), projectionFactorVector) );


        optimized = false; // TODO


        // Add projection factor to graph
        //graphProjection.push_back(projectionFactor);

        // We have a new landmark
        //numLandmarks++;
        //landmarkKeys.push_back( L(l) );
      }

      if (!useTriangulation) {
        cerr << "Deprecated use of -useTriangulation- flag" << endl;
      }
//      // Add landmark if triangulation is not being used to initialize them
//      if (!useTriangulation) {
//        // For projection factor, landmarks positions are used, but have to be transformed to world coordinates
//        if (graphProjectionValues->exists<Point3>(L(l)) == boost::none) {
//          Pose3 camera = loadedValues->at<Pose3>(X(r));
//          Point3 worldPoint = camera.transform_from(Point3(x, y, z));
//          graphProjectionValues->insert(L(l), worldPoint); // add point;
//        }
//
//        // Add initial pose value if pose does not exist
//        // Only do this if triangulation is not used.  Otherwise, it depends what projection factors are added
//        // based on triangulation success
//        if (!graphProjectionValues->exists<Pose3>(X(r)) && loadedValues->exists<Pose3>(X(r))) {
//          graphProjectionValues->insert(X(r), loadedValues->at<Pose3>(X(r)));
//          cameraPoseKeys.push_back( X(r) );
//          //numPoses++;
//        }
//
//        // Add projection factor to graph
//        graphProjection.push_back(projectionFactor);
//
//      }else {
//        // Alternatively: Triangulate similar to how SmartProjectionFactor does it
//        // We only do this at the end, when all of the camera poses are available
//        // Note we do not add anything to the graph until then, since in some cases
//        // of triangulation failure we cannot add the landmark to the graph
//      }

    }

    // Optimize if have a certain number of poses/landmarks, or we want to do incremental inference
    if (incrementalFlag && !optimized && ((numPoses+1) % optSkip)==0) {

      if (debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
      if (debug) cout << "Adding triangulated landmarks, graph size: " << graphProjection.size() << endl;

      //if (useSmartProjectionFactor == false && useTriangulation) {
      // addTriangulatedLandmarks(graphProjection, loadedValues, graphProjectionValues, K, projectionFactors, cameraPoseKeys, landmarkKeys);
      //}

      if (debug) cout << "Adding triangulated landmarks, graph size after: " << graphProjection.size() << endl;
      if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);

      // Optimize every optSkip poses if we want to do incremental inference
      if (incrementalFlag && !optimized && ((numPoses+1) % optSkip)==0 ){
        if (useSmartProjectionFactor == false && useTriangulation) {
          addTriangulatedLandmarks(graphSmart, loadedValues, graphSmartValues, K, projectionFactors, cameraPoseKeys, landmarkKeys);
        }
        if (useLM)
          optimizeGraphLM(graphSmart, graphSmartValues, result, ordering);
        else
          optimizeGraphISAM2(graphSmart, graphSmartValues, result);

        if(incrementalFlag) *graphSmartValues = result; // we use optimized solution as initial guess for the next one

        optimized = true;
        if (1||debug) std::cout << "Landmark Keys: " << landmarkKeys.size() << " Pose Keys: " << cameraPoseKeys.size() << std::endl;
        if (1||debug) std::cout << "Pose ordering: " << ordering->size() << std::endl;

        if (landmarkFirstOrderingMethod == 1) {
          OrderingMap orderingMap;
          // Add landmark keys first for ordering
          BOOST_FOREACH(const Key& key, landmarkKeys) {
            orderingMap.insert( make_pair(key, 1) );
          }
          //Ordering::iterator oit;
          BOOST_FOREACH(const Key& key, cameraPoseKeys) {
            orderingMap.insert( make_pair(key, 2) );
          }
          *ordering = graphProjection.orderingCOLAMDConstrained(orderingMap);
        }

        if (1||debug) std::cout << "Optimizing landmark first " << ordering->size() << std::endl;
        optimizeGraphLM(graphSmart, graphSmartValues, result, ordering);

        // Only process first N measurements (for development/debugging)
        if ( (numPoses > maxNumPoses || numLandmarks > maxNumLandmarks) ) {
          if (debug) fprintf(stderr,"%d: BREAKING %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
          break;
        }
        if(!incrementalFlag) break;
      }



      if (debug) fprintf(stderr,"%d %d\n", count, maxNumLandmarks);
      if (debug) cout << "CurrentLandmark " << currentLandmark << " Landmark " << l << std::endl;

      currentLandmark = l;
      count++;
      if(count==100000) {
        cout << "Loading graph smart... " << graphSmart.size() << endl;
        cout << "Loading graph projection... " << graphProjection.size() << endl;
      }
    }

    if(currentLandmark != l && (numPoses > maxNumPoses || numLandmarks > maxNumLandmarks)){ // reached desired number of landmarks/poses
      break;
    }

  } // end of while

  if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);

  // if we haven't optimized yet
  if (!optimized) {
    if (useSmartProjectionFactor == false && useTriangulation) {
      addTriangulatedLandmarks(graphSmart, loadedValues, graphSmartValues, K, projectionFactors, cameraPoseKeys, landmarkKeys);
    }
    if (useLM)
      optimizeGraphLM(graphSmart, graphSmartValues, result, ordering);
    else
      optimizeGraphISAM2(graphSmart, graphSmartValues, result);
    optimized = true;
  }
  if (useSmartProjectionFactor||debug) std::cout << "TOTAL NUM MEASUREMENTS " << totalNumMeasurements;

  cout << "===================================================" << endl;
  //graphSmartValues->print("before optimization ");
  //result.print("results of kitti optimization ");
  tictoc_print_();
  cout << "===================================================" << endl;
  writeValues("./", result);

  if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
  exit(0);
}