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/* ----------------------------------------------------------------------------
* 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>
// 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 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 = 45400; //3541
// Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
bool useSmartProjectionFactor = true;
bool useTriangulation = true;
bool useLM = true;
int landmarkFirstOrderingMethod = 2; // 0 - COLAMD, 1 - landmark first, poses from smart factor, 2 - landmark first through 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> views;
std::vector<Key> landmarkKeys, cameraPoseKeys;
std::vector<Point2> measurements;
Values values;
SmartFactorToStateMap smartFactorStates;
SmartFactorMap smartFactors;
ProjectionFactorMap projectionFactors;
Values result;
int totalNumMeasurements = 0;
bool optimized = false;
boost::shared_ptr<Ordering> ordering, landmarkFirstOrdering(new Ordering());
// main loop: reads measurements and adds factors (also performs optimization if desired)
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);
// Optimize if have a certain number of poses/landmarks, or we want to do incremental inference
if (currentLandmark != l && (numPoses > maxNumPoses || numLandmarks > maxNumLandmarks || (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 ){
// optimize
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) {
// Add landmark keys first for ordering
BOOST_FOREACH(const Key& key, landmarkKeys) {
landmarkFirstOrdering->push_back(key);
}
// Add COLAMD on pose keys to ordering
//Ordering::iterator oit;
BOOST_FOREACH(const Key& key, *ordering) {
landmarkFirstOrdering->push_back(key);
}
} else if (landmarkFirstOrderingMethod == 2) {
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, *ordering) {
orderingMap.insert( make_pair(key, 2) );
}
*landmarkFirstOrdering = graphProjection.orderingCOLAMDConstrained(orderingMap);
}
if (1||debug) std::cout << "Optimizing landmark first " << landmarkFirstOrdering->size() << std::endl;
optimizeGraphLM(graphProjection, graphProjectionValues, result, landmarkFirstOrdering);
// 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;
}
// add factors
// SMART FACTORS ..
if (useSmartProjectionFactor) {
// Check if landmark exists in mapping
SmartFactorToStateMap::iterator fsit = smartFactorStates.find(L(l));
SmartFactorMap::iterator fit = smartFactors.find(L(l));
if (fsit != smartFactorStates.end() && fit != smartFactors.end()) {
if (debug) fprintf(stderr,"Adding measurement to existing landmark\n");
// Add measurement to smart factor
(*fit).second->add(Point2(uL,v), X(r));
totalNumMeasurements++;
if (debug) (*fit).second->print();
} else {
if (debug) fprintf(stderr,"New landmark (%d,%d)\n", fsit != smartFactorStates.end(), fit != smartFactors.end());
views += X(r);
measurements += Point2(uL,v);
// This is a new landmark, create a new factor and add to mapping
boost::shared_ptr<SmartProjectionFactorState> smartFactorState(new SmartProjectionFactorState());
//SmartFactor::shared_ptr smartFactor(new SmartFactor(views, measurements, pixel_sigma, K));
SmartFactor::shared_ptr smartFactor(new SmartFactor(views, measurements, pixel_sigma, K, KittiRankTolerance, KittiLinThreshold));
smartFactorStates.insert( make_pair(L(l), smartFactorState) );
smartFactors.insert( make_pair(L(l), smartFactor) );
graphSmart.push_back(smartFactor);
numLandmarks++;
//landmarkKeys.push_back( L(l) );
totalNumMeasurements++;
views.clear();
measurements.clear();
}
// 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) );
// Add projection factor to graph
//graphProjection.push_back(projectionFactor);
// We have a new landmark
//numLandmarks++;
//landmarkKeys.push_back( L(l) );
}
// 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
}
}
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 (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);
}
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