433 lines
15 KiB
C++
433 lines
15 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file SmartProjectionFactorExample_kitti.cpp
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* @brief Example usage of SmartProjectionFactor using real dataset in a non-batch fashion
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* @date August, 2013
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* @author Zsolt Kira
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*/
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// Use a map to store landmark/smart factor pairs
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#include <gtsam/base/FastMap.h>
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// Both relative poses and recovered trajectory poses will be stored as Pose3 objects
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#include <gtsam/geometry/Pose3.h>
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// Each variable in the system (poses and landmarks) must be identified with a unique key.
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// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
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// Here we will use Symbols
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#include <gtsam/inference/Symbol.h>
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// We want to use iSAM2 to solve the range-SLAM problem incrementally
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#include <gtsam/nonlinear/ISAM2.h>
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// iSAM2 requires as input a set set of new factors to be added stored in a factor graph,
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// and initial guesses for any new variables used in the added factors
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#include <gtsam/nonlinear/Values.h>
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// We will use a non-linear solver to batch-initialize from the first 150 frames
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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// In GTSAM, measurement functions are represented as 'factors'. Several common factors
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// have been provided with the library for solving robotics SLAM problems.
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam_unstable/slam/SmartProjectionFactorsCreator.h>
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#include <gtsam_unstable/slam/GenericProjectionFactorsCreator.h>
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// Standard headers, added last, so we know headers above work on their own
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#include <boost/foreach.hpp>
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#include <boost/assign.hpp>
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#include <boost/assign/std/vector.hpp>
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#include <fstream>
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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using namespace boost::assign;
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namespace NM = gtsam::noiseModel;
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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typedef PriorFactor<Pose3> Pose3Prior;
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typedef SmartProjectionFactorsCreator<Pose3, Point3, Cal3_S2> SmartFactorsCreator;
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typedef GenericProjectionFactorsCreator<Pose3, Point3, Cal3_S2> ProjectionFactorsCreator;
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typedef FastMap<Key, int> OrderingMap;
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bool debug = false;
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//// Helper functions taken from VO code
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// Loaded all pose values into list
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Values::shared_ptr loadPoseValues(const string& filename) {
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Values::shared_ptr values(new Values());
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bool addNoise = false;
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std::cout << "PARAM Noise: " << addNoise << std::endl;
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// Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/10, 0., -M_PI/10), gtsam::Point3(0.5,0.1,0.3));
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Pose3 noise_pose = Pose3(Rot3::ypr(-M_PI/100, 0., -M_PI/100), gtsam::Point3(0.3,0.1,0.3));
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// read in camera poses
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string full_filename = filename;
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ifstream fin;
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fin.open(full_filename.c_str());
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int pose_id;
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while (fin >> pose_id) {
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double pose_matrix[16];
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for (int i = 0; i < 16; i++) {
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fin >> pose_matrix[i];
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}
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if (addNoise) {
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values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)).compose(noise_pose));
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} else {
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values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
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}
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}
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fin.close();
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return values;
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}
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// Load specific pose values that are in key list
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Values::shared_ptr loadPoseValues(const string& filename, list<Key> keys) {
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Values::shared_ptr values(new Values());
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std::list<Key>::iterator kit;
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// read in camera poses
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string full_filename = filename;
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ifstream fin;
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fin.open(full_filename.c_str());
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int pose_id;
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while (fin >> pose_id) {
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double pose_matrix[16];
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for (int i = 0; i < 16; i++) {
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fin >> pose_matrix[i];
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}
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kit = find (keys.begin(), keys.end(), X(pose_id));
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if (kit != keys.end()) {
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//cout << " Adding " << X(pose_id) << endl;
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values->insert(Symbol('x',pose_id), Pose3(Matrix_(4, 4, pose_matrix)));
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}
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}
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fin.close();
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return values;
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}
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// Load calibration info
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Cal3_S2::shared_ptr loadCalibration(const string& filename) {
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string full_filename = filename;
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ifstream fin;
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fin.open(full_filename.c_str());
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// try loading from parent directory as backup
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if(!fin) {
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cerr << "Could not load " << full_filename;
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exit(1);
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}
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double fx, fy, s, u, v, b;
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fin >> fx >> fy >> s >> u >> v >> b;
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fin.close();
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Cal3_S2::shared_ptr K(new Cal3_S2(fx, fy, s, u, v));
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return K;
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}
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// Write key values to file
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void writeValues(string directory_, const Values& values){
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string filename = directory_ + "out_camera_poses.txt";
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ofstream fout;
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fout.open(filename.c_str());
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fout.precision(20);
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// write out camera poses
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BOOST_FOREACH(Values::ConstFiltered<Pose3>::value_type key_value, values.filter<Pose3>()) {
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fout << Symbol(key_value.key).index();
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const gtsam::Matrix& matrix= key_value.value.matrix();
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for (size_t row=0; row < 4; ++row) {
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for (size_t col=0; col < 4; ++col) {
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fout << " " << matrix(row, col);
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}
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}
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fout << endl;
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}
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fout.close();
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if(values.filter<Point3>().size() > 0) {
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// write landmarks
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filename = directory_ + "landmarks.txt";
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fout.open(filename.c_str());
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BOOST_FOREACH(Values::ConstFiltered<Point3>::value_type key_value, values.filter<Point3>()) {
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fout << Symbol(key_value.key).index();
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fout << " " << key_value.value.x();
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fout << " " << key_value.value.y();
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fout << " " << key_value.value.z();
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fout << endl;
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}
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fout.close();
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}
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}
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void optimizeGraphLM(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result, boost::shared_ptr<Ordering> &ordering) {
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// Optimization parameters
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LevenbergMarquardtParams params;
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params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
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params.verbosity = NonlinearOptimizerParams::ERROR;
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params.lambdaInitial = 1;
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params.lambdaFactor = 10;
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// Profile a single iteration
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// params.maxIterations = 1;
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params.maxIterations = 100;
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std::cout << " LM max iterations: " << params.maxIterations << std::endl;
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// // params.relativeErrorTol = 1e-5;
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params.absoluteErrorTol = 1.0;
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params.verbosityLM = LevenbergMarquardtParams::TRYLAMBDA;
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params.verbosity = NonlinearOptimizerParams::ERROR;
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params.linearSolverType = SuccessiveLinearizationParams::MULTIFRONTAL_CHOLESKY;
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cout << "Graph size: " << graph.size() << endl;
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cout << "Number of variables: " << graphValues->size() << endl;
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std::cout << " OPTIMIZATION " << std::endl;
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std::cout << "\n\n=================================================\n\n";
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if (debug) {
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graph.print("thegraph");
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}
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std::cout << "\n\n=================================================\n\n";
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if (ordering && ordering->size() > 0) {
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if (debug) {
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std::cout << "Have an ordering\n" << std::endl;
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BOOST_FOREACH(const Key& key, *ordering) {
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std::cout << key << " ";
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}
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std::cout << std::endl;
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}
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params.ordering = *ordering;
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//for (int i = 0; i < 3; i++) {
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LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
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gttic_(GenericProjectionFactorExample_kitti);
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result = optimizer.optimize();
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gttoc_(GenericProjectionFactorExample_kitti);
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tictoc_finishedIteration_();
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//}
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} else {
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std::cout << "Using COLAMD ordering\n" << std::endl;
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//boost::shared_ptr<Ordering> ordering2(new Ordering()); ordering = ordering2;
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//for (int i = 0; i < 3; i++) {
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LevenbergMarquardtOptimizer optimizer(graph, *graphValues, params);
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params.ordering = Ordering::COLAMD(graph);
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gttic_(SmartProjectionFactorExample_kitti);
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result = optimizer.optimize();
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gttoc_(SmartProjectionFactorExample_kitti);
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tictoc_finishedIteration_();
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//}
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//*ordering = params.ordering;
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if (params.ordering) {
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std::cout << "Graph size: " << graph.size() << " ORdering: " << params.ordering->size() << std::endl;
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ordering = boost::make_shared<Ordering>(*(new Ordering()));
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*ordering = *params.ordering;
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} else {
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std::cout << "WARNING: NULL ordering!" << std::endl;
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}
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}
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}
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void optimizeGraphGN(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
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GaussNewtonParams params;
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//params.maxIterations = 1;
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params.verbosity = NonlinearOptimizerParams::DELTA;
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GaussNewtonOptimizer optimizer(graph, *graphValues, params);
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gttic_(SmartProjectionFactorExample_kitti);
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result = optimizer.optimize();
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gttoc_(SmartProjectionFactorExample_kitti);
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tictoc_finishedIteration_();
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}
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void optimizeGraphISAM2(NonlinearFactorGraph &graph, gtsam::Values::shared_ptr graphValues, Values &result) {
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ISAM2 isam;
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gttic_(SmartProjectionFactorExample_kitti);
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isam.update(graph, *graphValues);
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result = isam.calculateEstimate();
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gttoc_(SmartProjectionFactorExample_kitti);
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tictoc_finishedIteration_();
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}
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// main
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int main(int argc, char** argv) {
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unsigned int maxNumLandmarks = 10000; //389007; //100000000; // 309393 // (loop_closure_merged) //37106 //(reduced kitti);
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unsigned int maxNumPoses = 1e+6;
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// Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
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bool useSmartProjectionFactor = false;
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bool useLM = true;
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double KittiLinThreshold = -1.0; // 0.005; //
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double KittiRankTolerance = 1.0;
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bool incrementalFlag = false;
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int optSkip = 200; // we optimize the graph every optSkip poses
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std::cout << "PARAM SmartFactor: " << useSmartProjectionFactor << std::endl;
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std::cout << "PARAM LM: " << useLM << std::endl;
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std::cout << "PARAM KittiLinThreshold (negative is disabled): " << KittiLinThreshold << std::endl;
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// Get home directory and dataset
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string HOME = getenv("HOME");
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//string input_dir = HOME + "/data/KITTI_00_200/";
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string input_dir = HOME + "/data/kitti/loop_closures_merged/"; // 399997 landmarks, 4541 poses
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//string input_dir = HOME + "/data/kitti_00_full_dirty/";
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static SharedNoiseModel pixel_sigma(noiseModel::Unit::Create(2));
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NonlinearFactorGraph graphSmart, graphProjection;
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// Load calibration
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boost::shared_ptr<Cal3_S2> K = loadCalibration(input_dir+"calibration.txt");
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K->print("Calibration");
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// Read in kitti dataset
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ifstream fin;
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fin.open((input_dir+"stereo_factors.txt").c_str());
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if(!fin) {
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cerr << "Could not open stereo_factors.txt" << endl;
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exit(1);
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}
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// Load all values
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gtsam::Values::shared_ptr graphSmartValues(new gtsam::Values());
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gtsam::Values::shared_ptr graphProjectionValues(new gtsam::Values());
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gtsam::Values::shared_ptr loadedValues = loadPoseValues(input_dir+"camera_poses.txt");
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// read all measurements tracked by VO stereo
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cout << "Loading stereo_factors.txt" << endl;
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unsigned int count = 0;
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Key currentLandmark = 0;
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unsigned int numLandmarks = 0, numPoses = 0;
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Key r, l;
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double uL, uR, v, x, y, z;
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std::vector<Key> landmarkKeys, cameraPoseKeys;
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Values values;
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Values result;
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bool optimized = false;
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boost::shared_ptr<Ordering> ordering(new Ordering());
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bool breakingCondition;
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SmartFactorsCreator smartCreator(pixel_sigma, K, KittiRankTolerance, KittiLinThreshold);
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ProjectionFactorsCreator projectionCreator(pixel_sigma, K);
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// main loop: reads measurements and adds factors (also performs optimization if desired)
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// r >> pose id
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// l >> landmark id
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// (uL >> uR) >> measurement (xaxis pixel measurement in left and right camera - since we do monocular, we only use uL)
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// v >> measurement (yaxis pixel measurement)
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// (x >> y >> z) 3D initialization for the landmark (not used in this code)
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while (fin >> r >> l >> uL >> uR >> v >> x >> y >> z) {
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if (debug) fprintf(stderr,"Landmark %ld\n", l);
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if (debug) fprintf(stderr,"Line %d: %d landmarks, (max landmarks %d), %d poses, max poses %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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// 1: add values and factors to the graph
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// 1.1: add factors
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// SMART FACTORS ..
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if (useSmartProjectionFactor) {
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smartCreator.add(L(l), X(r), Point2(uL,v), graphSmart);
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numLandmarks = smartCreator.getNumLandmarks();
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// Add initial pose value if pose does not exist
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if (!graphSmartValues->exists<Pose3>(X(r)) && loadedValues->exists<Pose3>(X(r))) {
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graphSmartValues->insert(X(r), loadedValues->at<Pose3>(X(r)));
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numPoses++;
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optimized = false;
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}
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} else {
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// or STANDARD PROJECTION FACTORS
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projectionCreator.add(L(l), X(r), Point2(uL,v), graphProjection);
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numLandmarks = projectionCreator.getNumLandmarks();
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optimized = false;
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}
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breakingCondition = currentLandmark != l && (numPoses > maxNumPoses || numLandmarks > maxNumLandmarks);
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// Optimize if have a certain number of poses/landmarks, or we want to do incremental inference
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if (breakingCondition || (incrementalFlag && !optimized && ((numPoses+1) % optSkip)==0) ) {
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if (debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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if (debug) cout << "Adding triangulated landmarks, graph size: " << graphProjection.size() << endl;
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if (useSmartProjectionFactor == false) {
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projectionCreator.update(graphProjection, loadedValues, graphProjectionValues);
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ordering = projectionCreator.getOrdering();
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}
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if (debug) cout << "Adding triangulated landmarks, graph size after: " << graphProjection.size() << endl;
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if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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if (useSmartProjectionFactor) {
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if (useLM)
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optimizeGraphLM(graphSmart, graphSmartValues, result, ordering);
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else
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optimizeGraphISAM2(graphSmart, graphSmartValues, result);
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} else {
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if (useLM)
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optimizeGraphLM(graphProjection, graphProjectionValues, result, ordering);
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else
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optimizeGraphISAM2(graphSmart, graphSmartValues, result);
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}
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if(incrementalFlag) *graphSmartValues = result; // we use optimized solution as initial guess for the next one
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optimized = true;
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}
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if (debug) fprintf(stderr,"%d %d\n", count, maxNumLandmarks);
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if (debug) cout << "CurrentLandmark " << currentLandmark << " Landmark " << l << std::endl;
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if (1||debug) fprintf(stderr,"%d: %d, %d > %d, %d > %d\n", count, currentLandmark != l, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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if(breakingCondition){ // reached desired number of landmarks/poses
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break;
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}
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currentLandmark = l;
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count++;
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if(count==100000) {
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cout << "Loading graph smart... " << graphSmart.size() << endl;
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cout << "Loading graph projection... " << graphProjection.size() << endl;
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}
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} // end of while
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if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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cout << "===================================================" << endl;
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//graphSmartValues->print("before optimization ");
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//result.print("results of kitti optimization ");
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tictoc_print_();
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cout << "===================================================" << endl;
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writeValues("./", result);
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if (1||debug) fprintf(stderr,"%d: %d > %d, %d > %d\n", count, numLandmarks, maxNumLandmarks, numPoses, maxNumPoses);
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exit(0);
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}
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