394 lines
15 KiB
C++
394 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 SmartProjectionFactorTesting.cpp
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* @brief Example usage of SmartProjectionFactor using real datasets
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* @date August, 2013
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* @author Luca Carlone
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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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#include <gtsam/geometry/PinholeCamera.h>
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#include <gtsam/geometry/Cal3Bundler.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/slam/dataset.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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#define USE_BUNDLER
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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 FastMap<Key, int> OrderingMap;
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#ifdef USE_BUNDLER
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typedef SmartProjectionFactorsCreator<Pose3, Point3, Cal3Bundler> SmartFactorsCreator;
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typedef GenericProjectionFactorsCreator<Pose3, Point3, Cal3Bundler> ProjectionFactorsCreator;
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#else
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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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#endif
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bool debug = false;
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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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} // end of if on landmarks
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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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if (debug) {
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std::cout << "\n\n=================================================\n\n";
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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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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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std::cout << "Number of outer LM iterations: " << optimizer.state().iterations << std::endl;
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std::cout << "Total number of LM iterations (inner and outer): " << optimizer.getInnerIterations() << std::endl;
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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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// Set to true to use SmartProjectionFactor. Otherwise GenericProjectionFactor will be used
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bool useSmartProjectionFactor = true;
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bool doTriangulation = true; // we read points initial guess from file or we triangulate
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bool useLM = true;
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bool addNoise = false;
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// Smart factors settings
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double linThreshold = -1.0; // negative is disabled
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double rankTolerance = 1.0;
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// Get home directory and default dataset
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string HOME = getenv("HOME");
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string datasetFile = HOME + "/data/SfM/BAL/Ladybug/problem-1031-110968-pre.txt";
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// --------------- READ USER INPUTS (main arguments) ------------------------------------
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if(argc>1){ // if we have any input arguments
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// Arg1: "smart" or "standard" (select if to use smart factors or standard projection factors)
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// Arg2: "triangulation=0" or "triangulation=1" (select whether to initialize initial guess for points using triangulation)
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// Arg3: name of the dataset, e.g., /home/aspn/data/SfM/BAL/Ladybug/problem-1031-110968-pre.txt
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string useSmartArgument = argv[1];
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string useTriangulationArgument = argv[2];
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datasetFile = argv[3];
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if(useSmartArgument.compare("smart")==0){
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useSmartProjectionFactor=true;
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} else{
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if(useSmartArgument.compare("standard")==0){
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useSmartProjectionFactor=false;
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}else{
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cout << "Selected wrong option for input argument - useSmartProjectionFactor" << endl;
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exit(1);
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}
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}
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if(useTriangulationArgument.compare("triangulation=1")==0){
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doTriangulation=true;
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} else{
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if(useTriangulationArgument.compare("triangulation=0")==0){
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doTriangulation=false;
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}else{
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cout << "Selected wrong option for input argument - doTriangulation - not important for SmartFactors" << endl;
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}
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}
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}
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// --------------- PRINT USER's CHOICE ------------------------------------
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std::cout << "- useSmartFactor: " << useSmartProjectionFactor << std::endl;
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std::cout << "- doTriangulation: " << doTriangulation << std::endl;
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std::cout << "- datasetFile: " << datasetFile << std::endl;
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if (linThreshold >= 0)
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std::cout << "PARAM linThreshold (negative is disabled): " << linThreshold << std::endl;
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if(addNoise)
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std::cout << "PARAM Noise: " << addNoise << std::endl;
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// --------------- READ INPUT DATA ----------------------------------------
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SfM_data inputData;
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readBAL(datasetFile, inputData);
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std::cout << "read data from file... " << std::endl;
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// --------------- CREATE FACTOR GRAPH ------------------------------------
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static SharedNoiseModel pixel_sigma(noiseModel::Unit::Create(2));
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boost::shared_ptr<Ordering> ordering(new Ordering());
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NonlinearFactorGraph graphSmart;
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gtsam::Values::shared_ptr graphSmartValues(new gtsam::Values());
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NonlinearFactorGraph graphProjection;
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gtsam::Values::shared_ptr graphProjectionValues(new gtsam::Values());
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#ifdef USE_BUNDLER
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std::vector< boost::shared_ptr<Cal3Bundler> > K_cameras;
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boost::shared_ptr<Cal3Bundler> K(new Cal3Bundler());
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#else
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std::vector< boost::shared_ptr<Cal3_S2> > K_cameras;
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Cal3_S2::shared_ptr K(new Cal3_S2());
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#endif
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SmartFactorsCreator smartCreator(pixel_sigma, K, rankTolerance, linThreshold); // this initial K is not used
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ProjectionFactorsCreator projectionCreator(pixel_sigma, K); // this initial K is not used
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int numLandmarks=0;
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if(debug){
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std::cout << "Constructors for factor creators " << std::endl;
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std::cout << "inputData.number_cameras() " << inputData.number_cameras() << std::endl;
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std::cout << "inputData.number_tracks() " << inputData.number_tracks() << std::endl;
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}
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// Load graph values
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gtsam::Values::shared_ptr loadedValues(new gtsam::Values()); // values we read from file
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for (size_t i = 0; i < inputData.number_cameras(); i++){ // for each camera
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Pose3 cameraPose = inputData.cameras[i].pose();
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if(addNoise){
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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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cameraPose = cameraPose.compose(noise_pose);
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}
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loadedValues->insert(X(i), cameraPose);
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graphSmartValues->insert(X(i), cameraPose);
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}
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if(debug) std::cout << "Initialized values " << std::endl;
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for (size_t j = 0; j < inputData.number_tracks(); j++){ // for each 3D point
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Point3 point = inputData.tracks[j].p;
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loadedValues->insert(L(j), point);
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}
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if(debug) std::cout << "Initialized points " << std::endl;
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// Create the graph
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for (size_t j = 0; j < inputData.number_tracks(); j++){ // for each 3D point
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SfM_Track track = inputData.tracks[j];
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Point3 point = track.p;
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for (size_t k = 0; k < track.number_measurements(); k++){ // for each measurement of the point
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SfM_Measurement measurement = track.measurements[k];
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int i = measurement.first;
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double u = measurement.second.x();
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double v = measurement.second.y();
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boost::shared_ptr<Cal3Bundler> Ki(new Cal3Bundler(inputData.cameras[i].calibration()));
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//boost::shared_ptr<Cal3_S2> Ki(new Cal3_S2());
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// insert data in a format that can be understood
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if (useSmartProjectionFactor) {
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// Use smart factors
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smartCreator.add(L(j), X(i), Point2(u,v), pixel_sigma, Ki, graphSmart);
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numLandmarks = smartCreator.getNumLandmarks();
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} else {
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// or STANDARD PROJECTION FACTORS
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projectionCreator.add(L(j), X(i), Point2(u,v), pixel_sigma, Ki, graphProjection);
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numLandmarks = projectionCreator.getNumLandmarks();
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}
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}
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}
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if(debug) std::cout << "Included measurements in the graph " << std::endl;
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cout << "Number of landmarks " << numLandmarks << endl;
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cout << "Before call to update: ------------------ " << endl;
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cout << "Poses in SmartGraph values: "<< graphSmartValues->size() << endl;
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Values valuesProjPoses = graphProjectionValues->filter<Pose3>();
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cout << "Poses in ProjectionGraph values: "<< valuesProjPoses.size() << endl;
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Values valuesProjPoints = graphProjectionValues->filter<Point3>();
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cout << "Points in ProjectionGraph values: "<< valuesProjPoints.size() << endl;
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cout << "---------------------------------------------------------- " << endl;
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if (!useSmartProjectionFactor) {
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projectionCreator.update(graphProjection, loadedValues, graphProjectionValues, doTriangulation);
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// graphProjectionValues = loadedValues;
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ordering = projectionCreator.getOrdering();
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}
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cout << "After call to update: ------------------ " << endl;
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cout << "Poses in SmartGraph values: "<< graphSmartValues->size() << endl;
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valuesProjPoses = graphProjectionValues->filter<Pose3>();
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cout << "Poses in ProjectionGraph values: "<< valuesProjPoses.size() << endl;
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valuesProjPoints = graphProjectionValues->filter<Point3>();
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cout << "Points in ProjectionGraph values: "<< valuesProjPoints.size() << endl;
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cout << "---------------------------------------------------------- " << endl;
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Values result;
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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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cout << "Final reprojection error (smart): " << graphSmart.error(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(graphProjection, graphProjectionValues, result); // ?
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cout << "Final reprojection error (standard): " << graphProjection.error(result);
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}
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cout << "===================================================" << endl;
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tictoc_print_();
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cout << "===================================================" << endl;
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writeValues("./", result);
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exit(0);
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}
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