109 lines
3.2 KiB
C++
109 lines
3.2 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 timeShonanFactor.cpp
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* @brief time ShonanFactor with BAL file
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* @author Frank Dellaert
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* @date 2019
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*/
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#include <gtsam/base/timing.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/linear/NoiseModel.h>
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#include <gtsam/linear/PCGSolver.h>
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#include <gtsam/linear/SubgraphPreconditioner.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearEquality.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/dataset.h>
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#include <gtsam/sfm/ShonanFactor.h>
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#include <iostream>
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#include <random>
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#include <string>
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#include <vector>
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using namespace std;
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using namespace gtsam;
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static SharedNoiseModel gNoiseModel = noiseModel::Unit::Create(2);
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int main(int argc, char* argv[]) {
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// primitive argument parsing:
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if (argc > 3) {
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throw runtime_error("Usage: timeShonanFactor [g2oFile]");
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}
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string g2oFile;
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try {
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if (argc > 1)
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g2oFile = argv[argc - 1];
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else
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g2oFile = findExampleDataFile("sphere_smallnoise.graph");
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} catch (const exception& e) {
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cerr << e.what() << '\n';
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exit(1);
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}
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// Read G2O file
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const auto measurements = parseMeasurements<Rot3>(g2oFile);
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const auto poses = parseVariables<Pose3>(g2oFile);
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// Build graph
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NonlinearFactorGraph graph;
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// graph.add(NonlinearEquality<SOn>(0, SOn::Identity(4)));
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auto priorModel = noiseModel::Isotropic::Sigma(6, 10000);
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graph.add(PriorFactor<SOn>(0, SOn::Identity(4), priorModel));
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auto G = boost::make_shared<Matrix>(SOn::VectorizedGenerators(4));
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for (const auto &m : measurements) {
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const auto &keys = m.keys();
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const Rot3 &Rij = m.measured();
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const auto &model = m.noiseModel();
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graph.emplace_shared<ShonanFactor3>(
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keys[0], keys[1], Rij, 4, model, G);
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}
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std::mt19937 rng(42);
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// Set parameters to be similar to ceres
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LevenbergMarquardtParams params;
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LevenbergMarquardtParams::SetCeresDefaults(¶ms);
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params.setLinearSolverType("MULTIFRONTAL_QR");
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// params.setVerbosityLM("SUMMARY");
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// params.linearSolverType = LevenbergMarquardtParams::Iterative;
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// auto pcg = boost::make_shared<PCGSolverParameters>();
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// pcg->preconditioner_ =
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// boost::make_shared<SubgraphPreconditionerParameters>();
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// boost::make_shared<BlockJacobiPreconditionerParameters>();
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// params.iterativeParams = pcg;
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// Optimize
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for (size_t i = 0; i < 100; i++) {
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gttic_(optimize);
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Values initial;
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initial.insert(0, SOn::Identity(4));
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for (size_t j = 1; j < poses.size(); j++) {
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initial.insert(j, SOn::Random(rng, 4));
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}
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LevenbergMarquardtOptimizer lm(graph, initial, params);
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Values result = lm.optimize();
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cout << "cost = " << graph.error(result) << endl;
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}
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tictoc_finishedIteration_();
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tictoc_print_();
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return 0;
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}
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