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