96 lines
2.6 KiB
C++
96 lines
2.6 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 timeSFMBAL.h
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* @brief Common code for timeSFMBAL scripts
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* @author Frank Dellaert
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* @date July 5, 2015
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*/
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#include <gtsam/slam/dataset.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.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/linear/NoiseModel.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/base/timing.h>
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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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using symbol_shorthand::C;
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using symbol_shorthand::K;
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using symbol_shorthand::P;
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static bool gUseSchur = true;
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static SharedNoiseModel gNoiseModel = noiseModel::Unit::Create(2);
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// parse options and read BAL file
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SfM_Data preamble(int argc, char* argv[]) {
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// primitive argument parsing:
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if (argc > 2) {
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if (strcmp(argv[1], "--colamd"))
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gUseSchur = false;
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else
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throw runtime_error("Usage: timeSFMBALxxx [--colamd] [BALfile]");
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}
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// Load BAL file
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SfM_Data db;
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string filename;
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if (argc > 1)
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filename = argv[argc - 1];
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else
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filename = findExampleDataFile("dubrovnik-16-22106-pre");
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bool success = readBAL(filename, db);
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if (!success) throw runtime_error("Could not access file!");
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return db;
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}
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// Create ordering and optimize
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int optimize(const SfM_Data& db, const NonlinearFactorGraph& graph,
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const Values& initial, bool separateCalibration = false) {
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using symbol_shorthand::P;
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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("SEQUENTIAL_CHOLESKY");
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// params.setVerbosityLM("SUMMARY");
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if (gUseSchur) {
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// Create Schur-complement ordering
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Ordering ordering;
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for (size_t j = 0; j < db.number_tracks(); j++) ordering.push_back(P(j));
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for (size_t i = 0; i < db.number_cameras(); i++) {
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ordering.push_back(C(i));
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if (separateCalibration) ordering.push_back(K(i));
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}
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params.setOrdering(ordering);
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}
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// Optimize
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{
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gttic_(optimize);
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LevenbergMarquardtOptimizer lm(graph, initial, params);
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Values result = lm.optimize();
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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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