93 lines
3.2 KiB
C++
93 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 SFMExample.cpp
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* @brief Solve a structure-from-motion problem from a "Bundle Adjustment in the Large" file
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* @author Frank Dellaert
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*/
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// For an explanation of headers, see SFMExample.cpp
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/slam/GeneralSFMFactor.h>
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#include <gtsam/slam/dataset.h> // for loading BAL datasets !
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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::P;
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// We will be using a projection factor that ties a SFM_Camera to a 3D point.
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// An SFM_Camera is defined in datase.h as a camera with unknown Cal3Bundler calibration
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// and has a total of 9 free parameters
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typedef GeneralSFMFactor<SfmCamera,Point3> MyFactor;
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/* ************************************************************************* */
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int main (int argc, char* argv[]) {
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// Find default file, but if an argument is given, try loading a file
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string filename = findExampleDataFile("dubrovnik-3-7-pre");
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if (argc>1) filename = string(argv[1]);
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// Load the SfM data from file
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SfmData mydata;
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readBAL(filename, mydata);
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cout << boost::format("read %1% tracks on %2% cameras\n") % mydata.number_tracks() % mydata.number_cameras();
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// Create a factor graph
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NonlinearFactorGraph graph;
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// We share *one* noiseModel between all projection factors
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auto noise =
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noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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// Add measurements to the factor graph
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size_t j = 0;
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for(const SfmTrack& track: mydata.tracks) {
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for(const SfmMeasurement& m: track.measurements) {
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size_t i = m.first;
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Point2 uv = m.second;
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graph.emplace_shared<MyFactor>(uv, noise, C(i), P(j)); // note use of shorthand symbols C and P
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}
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j += 1;
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}
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// Add a prior on pose x1. This indirectly specifies where the origin is.
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// and a prior on the position of the first landmark to fix the scale
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graph.addPrior(C(0), mydata.cameras[0], noiseModel::Isotropic::Sigma(9, 0.1));
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graph.addPrior(P(0), mydata.tracks[0].p, noiseModel::Isotropic::Sigma(3, 0.1));
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// Create initial estimate
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Values initial;
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size_t i = 0; j = 0;
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for(const SfmCamera& camera: mydata.cameras) initial.insert(C(i++), camera);
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for(const SfmTrack& track: mydata.tracks) initial.insert(P(j++), track.p);
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/* Optimize the graph and print results */
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Values result;
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try {
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LevenbergMarquardtParams params;
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params.setVerbosity("ERROR");
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LevenbergMarquardtOptimizer lm(graph, initial, params);
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result = lm.optimize();
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} catch (exception& e) {
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cout << e.what();
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}
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cout << "final error: " << graph.error(result) << endl;
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return 0;
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}
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/* ************************************************************************* */
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