112 lines
3.7 KiB
C++
112 lines
3.7 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 SFMExampleExpressions_bal.cpp
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* @brief A structure-from-motion example done with Expressions
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* @author Frank Dellaert
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* @date January 2015
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*/
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/**
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* This is the Expression version of SFMExample
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* See detailed description of headers there, this focuses on explaining the AD part
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*/
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// The two new headers that allow using our Automatic Differentiation Expression framework
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#include <gtsam/slam/expressions.h>
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#include <gtsam/nonlinear/ExpressionFactorGraph.h>
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// Header order is close to far
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#include <gtsam/sfm/SfmData.h> // for loading BAL datasets !
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#include <gtsam/slam/dataset.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/inference/Symbol.h>
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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 namespace noiseModel;
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using symbol_shorthand::C;
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using symbol_shorthand::P;
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// An SfmCamera 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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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 = SfmData::FromBalFile(filename);
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cout << "read " << mydata.numberTracks() << " tracks on " << mydata.numberCameras() << " cameras" << endl;
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// Create a factor graph
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ExpressionFactorGraph graph;
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// Here we don't use a PriorFactor but directly the ExpressionFactor class
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// First, we create an expression to the pose from the first camera
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Expression<SfmCamera> camera0_(C(0));
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// Then, to get its pose:
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Pose3_ pose0_(&SfmCamera::getPose, camera0_);
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// Finally, we say it should be equal to first guess
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graph.addExpressionFactor(pose0_, mydata.cameras[0].pose(),
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noiseModel::Isotropic::Sigma(6, 0.1));
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// similarly, we create a prior on the first point
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Point3_ point0_(P(0));
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graph.addExpressionFactor(point0_, mydata.tracks[0].p,
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noiseModel::Isotropic::Sigma(3, 0.1));
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// We share *one* noiseModel between all projection factors
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auto noise = noiseModel::Isotropic::Sigma(2, 1.0); // one pixel in u and v
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// Simulated measurements from each camera pose, adding them to the factor
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// graph
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size_t j = 0;
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for (const SfmTrack& track : mydata.tracks) {
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// Leaf expression for j^th point
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Point3_ point_('p', j);
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for (const auto& [i, uv] : track.measurements) {
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// Leaf expression for i^th camera
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Expression<SfmCamera> camera_(C(i));
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// Below an expression for the prediction of the measurement:
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Point2_ predict_ = project2<SfmCamera>(camera_, point_);
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// Again, here we use an ExpressionFactor
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graph.addExpressionFactor(predict_, uv, noise);
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}
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j += 1;
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}
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// Create initial estimate
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Values initial;
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size_t i = 0;
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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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