62 lines
2.0 KiB
C++
62 lines
2.0 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 Pose2SLAMExample.cpp
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* @brief A 2D Pose SLAM example that reads input from g2o and uses robust kernels in optimization
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* @date May 15, 2014
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* @author Luca Carlone
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*/
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/inference/Key.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/slam/BetweenFactor.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
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#include <gtsam/nonlinear/Marginals.h>
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#include <gtsam/nonlinear/Values.h>
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#include <fstream>
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#include <sstream>
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using namespace std;
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using namespace gtsam;
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int main(const int argc, const char *argv[]){
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if (argc < 2)
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std::cout << "Please specify: 1st argument: input file (in g2o format) and 2nd argument: output file" << std::endl;
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const string g2oFile = argv[1];
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NonlinearFactorGraph graph;
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Values initial;
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readG2o(g2oFile, graph, initial);
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// Add prior on the pose having index (key) = 0
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NonlinearFactorGraph graphWithPrior = graph;
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noiseModel::Diagonal::shared_ptr priorModel = noiseModel::Diagonal::Variances((Vector(3) << 0.01, 0.01, 0.001));
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graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
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std::cout << "Optimizing the factor graph" << std::endl;
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GaussNewtonOptimizer optimizer(graphWithPrior, initial); // , parameters);
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Values result = optimizer.optimize();
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std::cout << "Optimization complete" << std::endl;
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const string outputFile = argv[2];
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std::cout << "Writing results to file: " << outputFile << std::endl;
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writeG2o(outputFile, graph, result);
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std::cout << "done! " << std::endl;
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return 0;
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}
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