65 lines
2.0 KiB
C++
65 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_lago.cpp
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* @brief A 2D Pose SLAM example that reads input from g2o, and solve the Pose2 problem
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* using LAGO (Linear Approximation for Graph Optimization). See class lago.h
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* Output is written on a file, in g2o format
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* Syntax for the script is ./Pose2SLAMExample_lago input.g2o output.g2o
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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/slam/lago.h>
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#include <gtsam/slam/dataset.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <fstream>
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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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// Read graph from file
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string g2oFile;
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if (argc < 2)
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g2oFile = "../../examples/Data/noisyToyGraph.txt";
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else
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g2oFile = argv[1];
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NonlinearFactorGraph::shared_ptr graph;
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Values::shared_ptr initial;
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boost::tie(graph, initial) = readG2o(g2oFile);
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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 = //
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noiseModel::Diagonal::Variances((Vector(3) << 1e-6, 1e-6, 1e-8));
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graphWithPrior.add(PriorFactor<Pose2>(0, Pose2(), priorModel));
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graphWithPrior.print();
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std::cout << "Computing LAGO estimate" << std::endl;
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Values estimateLago = lago::initialize(graphWithPrior);
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std::cout << "done!" << std::endl;
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if (argc < 3) {
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estimateLago.print("estimateLago");
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} else {
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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(*graph, estimateLago, outputFile);
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std::cout << "done! " << std::endl;
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}
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return 0;
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}
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