59 lines
2.0 KiB
C++
59 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_graph.cpp
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* @brief Read graph from file and perform GraphSLAM
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* @date June 3, 2012
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* @author Frank Dellaert
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*/
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// For an explanation of headers below, please see Pose2SLAMExample.cpp
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/Marginals.h>
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// This new header allows us to read examples easily from .graph files
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#include <gtsam/slam/dataset.h>
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using namespace std;
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using namespace gtsam;
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int main (int argc, char** argv) {
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// Read File, create graph and initial estimate
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// we are in build/examples, data is in examples/Data
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NonlinearFactorGraph::shared_ptr graph;
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Values::shared_ptr initial;
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SharedDiagonal model = noiseModel::Diagonal::Sigmas((Vector(3) << 0.05, 0.05, 5.0 * M_PI / 180.0).finished());
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string graph_file = findExampleDataFile("w100.graph");
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boost::tie(graph, initial) = load2D(graph_file, model);
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initial->print("Initial estimate:\n");
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// Add a Gaussian prior on first poses
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.01, 0.01, 0.01));
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graph->push_back(PriorFactor<Pose2>(0, priorMean, priorNoise));
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// Single Step Optimization using Levenberg-Marquardt
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Values result = LevenbergMarquardtOptimizer(*graph, *initial).optimize();
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result.print("\nFinal result:\n");
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// Plot the covariance of the last pose
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Marginals marginals(*graph, result);
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cout.precision(2);
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cout << "\nP3:\n" << marginals.marginalCovariance(99) << endl;
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return 0;
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}
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