65 lines
2.2 KiB
C++
65 lines
2.2 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 METISOrdering.cpp
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* @brief Simple robot motion example, with prior and two odometry measurements,
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* using a METIS ordering
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* @author Frank Dellaert
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* @author Andrew Melim
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*/
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/**
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* Example of a simple 2D localization example optimized using METIS ordering
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* - For more details on the full optimization pipeline, see OdometryExample.cpp
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*/
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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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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/slam/BetweenFactor.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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NonlinearFactorGraph graph;
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Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
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auto priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
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graph.addPrior(1, priorMean, priorNoise);
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Pose2 odometry(2.0, 0.0, 0.0);
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auto odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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graph.emplace_shared<BetweenFactor<Pose2> >(1, 2, odometry, odometryNoise);
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graph.emplace_shared<BetweenFactor<Pose2> >(2, 3, odometry, odometryNoise);
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graph.print("\nFactor Graph:\n"); // print
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Values initial;
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initial.insert(1, Pose2(0.5, 0.0, 0.2));
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initial.insert(2, Pose2(2.3, 0.1, -0.2));
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initial.insert(3, Pose2(4.1, 0.1, 0.1));
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initial.print("\nInitial Estimate:\n"); // print
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// optimize using Levenberg-Marquardt optimization
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LevenbergMarquardtParams params;
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// In order to specify the ordering type, we need to se the "orderingType". By
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// default this parameter is set to OrderingType::COLAMD
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params.orderingType = Ordering::METIS;
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LevenbergMarquardtOptimizer optimizer(graph, initial, params);
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Values result = optimizer.optimize();
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result.print("Final Result:\n");
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return 0;
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}
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