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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file METISOrdering.cpp
* @brief Simple robot motion example, with prior and two odometry measurements,
* using a METIS ordering
* @author Frank Dellaert
* @author Andrew Melim
*/
/**
* Example of a simple 2D localization example optimized using METIS ordering
* - For more details on the full optimization pipeline, see OdometryExample.cpp
*/
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/slam/BetweenFactor.h>
using namespace std;
using namespace gtsam;
int main(int argc, char** argv) {
NonlinearFactorGraph graph;
Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
auto priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.addPrior(1, priorMean, priorNoise);
Pose2 odometry(2.0, 0.0, 0.0);
auto odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.emplace_shared<BetweenFactor<Pose2> >(1, 2, odometry, odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2> >(2, 3, odometry, odometryNoise);
graph.print("\nFactor Graph:\n"); // print
Values initial;
initial.insert(1, Pose2(0.5, 0.0, 0.2));
initial.insert(2, Pose2(2.3, 0.1, -0.2));
initial.insert(3, Pose2(4.1, 0.1, 0.1));
initial.print("\nInitial Estimate:\n"); // print
// optimize using Levenberg-Marquardt optimization
LevenbergMarquardtParams params;
// In order to specify the ordering type, we need to se the "orderingType". By
// default this parameter is set to OrderingType::COLAMD
params.orderingType = Ordering::METIS;
LevenbergMarquardtOptimizer optimizer(graph, initial, params);
Values result = optimizer.optimize();
result.print("Final Result:\n");
return 0;
}
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