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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 Pose2SLAMwSPCG.cpp
* @brief A 2D Pose SLAM example using the SimpleSPCGSolver.
* @author Yong-Dian Jian
* @date June 2, 2012
*/
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
// In contrast to that example, however, we will use a PCG solver here
#include <gtsam/linear/SubgraphSolver.h>
using namespace std;
using namespace gtsam;
int main(int argc, char** argv) {
// 1. Create a factor graph container and add factors to it
NonlinearFactorGraph graph;
// 2a. Add a prior on the first pose, setting it to the origin
Pose2 prior(0.0, 0.0, 0.0); // prior at origin
auto priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.addPrior(1, prior, priorNoise);
// 2b. Add odometry factors
auto odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.emplace_shared<BetweenFactor<Pose2> >(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2> >(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2> >(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
graph.emplace_shared<BetweenFactor<Pose2> >(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise);
// 2c. Add the loop closure constraint
auto model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.emplace_shared<BetweenFactor<Pose2> >(5, 1, Pose2(0.0, 0.0, 0.0),
model);
graph.print("\nFactor Graph:\n"); // print
// 3. Create the data structure to hold the initialEstimate estimate to the
// solution
Values initialEstimate;
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
initialEstimate.insert(2, Pose2(2.3, 0.1, 1.1));
initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8));
initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2));
initialEstimate.insert(5, Pose2(0.1, -0.7, 5.8));
initialEstimate.print("\nInitial Estimate:\n"); // print
// 4. Single Step Optimization using Levenberg-Marquardt
LevenbergMarquardtParams parameters;
parameters.verbosity = NonlinearOptimizerParams::ERROR;
parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
// LM is still the outer optimization loop, but by specifying "Iterative"
// below We indicate that an iterative linear solver should be used. In
// addition, the *type* of the iterativeParams decides on the type of
// iterative solver, in this case the SPCG (subgraph PCG)
parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters);
Values result = optimizer.optimize();
result.print("Final Result:\n");
cout << "subgraph solver final error = " << graph.error(result) << endl;
return 0;
}
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