gtsam/examples/Pose2SLAMwSPCG.cpp

/* ----------------------------------------------------------------------------

 * 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/PriorFactor.h>
#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
  noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
  graph.push_back(PriorFactor<Pose2>(1, prior, priorNoise));

  // 2b. Add odometry factors
  noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
  graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2),    odometryNoise));
  graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
  graph.push_back(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
  graph.push_back(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));

  // 2c. Add the loop closure constraint
  noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
  graph.push_back(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;
}