Pose2SLAMExampleExpressions.cpp

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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
 
 * -------------------------------------------------------------------------- */
 
// The two new headers that allow using our Automatic Differentiation Expression framework
#include <gtsam/slam/expressions.h>
#include <gtsam/nonlinear/ExpressionFactorGraph.h>
 
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
 
using namespace std;
using namespace gtsam;
 
int main(int argc, char** argv) {
  // 1. Create a factor graph container and add factors to it
  ExpressionFactorGraph graph;
 
  // Create Expressions for unknowns
  Pose2_ x1(1), x2(2), x3(3), x4(4), x5(5);
 
  // 2a. Add a prior on the first pose, setting it to the origin
  auto priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
  graph.addExpressionFactor(x1, Pose2(0, 0, 0), priorNoise);
 
  // For simplicity, we use the same noise model for odometry and loop closures
  auto model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
 
  // 2b. Add odometry factors
  graph.addExpressionFactor(between(x1, x2), Pose2(2, 0, 0), model);
  graph.addExpressionFactor(between(x2, x3), Pose2(2, 0, M_PI_2), model);
  graph.addExpressionFactor(between(x3, x4), Pose2(2, 0, M_PI_2), model);
  graph.addExpressionFactor(between(x4, x5), Pose2(2, 0, M_PI_2), model);
 
  // 2c. Add the loop closure constraint
  graph.addExpressionFactor(between(x5, x2), Pose2(2, 0, M_PI_2), model);
  graph.print("\nFactor Graph:\n");  // print
 
  // 3. Create the data structure to hold the initialEstimate estimate to the
  // solution For illustrative purposes, these have been deliberately set to
  // incorrect values
  Values initialEstimate;
  initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
  initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2));
  initialEstimate.insert(3, Pose2(4.1, 0.1, M_PI_2));
  initialEstimate.insert(4, Pose2(4.0, 2.0, M_PI));
  initialEstimate.insert(5, Pose2(2.1, 2.1, -M_PI_2));
  initialEstimate.print("\nInitial Estimate:\n");  // print
 
  // 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
  GaussNewtonParams parameters;
  parameters.relativeErrorTol = 1e-5;
  parameters.maxIterations = 100;
  GaussNewtonOptimizer optimizer(graph, initialEstimate, parameters);
  Values result = optimizer.optimize();
  result.print("Final Result:\n");
 
  // 5. Calculate and print marginal covariances for all variables
  cout.precision(3);
  Marginals marginals(graph, result);
  cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl;
  cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl;
  cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl;
  cout << "x4 covariance:\n" << marginals.marginalCovariance(4) << endl;
  cout << "x5 covariance:\n" << marginals.marginalCovariance(5) << endl;
 
  return 0;
}