SimpleRotation.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

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

// In this example, a 2D rotation will be used as the variable of interest
#include <gtsam/geometry/Rot2.h>

// Each variable in the system (poses) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use symbols
#include <gtsam/inference/Symbol.h>

// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
// We will apply a simple prior on the rotation. We do so via the `addPrior` convenience
// method in NonlinearFactorGraph.

// When the factors are created, we will add them to a Factor Graph. As the factors we are using
// are nonlinear factors, we will need a Nonlinear Factor Graph.
#include <gtsam/nonlinear/NonlinearFactorGraph.h>

// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
// nonlinear functions around an initial linearization point, then solve the linear system
// to update the linearization point. This happens repeatedly until the solver converges
// to a consistent set of variable values. This requires us to specify an initial guess
// for each variable, held in a Values container.
#include <gtsam/nonlinear/Values.h>

// Finally, once all of the factors have been added to our factor graph, we will want to
// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
// GTSAM includes several nonlinear optimizers to perform this step. Here we will use the
// standard Levenberg-Marquardt solver
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>


using namespace std;
using namespace gtsam;

const double degree = M_PI / 180;

int main() {
  Rot2 prior = Rot2::fromAngle(30 * degree);
  prior.print("goal angle");
  auto model = noiseModel::Isotropic::Sigma(1, 1 * degree);
  Symbol key('x', 1);

  NonlinearFactorGraph graph;
  graph.addPrior(key, prior, model);
  graph.print("full graph");

  Values initial;
  initial.insert(key, Rot2::fromAngle(20 * degree));
  initial.print("initial estimate");

  Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
  result.print("final result");

  return 0;
}