testNonlinearFactorGraph.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

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

#include <gtsam/base/Testable.h>
#include <gtsam/base/Matrix.h>
#include <tests/smallExample.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/symbolic/SymbolicFactorGraph.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/sam/RangeFactor.h>
#include <gtsam/slam/BetweenFactor.h>

#include <CppUnitLite/TestHarness.h>

#include <boost/assign/std/list.hpp>
#include <boost/assign/std/set.hpp>
using namespace boost::assign;

/*STL/C++*/
#include <iostream>

using namespace std;
using namespace gtsam;
using namespace example;

using symbol_shorthand::X;
using symbol_shorthand::L;

/* ************************************************************************* */
TEST( NonlinearFactorGraph, equals )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  NonlinearFactorGraph fg2 = createNonlinearFactorGraph();
  CHECK( fg.equals(fg2) );
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, error )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  Values c1 = createValues();
  double actual1 = fg.error(c1);
  DOUBLES_EQUAL( 0.0, actual1, 1e-9 );

  Values c2 = createNoisyValues();
  double actual2 = fg.error(c2);
  DOUBLES_EQUAL( 5.625, actual2, 1e-9 );
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, keys )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  KeySet actual = fg.keys();
  LONGS_EQUAL(3, (long)actual.size());
  KeySet::const_iterator it = actual.begin();
  LONGS_EQUAL((long)L(1), (long)*(it++));
  LONGS_EQUAL((long)X(1), (long)*(it++));
  LONGS_EQUAL((long)X(2), (long)*(it++));
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, GET_ORDERING)
{
  Ordering expected; expected += L(1), X(2), X(1); // For starting with l1,x1,x2
  NonlinearFactorGraph nlfg = createNonlinearFactorGraph();
  Ordering actual = Ordering::Colamd(nlfg);
  EXPECT(assert_equal(expected,actual));

  // Constrained ordering - put x2 at the end
  Ordering expectedConstrained; expectedConstrained += L(1), X(1), X(2);
  FastMap<Key, int> constraints;
  constraints[X(2)] = 1;
  Ordering actualConstrained = Ordering::ColamdConstrained(nlfg, constraints);
  EXPECT(assert_equal(expectedConstrained, actualConstrained));
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, probPrime )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  Values cfg = createValues();

  // evaluate the probability of the factor graph
  double actual = fg.probPrime(cfg);
  double expected = 1.0;
  DOUBLES_EQUAL(expected,actual,0);
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, linearize )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  Values initial = createNoisyValues();
  GaussianFactorGraph linearFG = *fg.linearize(initial);
  GaussianFactorGraph expected = createGaussianFactorGraph();
  CHECK(assert_equal(expected,linearFG)); // Needs correct linearizations
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, clone )
{
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  NonlinearFactorGraph actClone = fg.clone();
  EXPECT(assert_equal(fg, actClone));
  for (size_t i=0; i<fg.size(); ++i)
    EXPECT(fg[i] != actClone[i]);
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, rekey )
{
  NonlinearFactorGraph init = createNonlinearFactorGraph();
  map<Key,Key> rekey_mapping;
  rekey_mapping.insert(make_pair(L(1), L(4)));
  NonlinearFactorGraph actRekey = init.rekey(rekey_mapping);

  // ensure deep clone
  LONGS_EQUAL((long)init.size(), (long)actRekey.size());
  for (size_t i=0; i<init.size(); ++i)
      EXPECT(init[i] != actRekey[i]);

  NonlinearFactorGraph expRekey;
  // original measurements
  expRekey.push_back(init[0]);
  expRekey.push_back(init[1]);

  // updated measurements
  Point2 z3(0, -1),  z4(-1.5, -1.);
  SharedDiagonal sigma0_2 = noiseModel::Isotropic::Sigma(2,0.2);
  expRekey += simulated2D::Measurement(z3, sigma0_2, X(1), L(4));
  expRekey += simulated2D::Measurement(z4, sigma0_2, X(2), L(4));

  EXPECT(assert_equal(expRekey, actRekey));
}

/* ************************************************************************* */
TEST( NonlinearFactorGraph, symbolic )
{
  NonlinearFactorGraph graph = createNonlinearFactorGraph();

  SymbolicFactorGraph expected;
  expected.push_factor(X(1));
  expected.push_factor(X(1), X(2));
  expected.push_factor(X(1), L(1));
  expected.push_factor(X(2), L(1));

  SymbolicFactorGraph actual = *graph.symbolic();

  EXPECT(assert_equal(expected, actual));
}

/* ************************************************************************* */
TEST(NonlinearFactorGraph, UpdateCholesky) {
  NonlinearFactorGraph fg = createNonlinearFactorGraph();
  Values initial = createNoisyValues();

  // solve conventionally
  GaussianFactorGraph linearFG = *fg.linearize(initial);
  auto delta = linearFG.optimizeDensely();
  auto expected = initial.retract(delta);

  // solve with new method
  EXPECT(assert_equal(expected, fg.updateCholesky(initial)));

  // solve with Ordering
  Ordering ordering;
  ordering += L(1), X(2), X(1);
  EXPECT(assert_equal(expected, fg.updateCholesky(initial, ordering)));

  // solve with new method, heavily damped
  auto dampen = [](const HessianFactor::shared_ptr& hessianFactor) {
    auto iterator = hessianFactor->begin();
    for (; iterator != hessianFactor->end(); iterator++) {
      const auto index = std::distance(hessianFactor->begin(), iterator);
      auto block = hessianFactor->info().diagonalBlock(index);
      for (int j = 0; j < block.rows(); j++) {
        block(j, j) += 1e9;
      }
    }
  };
  EXPECT(assert_equal(initial, fg.updateCholesky(initial, dampen), 1e-6));
}

/* ************************************************************************* */
// Example from issue #452 which threw an ILS error. The reason was a very 
// weak prior on heading, which was tightened, and the ILS disappeared.
TEST(testNonlinearFactorGraph, eliminate) {
  // Linearization point
  Pose2 T11(0, 0, 0);
  Pose2 T12(1, 0, 0);
  Pose2 T21(0, 1, 0);
  Pose2 T22(1, 1, 0);

  // Factor graph
  auto graph = NonlinearFactorGraph();

  // Priors
  auto prior = noiseModel::Isotropic::Sigma(3, 1);
  graph.addPrior(11, T11, prior);
  graph.addPrior(21, T21, prior);

  // Odometry
  auto model = noiseModel::Diagonal::Sigmas(Vector3(0.01, 0.01, 0.3));
  graph.add(BetweenFactor<Pose2>(11, 12, T11.between(T12), model));
  graph.add(BetweenFactor<Pose2>(21, 22, T21.between(T22), model));

  // Range factor
  auto model_rho = noiseModel::Isotropic::Sigma(1, 0.01);
  graph.add(RangeFactor<Pose2>(12, 22, 1.0, model_rho));

  Values values;
  values.insert(11, T11.retract(Vector3(0.1,0.2,0.3)));
  values.insert(12, T12);
  values.insert(21, T21);
  values.insert(22, T22);
  auto linearized = graph.linearize(values);

  // Eliminate
  Ordering ordering;
  ordering += 11, 21, 12, 22;
  auto bn = linearized->eliminateSequential(ordering);
  EXPECT_LONGS_EQUAL(4, bn->size());
}

/* ************************************************************************* */
TEST(testNonlinearFactorGraph, addPrior) {
  Key k(0);

  // Factor graph.
  auto graph = NonlinearFactorGraph();

  // Add a prior factor for key k.
  auto model_double = noiseModel::Isotropic::Sigma(1, 1);
  graph.addPrior<double>(k, 10, model_double);

  // Assert the graph has 0 error with the correct values.
  Values values;
  values.insert(k, 10.0);
  EXPECT_DOUBLES_EQUAL(0, graph.error(values), 1e-16);

  // Assert the graph has some error with incorrect values.
  values.clear();
  values.insert(k, 11.0);
  EXPECT(0 != graph.error(values));

  // Clear the factor graph and values.
  values.clear();
  graph.erase(graph.begin(), graph.end());

  // Add a Pose3 prior to the factor graph. Use a gaussian noise model by
  // providing the covariance matrix.
  Eigen::DiagonalMatrix<double, 6, 6> covariance_pose3;
  covariance_pose3.setIdentity();
  Pose3 pose{Rot3(), Point3(0, 0, 0)};
  graph.addPrior(k, pose, covariance_pose3);

  // Assert the graph has 0 error with the correct values.
  values.insert(k, pose);
  EXPECT_DOUBLES_EQUAL(0, graph.error(values), 1e-16);

  // Assert the graph has some error with incorrect values.
  values.clear();
  Pose3 pose_incorrect{Rot3::RzRyRx(-M_PI, M_PI, -M_PI / 8), Point3(1, 2, 3)};
  values.insert(k, pose_incorrect);
  EXPECT(0 != graph.error(values));
}

TEST(NonlinearFactorGraph, printErrors)
{
  const NonlinearFactorGraph fg = createNonlinearFactorGraph();
  const Values c = createValues();

  // Test that it builds with default parameters.
  // We cannot check the output since (at present) output is fixed to std::cout.
  fg.printErrors(c);

  // Second round: using callback filter to check that we actually visit all factors:
  std::vector<bool> visited;
  visited.assign(fg.size(), false);
  const auto testFilter =
      [&](const gtsam::Factor *f, double error, size_t index) {
        EXPECT(f!=nullptr);
        EXPECT(error>=.0);
        visited.at(index)=true;
        return false; // do not print
      };
  fg.printErrors(c,"Test graph: ", gtsam::DefaultKeyFormatter,testFilter);

  for (bool visit : visited) EXPECT(visit==true);
}

/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */