388 lines
13 KiB
C++
388 lines
13 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testNonlinearFactorGraph.cpp
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* @brief Unit tests for Non-Linear Factor NonlinearFactorGraph
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* @brief testNonlinearFactorGraph
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* @author Carlos Nieto
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* @author Christian Potthast
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* @author Frank Dellaert
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*/
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/Matrix.h>
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#include <tests/smallExample.h>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/symbolic/SymbolicFactorGraph.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/sam/RangeFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <CppUnitLite/TestHarness.h>
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/*STL/C++*/
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#include <iostream>
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using namespace std;
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using namespace gtsam;
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using namespace example;
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, equals )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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NonlinearFactorGraph fg2 = createNonlinearFactorGraph();
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CHECK( fg.equals(fg2) );
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, error )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values c1 = createValues();
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double actual1 = fg.error(c1);
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DOUBLES_EQUAL( 0.0, actual1, 1e-9 );
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Values c2 = createNoisyValues();
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double actual2 = fg.error(c2);
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DOUBLES_EQUAL( 5.625, actual2, 1e-9 );
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, keys )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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KeySet actual = fg.keys();
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LONGS_EQUAL(3, (long)actual.size());
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KeySet::const_iterator it = actual.begin();
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LONGS_EQUAL((long)L(1), (long)*(it++));
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LONGS_EQUAL((long)X(1), (long)*(it++));
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LONGS_EQUAL((long)X(2), (long)*(it++));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, GET_ORDERING)
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{
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const Ordering expected{L(1), X(2), X(1)}; // For starting with l1,x1,x2
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NonlinearFactorGraph nlfg = createNonlinearFactorGraph();
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Ordering actual = Ordering::Colamd(nlfg);
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EXPECT(assert_equal(expected,actual));
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// Constrained ordering - put x2 at the end
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const Ordering expectedConstrained{L(1), X(1), X(2)};
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FastMap<Key, int> constraints;
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constraints[X(2)] = 1;
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Ordering actualConstrained = Ordering::ColamdConstrained(nlfg, constraints);
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EXPECT(assert_equal(expectedConstrained, actualConstrained));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, probPrime )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values cfg = createValues();
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// evaluate the probability of the factor graph
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double actual = fg.probPrime(cfg);
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double expected = 1.0;
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DOUBLES_EQUAL(expected,actual,0);
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}
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/* ************************************************************************* */
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TEST(NonlinearFactorGraph, ProbPrime2) {
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NonlinearFactorGraph fg;
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fg.emplace_shared<PriorFactor<double>>(1, 0.0,
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noiseModel::Isotropic::Sigma(1, 1.0));
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Values values;
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values.insert(1, 1.0);
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// The prior factor squared error is: 0.5.
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EXPECT_DOUBLES_EQUAL(0.5, fg.error(values), 1e-12);
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// The probability value is: exp^(-factor_error) / sqrt(2 * PI)
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// Ignore the denominator and we get: exp^(-factor_error) = exp^(-0.5)
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double expected = exp(-0.5);
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EXPECT_DOUBLES_EQUAL(expected, fg.probPrime(values), 1e-12);
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, linearize )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values initial = createNoisyValues();
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GaussianFactorGraph linearFG = *fg.linearize(initial);
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GaussianFactorGraph expected = createGaussianFactorGraph();
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CHECK(assert_equal(expected,linearFG)); // Needs correct linearizations
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, clone )
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{
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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NonlinearFactorGraph actClone = fg.clone();
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EXPECT(assert_equal(fg, actClone));
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for (size_t i=0; i<fg.size(); ++i)
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EXPECT(fg[i] != actClone[i]);
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, rekey )
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{
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NonlinearFactorGraph init = createNonlinearFactorGraph();
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map<Key,Key> rekey_mapping;
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rekey_mapping.insert(make_pair(L(1), L(4)));
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NonlinearFactorGraph actRekey = init.rekey(rekey_mapping);
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// ensure deep clone
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LONGS_EQUAL((long)init.size(), (long)actRekey.size());
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for (size_t i=0; i<init.size(); ++i)
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EXPECT(init[i] != actRekey[i]);
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NonlinearFactorGraph expRekey;
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// original measurements
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expRekey.push_back(init[0]);
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expRekey.push_back(init[1]);
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// updated measurements
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Point2 z3(0, -1), z4(-1.5, -1.);
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SharedDiagonal sigma0_2 = noiseModel::Isotropic::Sigma(2,0.2);
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expRekey.emplace_shared<simulated2D::Measurement>(z3, sigma0_2, X(1), L(4));
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expRekey.emplace_shared<simulated2D::Measurement>(z4, sigma0_2, X(2), L(4));
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EXPECT(assert_equal(expRekey, actRekey));
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}
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/* ************************************************************************* */
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TEST( NonlinearFactorGraph, symbolic )
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{
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NonlinearFactorGraph graph = createNonlinearFactorGraph();
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SymbolicFactorGraph expected;
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expected.push_factor(X(1));
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expected.push_factor(X(1), X(2));
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expected.push_factor(X(1), L(1));
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expected.push_factor(X(2), L(1));
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SymbolicFactorGraph actual = *graph.symbolic();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST(NonlinearFactorGraph, UpdateCholesky) {
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NonlinearFactorGraph fg = createNonlinearFactorGraph();
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Values initial = createNoisyValues();
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// solve conventionally
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GaussianFactorGraph linearFG = *fg.linearize(initial);
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auto delta = linearFG.optimizeDensely();
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auto expected = initial.retract(delta);
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// solve with new method
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EXPECT(assert_equal(expected, fg.updateCholesky(initial)));
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// solve with Ordering
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const Ordering ordering{L(1), X(2), X(1)};
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EXPECT(assert_equal(expected, fg.updateCholesky(initial, ordering)));
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// solve with new method, heavily damped
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auto dampen = [](const HessianFactor::shared_ptr& hessianFactor) {
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auto iterator = hessianFactor->begin();
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for (; iterator != hessianFactor->end(); iterator++) {
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const auto index = std::distance(hessianFactor->begin(), iterator);
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auto block = hessianFactor->info().diagonalBlock(index);
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for (int j = 0; j < block.rows(); j++) {
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block(j, j) += 1e9;
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}
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}
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};
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EXPECT(assert_equal(initial, fg.updateCholesky(initial, dampen), 1e-6));
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}
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/* ************************************************************************* */
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// Example from issue #452 which threw an ILS error. The reason was a very
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// weak prior on heading, which was tightened, and the ILS disappeared.
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TEST(testNonlinearFactorGraph, eliminate) {
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// Linearization point
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Pose2 T11(0, 0, 0);
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Pose2 T12(1, 0, 0);
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Pose2 T21(0, 1, 0);
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Pose2 T22(1, 1, 0);
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// Factor graph
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auto graph = NonlinearFactorGraph();
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// Priors
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auto prior = noiseModel::Isotropic::Sigma(3, 1);
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graph.addPrior(11, T11, prior);
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graph.addPrior(21, T21, prior);
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// Odometry
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auto model = noiseModel::Diagonal::Sigmas(Vector3(0.01, 0.01, 0.3));
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graph.add(BetweenFactor<Pose2>(11, 12, T11.between(T12), model));
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graph.add(BetweenFactor<Pose2>(21, 22, T21.between(T22), model));
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// Range factor
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auto model_rho = noiseModel::Isotropic::Sigma(1, 0.01);
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graph.add(RangeFactor<Pose2>(12, 22, 1.0, model_rho));
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Values values;
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values.insert(11, T11.retract(Vector3(0.1,0.2,0.3)));
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values.insert(12, T12);
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values.insert(21, T21);
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values.insert(22, T22);
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auto linearized = graph.linearize(values);
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// Eliminate
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const Ordering ordering{11, 21, 12, 22};
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auto bn = linearized->eliminateSequential(ordering);
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EXPECT_LONGS_EQUAL(4, bn->size());
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}
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/* ************************************************************************* */
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TEST(testNonlinearFactorGraph, addPrior) {
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Key k(0);
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// Factor graph.
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auto graph = NonlinearFactorGraph();
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// Add a prior factor for key k.
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auto model_double = noiseModel::Isotropic::Sigma(1, 1);
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graph.addPrior<double>(k, 10, model_double);
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// Assert the graph has 0 error with the correct values.
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Values values;
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values.insert(k, 10.0);
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EXPECT_DOUBLES_EQUAL(0, graph.error(values), 1e-16);
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// Assert the graph has some error with incorrect values.
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values.clear();
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values.insert(k, 11.0);
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EXPECT(0 != graph.error(values));
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// Clear the factor graph and values.
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values.clear();
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graph.erase(graph.begin(), graph.end());
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// Add a Pose3 prior to the factor graph. Use a gaussian noise model by
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// providing the covariance matrix.
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Eigen::DiagonalMatrix<double, 6, 6> covariance_pose3;
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covariance_pose3.setIdentity();
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Pose3 pose{Rot3(), Point3(0, 0, 0)};
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graph.addPrior(k, pose, covariance_pose3);
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// Assert the graph has 0 error with the correct values.
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values.insert(k, pose);
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EXPECT_DOUBLES_EQUAL(0, graph.error(values), 1e-16);
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// Assert the graph has some error with incorrect values.
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values.clear();
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Pose3 pose_incorrect{Rot3::RzRyRx(-M_PI, M_PI, -M_PI / 8), Point3(1, 2, 3)};
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values.insert(k, pose_incorrect);
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EXPECT(0 != graph.error(values));
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}
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/* ************************************************************************* */
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TEST(NonlinearFactorGraph, printErrors)
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{
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const NonlinearFactorGraph fg = createNonlinearFactorGraph();
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const Values c = createValues();
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// Test that it builds with default parameters.
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// We cannot check the output since (at present) output is fixed to std::cout.
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fg.printErrors(c);
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// Second round: using callback filter to check that we actually visit all factors:
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std::vector<bool> visited;
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visited.assign(fg.size(), false);
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const auto testFilter =
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[&](const gtsam::Factor *f, double error, size_t index) {
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EXPECT(f!=nullptr);
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EXPECT(error>=.0);
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visited.at(index)=true;
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return false; // do not print
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};
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fg.printErrors(c,"Test graph: ", gtsam::DefaultKeyFormatter,testFilter);
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for (bool visit : visited) EXPECT(visit==true);
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}
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/* ************************************************************************* */
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TEST(NonlinearFactorGraph, dot) {
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string expected =
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"graph {\n"
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" size=\"5,5\";\n"
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"\n"
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" var7782220156096217089[label=\"l1\"];\n"
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" var8646911284551352321[label=\"x1\"];\n"
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" var8646911284551352322[label=\"x2\"];\n"
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"\n"
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" factor0[label=\"\", shape=point];\n"
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" var8646911284551352321--factor0;\n"
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" factor1[label=\"\", shape=point];\n"
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" var8646911284551352321--factor1;\n"
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" var8646911284551352322--factor1;\n"
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" factor2[label=\"\", shape=point];\n"
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" var8646911284551352321--factor2;\n"
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" var7782220156096217089--factor2;\n"
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" factor3[label=\"\", shape=point];\n"
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" var8646911284551352322--factor3;\n"
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" var7782220156096217089--factor3;\n"
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"}\n";
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const NonlinearFactorGraph fg = createNonlinearFactorGraph();
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string actual = fg.dot();
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EXPECT(actual == expected);
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}
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/* ************************************************************************* */
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TEST(NonlinearFactorGraph, dot_extra) {
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string expected =
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"graph {\n"
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" size=\"5,5\";\n"
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"\n"
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" var7782220156096217089[label=\"l1\", pos=\"0,0!\"];\n"
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" var8646911284551352321[label=\"x1\", pos=\"1,0!\"];\n"
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" var8646911284551352322[label=\"x2\", pos=\"1,1.5!\"];\n"
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"\n"
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" factor0[label=\"\", shape=point];\n"
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" var8646911284551352321--factor0;\n"
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" factor1[label=\"\", shape=point];\n"
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" var8646911284551352321--factor1;\n"
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" var8646911284551352322--factor1;\n"
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" factor2[label=\"\", shape=point];\n"
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" var8646911284551352321--factor2;\n"
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" var7782220156096217089--factor2;\n"
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" factor3[label=\"\", shape=point];\n"
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" var8646911284551352322--factor3;\n"
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" var7782220156096217089--factor3;\n"
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"}\n";
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const NonlinearFactorGraph fg = createNonlinearFactorGraph();
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const Values c = createValues();
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stringstream ss;
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fg.dot(ss, c);
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EXPECT(ss.str() == expected);
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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