188 lines
7.9 KiB
C++
188 lines
7.9 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 testDoglegOptimizer.cpp
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* @brief Unit tests for DoglegOptimizer
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* @author Richard Roberts
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* @author Frank dellaert
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <tests/smallExample.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/nonlinear/DoglegOptimizer.h>
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#include <gtsam/nonlinear/DoglegOptimizerImpl.h>
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#include <gtsam/nonlinear/NonlinearEquality.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/linear/GaussianBayesTree.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <boost/assign/list_of.hpp> // for 'list_of()'
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#include <functional>
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#include <boost/iterator/counting_iterator.hpp>
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using namespace std;
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using namespace gtsam;
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// Convenience for named keys
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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(DoglegOptimizer, ComputeBlend) {
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// Create an arbitrary Bayes Net
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GaussianBayesNet gbn;
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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0, Vector2(1.0,2.0), (Matrix(2, 2) << 3.0,4.0,0.0,6.0).finished(),
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3, (Matrix(2, 2) << 7.0,8.0,9.0,10.0).finished(),
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4, (Matrix(2, 2) << 11.0,12.0,13.0,14.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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1, Vector2(15.0,16.0), (Matrix(2, 2) << 17.0,18.0,0.0,20.0).finished(),
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2, (Matrix(2, 2) << 21.0,22.0,23.0,24.0).finished(),
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4, (Matrix(2, 2) << 25.0,26.0,27.0,28.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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2, Vector2(29.0,30.0), (Matrix(2, 2) << 31.0,32.0,0.0,34.0).finished(),
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3, (Matrix(2, 2) << 35.0,36.0,37.0,38.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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3, Vector2(39.0,40.0), (Matrix(2, 2) << 41.0,42.0,0.0,44.0).finished(),
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4, (Matrix(2, 2) << 45.0,46.0,47.0,48.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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4, Vector2(49.0,50.0), (Matrix(2, 2) << 51.0,52.0,0.0,54.0).finished()));
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// Compute steepest descent point
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VectorValues xu = gbn.optimizeGradientSearch();
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// Compute Newton's method point
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VectorValues xn = gbn.optimize();
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// The Newton's method point should be more "adventurous", i.e. larger, than the steepest descent point
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EXPECT(xu.vector().norm() < xn.vector().norm());
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// Compute blend
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double Delta = 1.5;
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VectorValues xb = DoglegOptimizerImpl::ComputeBlend(Delta, xu, xn);
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DOUBLES_EQUAL(Delta, xb.vector().norm(), 1e-10);
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}
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/* ************************************************************************* */
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TEST(DoglegOptimizer, ComputeDoglegPoint) {
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// Create an arbitrary Bayes Net
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GaussianBayesNet gbn;
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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0, Vector2(1.0,2.0), (Matrix(2, 2) << 3.0,4.0,0.0,6.0).finished(),
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3, (Matrix(2, 2) << 7.0,8.0,9.0,10.0).finished(),
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4, (Matrix(2, 2) << 11.0,12.0,13.0,14.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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1, Vector2(15.0,16.0), (Matrix(2, 2) << 17.0,18.0,0.0,20.0).finished(),
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2, (Matrix(2, 2) << 21.0,22.0,23.0,24.0).finished(),
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4, (Matrix(2, 2) << 25.0,26.0,27.0,28.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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2, Vector2(29.0,30.0), (Matrix(2, 2) << 31.0,32.0,0.0,34.0).finished(),
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3, (Matrix(2, 2) << 35.0,36.0,37.0,38.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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3, Vector2(39.0,40.0), (Matrix(2, 2) << 41.0,42.0,0.0,44.0).finished(),
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4, (Matrix(2, 2) << 45.0,46.0,47.0,48.0).finished()));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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4, Vector2(49.0,50.0), (Matrix(2, 2) << 51.0,52.0,0.0,54.0).finished()));
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// Compute dogleg point for different deltas
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double Delta1 = 0.5; // Less than steepest descent
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VectorValues actual1 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta1, gbn.optimizeGradientSearch(), gbn.optimize());
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DOUBLES_EQUAL(Delta1, actual1.vector().norm(), 1e-5);
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double Delta2 = 1.5; // Between steepest descent and Newton's method
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VectorValues expected2 = DoglegOptimizerImpl::ComputeBlend(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
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VectorValues actual2 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
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DOUBLES_EQUAL(Delta2, actual2.vector().norm(), 1e-5);
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EXPECT(assert_equal(expected2, actual2));
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double Delta3 = 5.0; // Larger than Newton's method point
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VectorValues expected3 = gbn.optimize();
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VectorValues actual3 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta3, gbn.optimizeGradientSearch(), gbn.optimize());
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EXPECT(assert_equal(expected3, actual3));
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}
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/* ************************************************************************* */
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TEST(DoglegOptimizer, Iterate) {
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// really non-linear factor graph
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NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
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// config far from minimum
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Point2 x0(3,0);
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Values config;
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config.insert(X(1), x0);
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double Delta = 1.0;
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for(size_t it=0; it<10; ++it) {
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GaussianBayesNet gbn = *fg.linearize(config)->eliminateSequential();
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// Iterate assumes that linear error = nonlinear error at the linearization point, and this should be true
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double nonlinearError = fg.error(config);
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double linearError = GaussianFactorGraph(gbn).error(config.zeroVectors());
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DOUBLES_EQUAL(nonlinearError, linearError, 1e-5);
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// cout << "it " << it << ", Delta = " << Delta << ", error = " << fg->error(*config) << endl;
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VectorValues dx_u = gbn.optimizeGradientSearch();
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VectorValues dx_n = gbn.optimize();
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DoglegOptimizerImpl::IterationResult result = DoglegOptimizerImpl::Iterate(Delta, DoglegOptimizerImpl::SEARCH_EACH_ITERATION, dx_u, dx_n, gbn, fg, config, fg.error(config));
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Delta = result.delta;
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EXPECT(result.f_error < fg.error(config)); // Check that error decreases
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Values newConfig(config.retract(result.dx_d));
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config = newConfig;
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DOUBLES_EQUAL(fg.error(config), result.f_error, 1e-5); // Check that error is correctly filled in
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}
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}
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/* ************************************************************************* */
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TEST(DoglegOptimizer, Constraint) {
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// Create a pose-graph graph with a constraint on the first pose
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NonlinearFactorGraph graph;
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const Pose2 origin(0, 0, 0), pose2(2, 0, 0);
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graph.emplace_shared<NonlinearEquality<Pose2> >(1, origin);
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auto model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
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graph.emplace_shared<BetweenFactor<Pose2> >(1, 2, pose2, model);
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// Create feasible initial estimate
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Values initial;
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initial.insert(1, origin); // feasible !
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initial.insert(2, Pose2(2.3, 0.1, -0.2));
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// Optimize the initial values using DoglegOptimizer
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DoglegParams params;
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params.setVerbosityDL("VERBOSITY");
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DoglegOptimizer optimizer(graph, initial, params);
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Values result = optimizer.optimize();
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// Check result
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EXPECT(assert_equal(pose2, result.at<Pose2>(2)));
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// Create infeasible initial estimate
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Values infeasible;
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infeasible.insert(1, Pose2(0.1, 0, 0)); // infeasible !
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infeasible.insert(2, Pose2(2.3, 0.1, -0.2));
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// Try optimizing with infeasible initial estimate
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DoglegOptimizer optimizer2(graph, infeasible, params);
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#ifdef GTSAM_USE_TBB
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CHECK_EXCEPTION(optimizer2.optimize(), std::exception);
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#else
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CHECK_EXCEPTION(optimizer2.optimize(), std::invalid_argument);
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#endif
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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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