133 lines
4.9 KiB
C++
133 lines
4.9 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 testAntiFactor.cpp
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* @brief Unit test for the AntiFactor
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* @author Stephen Williams
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/slam/AntiFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/nonlinear/NonlinearOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/linear/HessianFactor.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/geometry/Pose3.h>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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TEST( AntiFactor, NegativeHessian)
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{
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// The AntiFactor should produce a Hessian Factor with negative matrices
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// Create linearization points
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Pose3 pose1(Rot3(), Point3(0, 0, 0));
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Pose3 pose2(Rot3(), Point3(2, 1, 3));
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Pose3 z(Rot3(), Point3(1, 1, 1));
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SharedNoiseModel sigma(noiseModel::Unit::Create(6));
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// Create a configuration corresponding to the ground truth
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Values values;
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values.insert(1, pose1);
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values.insert(2, pose2);
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// Create a "standard" factor
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BetweenFactor<Pose3>::shared_ptr originalFactor(new BetweenFactor<Pose3>(1, 2, z, sigma));
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// Linearize it into a Jacobian Factor
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GaussianFactor::shared_ptr originalJacobian = originalFactor->linearize(values);
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// Convert it to a Hessian Factor
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HessianFactor::shared_ptr originalHessian = HessianFactor::shared_ptr(new HessianFactor(*originalJacobian));
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// Create the AntiFactor version of the original nonlinear factor
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AntiFactor::shared_ptr antiFactor(new AntiFactor(originalFactor));
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// Linearize the AntiFactor into a Hessian Factor
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GaussianFactor::shared_ptr antiGaussian = antiFactor->linearize(values);
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HessianFactor::shared_ptr antiHessian = boost::dynamic_pointer_cast<HessianFactor>(antiGaussian);
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Matrix expected_information = -originalHessian->information();
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Matrix actual_information = antiHessian->information();
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EXPECT(assert_equal(expected_information, actual_information));
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Vector expected_linear = -originalHessian->linearTerm();
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Vector actual_linear = antiHessian->linearTerm();
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EXPECT(assert_equal(expected_linear, actual_linear));
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double expected_f = -originalHessian->constantTerm();
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double actual_f = antiHessian->constantTerm();
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EXPECT_DOUBLES_EQUAL(expected_f, actual_f, 1e-5);
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}
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/* ************************************************************************* */
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TEST( AntiFactor, EquivalentBayesNet)
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{
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// Test the AntiFactor by creating a simple graph and eliminating into a BayesNet
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// Then add an additional factor and the corresponding AntiFactor and eliminate
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// The resulting BayesNet should be identical to the first
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Pose3 pose1(Rot3(), Point3(0, 0, 0));
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Pose3 pose2(Rot3(), Point3(2, 1, 3));
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Pose3 z(Rot3(), Point3(1, 1, 1));
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SharedNoiseModel sigma(noiseModel::Unit::Create(6));
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NonlinearFactorGraph graph;
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graph.addPrior<>(1, pose1, sigma);
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graph.emplace_shared<BetweenFactor<Pose3> >(1, 2, pose1.between(pose2), sigma);
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// Create a configuration corresponding to the ground truth
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Values values;
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values.insert(1, pose1);
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values.insert(2, pose2);
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// Define an elimination ordering
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Ordering ordering = graph.orderingCOLAMD();
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// Eliminate into a BayesNet
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GaussianFactorGraph lin_graph = *graph.linearize(values);
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GaussianBayesNet::shared_ptr expectedBayesNet = lin_graph.eliminateSequential(ordering);
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// Back-substitute to find the optimal deltas
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VectorValues expectedDeltas = expectedBayesNet->optimize();
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// Add an additional factor between Pose1 and Pose2
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BetweenFactor<Pose3>::shared_ptr f1(new BetweenFactor<Pose3>(1, 2, z, sigma));
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graph.push_back(f1);
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// Add the corresponding AntiFactor between Pose1 and Pose2
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AntiFactor::shared_ptr f2(new AntiFactor(f1));
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graph.push_back(f2);
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// Again, Eliminate into a BayesNet
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GaussianFactorGraph lin_graph1 = *graph.linearize(values);
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GaussianBayesNet::shared_ptr actualBayesNet = lin_graph1.eliminateSequential(ordering);
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// Back-substitute to find the optimal deltas
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VectorValues actualDeltas = actualBayesNet->optimize();
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// Verify the BayesNets are identical
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CHECK(assert_equal(*expectedBayesNet, *actualBayesNet, 1e-5));
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CHECK(assert_equal(expectedDeltas, actualDeltas, 1e-5));
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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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