96 lines
3.6 KiB
C++
96 lines
3.6 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 testInference.cpp
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* @brief Unit tests for functionality declared in inference.h
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* @author Frank Dellaert
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*/
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#include <CppUnitLite/TestHarness.h>
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#define GTSAM_MAGIC_KEY
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/slam/smallExample.h>
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#include <gtsam/slam/planarSLAM.h>
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using namespace std;
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using namespace gtsam;
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/* ************************************************************************* */
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// The tests below test the *generic* inference algorithms. Some of these have
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// specialized versions in the derived classes GaussianFactorGraph etc...
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/* ************************************************************************* */
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/* ************************************************************************* */
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TEST(GaussianFactorGraph, createSmoother)
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{
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using namespace example;
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GaussianFactorGraph fg2;
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Ordering ordering;
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boost::tie(fg2,ordering) = createSmoother(3);
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LONGS_EQUAL(5,fg2.size());
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// eliminate
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vector<Index> x3var; x3var.push_back(ordering["x3"]);
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vector<Index> x1var; x1var.push_back(ordering["x1"]);
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GaussianBayesNet p_x3 = *GaussianSequentialSolver(*GaussianSequentialSolver(fg2).jointFactorGraph(x3var)).eliminate();
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GaussianBayesNet p_x1 = *GaussianSequentialSolver(*GaussianSequentialSolver(fg2).jointFactorGraph(x1var)).eliminate();
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CHECK(assert_equal(*p_x1.back(),*p_x3.front())); // should be the same because of symmetry
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}
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/* ************************************************************************* */
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TEST( Inference, marginals )
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{
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using namespace example;
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// create and marginalize a small Bayes net on "x"
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GaussianBayesNet cbn = createSmallGaussianBayesNet();
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vector<Index> xvar; xvar.push_back(0);
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GaussianBayesNet actual = *GaussianSequentialSolver(*GaussianSequentialSolver(GaussianFactorGraph(cbn)).jointFactorGraph(xvar)).eliminate();
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// expected is just scalar Gaussian on x
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GaussianBayesNet expected = scalarGaussian(0, 4, sqrt(2));
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( Inference, marginals2)
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{
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using namespace gtsam::planarSLAM;
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Graph fg;
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SharedDiagonal poseModel(sharedSigma(3, 0.1));
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SharedDiagonal pointModel(sharedSigma(3, 0.1));
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fg.addPrior(PoseKey(0), Pose2(), poseModel);
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fg.addOdometry(PoseKey(0), PoseKey(1), Pose2(1.0,0.0,0.0), poseModel);
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fg.addOdometry(PoseKey(1), PoseKey(2), Pose2(1.0,0.0,0.0), poseModel);
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fg.addBearingRange(PoseKey(0), PointKey(0), Rot2(), 1.0, pointModel);
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fg.addBearingRange(PoseKey(1), PointKey(0), Rot2(), 1.0, pointModel);
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fg.addBearingRange(PoseKey(2), PointKey(0), Rot2(), 1.0, pointModel);
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Values init;
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init.insert(PoseKey(0), Pose2(0.0,0.0,0.0));
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init.insert(PoseKey(1), Pose2(1.0,0.0,0.0));
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init.insert(PoseKey(2), Pose2(2.0,0.0,0.0));
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init.insert(PointKey(0), Point2(1.0,1.0));
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Ordering ordering(*fg.orderingCOLAMD(init));
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FactorGraph<JacobianFactor>::shared_ptr gfg(fg.linearize(init, ordering));
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GaussianMultifrontalSolver solver(*gfg);
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solver.marginalFactor(ordering[PointKey(0)]);
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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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