191 lines
6.7 KiB
C++
191 lines
6.7 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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* testGaussianJunctionTree.cpp
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*
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* Created on: Jul 8, 2010
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* Author: nikai
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* Description:
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*/
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#include <iostream>
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#include <gtsam/CppUnitLite/TestHarness.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <boost/assign/list_of.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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#include <boost/assign/std/set.hpp> // for operator +=
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#include <boost/assign/std/vector.hpp>
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using namespace boost::assign;
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#define GTSAM_MAGIC_KEY
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/linear/GaussianJunctionTree.h>
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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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using namespace example;
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/* ************************************************************************* *
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Bayes tree for smoother with "nested dissection" ordering:
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C1 x5 x6 x4
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C2 x3 x2 : x4
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C3 x1 : x2
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C4 x7 : x6
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*/
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TEST( GaussianJunctionTree, constructor2 )
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{
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// create a graph
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Ordering ordering; ordering += "x1","x3","x5","x7","x2","x6","x4";
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GaussianFactorGraph fg = createSmoother(7, ordering).first;
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// create an ordering
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GaussianJunctionTree actual(fg);
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vector<Index> frontal1; frontal1 += ordering["x5"], ordering["x6"], ordering["x4"];
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vector<Index> frontal2; frontal2 += ordering["x3"], ordering["x2"];
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vector<Index> frontal3; frontal3 += ordering["x1"];
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vector<Index> frontal4; frontal4 += ordering["x7"];
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vector<Index> sep1;
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vector<Index> sep2; sep2 += ordering["x4"];
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vector<Index> sep3; sep3 += ordering["x2"];
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vector<Index> sep4; sep4 += ordering["x6"];
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CHECK(assert_equal(frontal1, actual.root()->frontal));
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CHECK(assert_equal(sep1, actual.root()->separator));
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LONGS_EQUAL(5, actual.root()->size());
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list<GaussianJunctionTree::sharedClique>::const_iterator child0it = actual.root()->children().begin();
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list<GaussianJunctionTree::sharedClique>::const_iterator child1it = child0it; ++child1it;
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GaussianJunctionTree::sharedClique child0 = *child0it;
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GaussianJunctionTree::sharedClique child1 = *child1it;
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CHECK(assert_equal(frontal2, child0->frontal));
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CHECK(assert_equal(sep2, child0->separator));
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LONGS_EQUAL(4, child0->size());
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CHECK(assert_equal(frontal3, child0->children().front()->frontal));
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CHECK(assert_equal(sep3, child0->children().front()->separator));
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LONGS_EQUAL(2, child0->children().front()->size());
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CHECK(assert_equal(frontal4, child1->frontal));
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CHECK(assert_equal(sep4, child1->separator));
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LONGS_EQUAL(2, child1->size());
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}
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/* ************************************************************************* */
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TEST( GaussianJunctionTree, optimizeMultiFrontal )
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{
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// create a graph
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GaussianFactorGraph fg;
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Ordering ordering;
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boost::tie(fg,ordering) = createSmoother(7);
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// optimize the graph
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GaussianJunctionTree tree(fg);
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VectorValues actual = tree.optimize();
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// verify
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VectorValues expected(GaussianVariableIndex<>(fg).dims()); // expected solution
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Vector v = Vector_(2, 0., 0.);
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for (int i=1; i<=7; i++)
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expected[ordering[Symbol('x',i)]] = v;
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianJunctionTree, optimizeMultiFrontal2)
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{
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// create a graph
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Graph nlfg = createNonlinearFactorGraph();
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Values noisy = createNoisyValues();
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Ordering ordering; ordering += "x1","x2","l1";
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GaussianFactorGraph fg = *nlfg.linearize(noisy, ordering);
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// optimize the graph
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GaussianJunctionTree tree(fg);
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VectorValues actual = tree.optimize();
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// verify
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VectorValues expected = createCorrectDelta(ordering); // expected solution
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CHECK(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST(GaussianJunctionTree, slamlike) {
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typedef planarSLAM::PoseKey PoseKey;
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typedef planarSLAM::PointKey PointKey;
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planarSLAM::Values init;
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planarSLAM::Graph newfactors;
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planarSLAM::Graph fullgraph;
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SharedDiagonal odoNoise = sharedSigmas(Vector_(3, 0.1, 0.1, M_PI/100.0));
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SharedDiagonal brNoise = sharedSigmas(Vector_(2, M_PI/100.0, 0.1));
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size_t i = 0;
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newfactors = planarSLAM::Graph();
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newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
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init.insert(PoseKey(0), Pose2(0.01, 0.01, 0.01));
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fullgraph.push_back(newfactors);
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for( ; i<5; ++i) {
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newfactors = planarSLAM::Graph();
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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init.insert(PoseKey(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullgraph.push_back(newfactors);
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}
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newfactors = planarSLAM::Graph();
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 0, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
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newfactors.addBearingRange(i, 1, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
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init.insert(PoseKey(i+1), Pose2(1.01, 0.01, 0.01));
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init.insert(PointKey(0), Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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init.insert(PointKey(1), Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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fullgraph.push_back(newfactors);
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++ i;
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for( ; i<5; ++i) {
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newfactors = planarSLAM::Graph();
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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init.insert(PoseKey(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullgraph.push_back(newfactors);
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}
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newfactors = planarSLAM::Graph();
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 0, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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newfactors.addBearingRange(i, 1, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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init.insert(PoseKey(i+1), Pose2(6.9, 0.1, 0.01));
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fullgraph.push_back(newfactors);
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++ i;
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// Compare solutions
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Ordering ordering = *fullgraph.orderingCOLAMD(init);
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GaussianFactorGraph linearized = *fullgraph.linearize(init, ordering);
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GaussianJunctionTree gjt(linearized);
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VectorValues deltaactual = gjt.optimize();
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planarSLAM::Values actual = init.expmap(deltaactual, ordering);
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GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
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VectorValues delta = optimize(gbn);
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planarSLAM::Values expected = init.expmap(delta, ordering);
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CHECK(assert_equal(expected, actual));
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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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