1248 lines
44 KiB
C++
1248 lines
44 KiB
C++
/**
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* @file testGaussianISAM2.cpp
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* @brief Unit tests for GaussianISAM2
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* @author Michael Kaess
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*/
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#include <gtsam/base/debug.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/linear/GaussianBayesTree.h>
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <tests/smallExample.h>
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#include <gtsam/slam/planarSLAM.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/foreach.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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#include <boost/assign.hpp>
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using namespace boost::assign;
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using namespace std;
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using namespace gtsam;
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using boost::shared_ptr;
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const double tol = 1e-4;
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// SETDEBUG("ISAM2 update", true);
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// SETDEBUG("ISAM2 update verbose", true);
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// SETDEBUG("ISAM2 recalculate", true);
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// Set up parameters
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SharedDiagonal odoNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.1, 0.1, M_PI/100.0));
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SharedDiagonal brNoise = noiseModel::Diagonal::Sigmas(Vector_(2, M_PI/100.0, 0.1));
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ISAM2 createSlamlikeISAM2(
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boost::optional<Values&> init_values = boost::none,
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boost::optional<planarSLAM::Graph&> full_graph = boost::none,
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const ISAM2Params& params = ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false, true)) {
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// These variables will be reused and accumulate factors and values
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ISAM2 isam(params);
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// ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false, true));
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Values fullinit;
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planarSLAM::Graph fullgraph;
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// i keeps track of the time step
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size_t i = 0;
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// Add a prior at time 0 and update isam
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{
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planarSLAM::Graph newfactors;
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newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((0), Pose2(0.01, 0.01, 0.01));
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fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 0 to time 5
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for( ; i<5; ++i) {
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 5 to 6 and landmark measurement at time 5
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{
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
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newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(1.01, 0.01, 0.01));
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init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
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fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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isam.update(newfactors, init);
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++ i;
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}
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// Add odometry from time 6 to time 10
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for( ; i<10; ++i) {
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 10 to 11 and landmark measurement at time 10
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{
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(6.9, 0.1, 0.01));
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fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
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isam.update(newfactors, init);
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++ i;
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}
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if (full_graph)
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*full_graph = fullgraph;
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if (init_values)
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*init_values = fullinit;
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return isam;
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, AddVariables) {
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// Create initial state
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Values theta;
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theta.insert((0), Pose2(.1, .2, .3));
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theta.insert(100, Point2(.4, .5));
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Values newTheta;
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newTheta.insert((1), Pose2(.6, .7, .8));
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VectorValues deltaUnpermuted;
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deltaUnpermuted.insert(0, Vector_(3, .1, .2, .3));
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deltaUnpermuted.insert(1, Vector_(2, .4, .5));
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Permutation permutation(2);
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permutation[0] = 1;
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permutation[1] = 0;
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Permuted<VectorValues> delta(permutation, deltaUnpermuted);
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VectorValues deltaNewtonUnpermuted;
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deltaNewtonUnpermuted.insert(0, Vector_(3, .1, .2, .3));
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deltaNewtonUnpermuted.insert(1, Vector_(2, .4, .5));
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Permutation permutationNewton(2);
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permutationNewton[0] = 1;
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permutationNewton[1] = 0;
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Permuted<VectorValues> deltaNewton(permutationNewton, deltaNewtonUnpermuted);
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VectorValues deltaRgUnpermuted;
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deltaRgUnpermuted.insert(0, Vector_(3, .1, .2, .3));
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deltaRgUnpermuted.insert(1, Vector_(2, .4, .5));
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Permutation permutationRg(2);
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permutationRg[0] = 1;
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permutationRg[1] = 0;
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Permuted<VectorValues> deltaRg(permutationRg, deltaRgUnpermuted);
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vector<bool> replacedKeys(2, false);
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Ordering ordering; ordering += 100, (0);
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ISAM2::Nodes nodes(2);
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// Verify initial state
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LONGS_EQUAL(0, ordering[100]);
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LONGS_EQUAL(1, ordering[(0)]);
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EXPECT(assert_equal(deltaUnpermuted[1], delta[ordering[100]]));
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EXPECT(assert_equal(deltaUnpermuted[0], delta[ordering[(0)]]));
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// Create expected state
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Values thetaExpected;
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thetaExpected.insert((0), Pose2(.1, .2, .3));
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thetaExpected.insert(100, Point2(.4, .5));
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thetaExpected.insert((1), Pose2(.6, .7, .8));
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VectorValues deltaUnpermutedExpected;
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deltaUnpermutedExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaUnpermutedExpected.insert(1, Vector_(2, .4, .5));
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deltaUnpermutedExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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Permutation permutationExpected(3);
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permutationExpected[0] = 1;
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permutationExpected[1] = 0;
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permutationExpected[2] = 2;
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Permuted<VectorValues> deltaExpected(permutationExpected, deltaUnpermutedExpected);
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VectorValues deltaNewtonUnpermutedExpected;
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deltaNewtonUnpermutedExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaNewtonUnpermutedExpected.insert(1, Vector_(2, .4, .5));
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deltaNewtonUnpermutedExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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Permutation permutationNewtonExpected(3);
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permutationNewtonExpected[0] = 1;
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permutationNewtonExpected[1] = 0;
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permutationNewtonExpected[2] = 2;
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Permuted<VectorValues> deltaNewtonExpected(permutationNewtonExpected, deltaNewtonUnpermutedExpected);
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VectorValues deltaRgUnpermutedExpected;
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deltaRgUnpermutedExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaRgUnpermutedExpected.insert(1, Vector_(2, .4, .5));
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deltaRgUnpermutedExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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Permutation permutationRgExpected(3);
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permutationRgExpected[0] = 1;
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permutationRgExpected[1] = 0;
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permutationRgExpected[2] = 2;
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Permuted<VectorValues> deltaRgExpected(permutationRgExpected, deltaRgUnpermutedExpected);
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vector<bool> replacedKeysExpected(3, false);
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Ordering orderingExpected; orderingExpected += 100, (0), (1);
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ISAM2::Nodes nodesExpected(
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3, ISAM2::sharedClique());
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// Expand initial state
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ISAM2::Impl::AddVariables(newTheta, theta, delta, deltaNewton, deltaRg, replacedKeys, ordering, nodes);
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EXPECT(assert_equal(thetaExpected, theta));
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EXPECT(assert_equal(deltaUnpermutedExpected, deltaUnpermuted));
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EXPECT(assert_equal(deltaExpected.permutation(), delta.permutation()));
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EXPECT(assert_equal(deltaNewtonUnpermutedExpected, deltaNewtonUnpermuted));
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EXPECT(assert_equal(deltaNewtonExpected.permutation(), deltaNewton.permutation()));
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EXPECT(assert_equal(deltaRgUnpermutedExpected, deltaRgUnpermuted));
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EXPECT(assert_equal(deltaRgExpected.permutation(), deltaRg.permutation()));
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EXPECT(assert_container_equality(replacedKeysExpected, replacedKeys));
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EXPECT(assert_equal(orderingExpected, ordering));
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}
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/* ************************************************************************* */
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//TEST(ISAM2, IndicesFromFactors) {
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//
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// using namespace gtsam::planarSLAM;
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// typedef GaussianISAM2<Values>::Impl Impl;
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//
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// Ordering ordering; ordering += (0), (0), (1);
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// planarSLAM::Graph graph;
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// graph.addPrior((0), Pose2(), noiseModel::Unit::Create(Pose2::dimension));
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// graph.addRange((0), (0), 1.0, noiseModel::Unit::Create(1));
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//
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// FastSet<Index> expected;
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// expected.insert(0);
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// expected.insert(1);
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//
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// FastSet<Index> actual = Impl::IndicesFromFactors(ordering, graph);
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//
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// EXPECT(assert_equal(expected, actual));
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//}
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/* ************************************************************************* */
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//TEST(ISAM2, CheckRelinearization) {
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//
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// typedef GaussianISAM2<Values>::Impl Impl;
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//
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// // Create values where indices 1 and 3 are above the threshold of 0.1
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// VectorValues values;
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// values.reserve(4, 10);
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// values.push_back_preallocated(Vector_(2, 0.09, 0.09));
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// values.push_back_preallocated(Vector_(3, 0.11, 0.11, 0.09));
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// values.push_back_preallocated(Vector_(3, 0.09, 0.09, 0.09));
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// values.push_back_preallocated(Vector_(2, 0.11, 0.11));
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//
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// // Create a permutation
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// Permutation permutation(4);
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// permutation[0] = 2;
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// permutation[1] = 0;
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// permutation[2] = 1;
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// permutation[3] = 3;
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//
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// Permuted<VectorValues> permuted(permutation, values);
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//
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// // After permutation, the indices above the threshold are 2 and 2
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// FastSet<Index> expected;
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// expected.insert(2);
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// expected.insert(3);
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//
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// // Indices checked by CheckRelinearization
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// FastSet<Index> actual = Impl::CheckRelinearization(permuted, 0.1);
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//
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// EXPECT(assert_equal(expected, actual));
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//}
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/* ************************************************************************* */
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TEST(ISAM2, optimize2) {
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// Create initialization
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Values theta;
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theta.insert((0), Pose2(0.01, 0.01, 0.01));
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// Create conditional
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Vector d(3); d << -0.1, -0.1, -0.31831;
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Matrix R(3,3); R <<
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10, 0.0, 0.0,
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0.0, 10, 0.0,
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0.0, 0.0, 31.8309886;
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GaussianConditional::shared_ptr conditional(new GaussianConditional(0, d, R, Vector::Ones(3)));
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// Create ordering
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Ordering ordering; ordering += (0);
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// Expected vector
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VectorValues expected(1, 3);
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conditional->solveInPlace(expected);
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// Clique
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ISAM2::sharedClique clique(
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ISAM2::Clique::Create(make_pair(conditional,GaussianFactor::shared_ptr())));
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VectorValues actual(theta.dims(ordering));
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internal::optimizeInPlace<ISAM2::Base>(clique, actual);
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// expected.print("expected: ");
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// actual.print("actual: ");
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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bool isam_check(const planarSLAM::Graph& fullgraph, const Values& fullinit, const ISAM2& isam) {
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Values actual = isam.calculateEstimate();
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Ordering ordering = isam.getOrdering(); // *fullgraph.orderingCOLAMD(fullinit).first;
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GaussianFactorGraph linearized = *fullgraph.linearize(fullinit, ordering);
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// linearized.print("Expected linearized: ");
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GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
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// gbn.print("Expected bayesnet: ");
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VectorValues delta = optimize(gbn);
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Values expected = fullinit.retract(delta, ordering);
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return assert_equal(expected, actual);
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}
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/* ************************************************************************* */
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TEST(ISAM2, slamlike_solution_gaussnewton)
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{
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// These variables will be reused and accumulate factors and values
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ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
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Values fullinit;
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planarSLAM::Graph fullgraph;
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// i keeps track of the time step
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size_t i = 0;
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// Add a prior at time 0 and update isam
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{
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planarSLAM::Graph newfactors;
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newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((0), Pose2(0.01, 0.01, 0.01));
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fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
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isam.update(newfactors, init);
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}
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CHECK(isam_check(fullgraph, fullinit, isam));
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// Add odometry from time 0 to time 5
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for( ; i<5; ++i) {
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 5 to 6 and landmark measurement at time 5
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{
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
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newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(1.01, 0.01, 0.01));
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init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
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fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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isam.update(newfactors, init);
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++ i;
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}
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// Add odometry from time 6 to time 10
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for( ; i<10; ++i) {
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 10 to 11 and landmark measurement at time 10
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{
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planarSLAM::Graph newfactors;
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newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
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newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(6.9, 0.1, 0.01));
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fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
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isam.update(newfactors, init);
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++ i;
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}
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// Compare solutions
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CHECK(isam_check(fullgraph, fullinit, isam));
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// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_dogleg)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2DoglegParams(1.0), 0.0, 0, false));
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_gaussnewton_qr)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false, false, ISAM2Params::QR));
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_dogleg_qr)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2DoglegParams(1.0), 0.0, 0, false, false, ISAM2Params::QR));
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, clone) {
|
|
|
|
ISAM2 clone1;
|
|
|
|
{
|
|
ISAM2 isam = createSlamlikeISAM2();
|
|
clone1 = isam;
|
|
|
|
ISAM2 clone2(isam);
|
|
|
|
// Modify original isam
|
|
NonlinearFactorGraph factors;
|
|
factors.add(BetweenFactor<Pose2>(0, 10,
|
|
isam.calculateEstimate<Pose2>(0).between(isam.calculateEstimate<Pose2>(10)), noiseModel::Unit::Create(3)));
|
|
isam.update(factors);
|
|
|
|
CHECK(assert_equal(createSlamlikeISAM2(), clone2));
|
|
}
|
|
|
|
// This is to (perhaps unsuccessfully) try to currupt unallocated memory referenced
|
|
// if the references in the iSAM2 copy point to the old instance which deleted at
|
|
// the end of the {...} section above.
|
|
ISAM2 temp = createSlamlikeISAM2();
|
|
|
|
CHECK(assert_equal(createSlamlikeISAM2(), clone1));
|
|
CHECK(assert_equal(clone1, temp));
|
|
|
|
// Check clone empty
|
|
ISAM2 isam;
|
|
clone1 = isam;
|
|
CHECK(assert_equal(ISAM2(), clone1));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, permute_cached) {
|
|
typedef boost::shared_ptr<ISAM2Clique> sharedISAM2Clique;
|
|
|
|
// Construct expected permuted BayesTree (variable 2 has been changed to 1)
|
|
BayesTree<GaussianConditional, ISAM2Clique> expected;
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(3, Matrix_(1,1,1.0))
|
|
(4, Matrix_(1,1,2.0)),
|
|
2, Vector_(1,1.0), Vector_(1,1.0)), // p(3,4)
|
|
HessianFactor::shared_ptr())))); // Cached: empty
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(2, Matrix_(1,1,1.0))
|
|
(3, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(2|3)
|
|
boost::make_shared<HessianFactor>(3, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(3)
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(0, Matrix_(1,1,1.0))
|
|
(2, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(0|2)
|
|
boost::make_shared<HessianFactor>(1, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(1)
|
|
// Change variable 2 to 1
|
|
expected.root()->children().front()->conditional()->keys()[0] = 1;
|
|
expected.root()->children().front()->children().front()->conditional()->keys()[1] = 1;
|
|
|
|
// Construct unpermuted BayesTree
|
|
BayesTree<GaussianConditional, ISAM2Clique> actual;
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(3, Matrix_(1,1,1.0))
|
|
(4, Matrix_(1,1,2.0)),
|
|
2, Vector_(1,1.0), Vector_(1,1.0)), // p(3,4)
|
|
HessianFactor::shared_ptr())))); // Cached: empty
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(2, Matrix_(1,1,1.0))
|
|
(3, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(2|3)
|
|
boost::make_shared<HessianFactor>(3, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(3)
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(0, Matrix_(1,1,1.0))
|
|
(2, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(0|2)
|
|
boost::make_shared<HessianFactor>(2, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(2)
|
|
|
|
// Create permutation that changes variable 2 -> 0
|
|
Permutation permutation = Permutation::Identity(5);
|
|
permutation[2] = 1;
|
|
|
|
// Permute BayesTree
|
|
actual.root()->permuteWithInverse(permutation);
|
|
|
|
// Check
|
|
EXPECT(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, removeFactors)
|
|
{
|
|
// This test builds a graph in the same way as the "slamlike" test above, but
|
|
// then removes the 2nd-to-last landmark measurement
|
|
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
fullgraph.push_back(newfactors[0]);
|
|
fullgraph.push_back(newfactors[2]); // Don't add measurement on landmark 0
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
ISAM2Result result = isam.update(newfactors, init);
|
|
++ i;
|
|
|
|
// Remove the measurement on landmark 0
|
|
FastVector<size_t> toRemove;
|
|
EXPECT_LONGS_EQUAL(isam.getFactorsUnsafe().size()-2, result.newFactorsIndices[1]);
|
|
toRemove.push_back(result.newFactorsIndices[1]);
|
|
isam.update(planarSLAM::Graph(), Values(), toRemove);
|
|
}
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, swapFactors)
|
|
{
|
|
// This test builds a graph in the same way as the "slamlike" test above, but
|
|
// then swaps the 2nd-to-last landmark measurement with a different one
|
|
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph);
|
|
|
|
// Remove the measurement on landmark 0 and replace with a different one
|
|
{
|
|
size_t swap_idx = isam.getFactorsUnsafe().size()-2;
|
|
FastVector<size_t> toRemove;
|
|
toRemove.push_back(swap_idx);
|
|
fullgraph.remove(swap_idx);
|
|
|
|
planarSLAM::Graph swapfactors;
|
|
// swapfactors.addBearingRange(10, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise); // original factor
|
|
swapfactors.addBearingRange(10, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 5.0, brNoise);
|
|
fullgraph.push_back(swapfactors);
|
|
isam.update(swapfactors, Values(), toRemove);
|
|
}
|
|
|
|
// Compare solutions
|
|
EXPECT(assert_equal(fullgraph, planarSLAM::Graph(isam.getFactorsUnsafe())));
|
|
EXPECT(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
EXPECT_LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, constrained_ordering)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
Values fullinit;
|
|
planarSLAM::Graph fullgraph;
|
|
|
|
// We will constrain x3 and x4 to the end
|
|
FastMap<Key, int> constrained;
|
|
constrained.insert(make_pair((3), 1));
|
|
constrained.insert(make_pair((4), 2));
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addPrior(0, Pose2(0.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
if(i >= 3)
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
else
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
planarSLAM::Graph newfactors;
|
|
newfactors.addOdometry(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise);
|
|
newfactors.addBearingRange(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
newfactors.addBearingRange(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise);
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
++ i;
|
|
}
|
|
|
|
// Compare solutions
|
|
EXPECT(isam_check(fullgraph, fullinit, isam));
|
|
|
|
// Check that x3 and x4 are last, but either can come before the other
|
|
EXPECT(isam.getOrdering()[(3)] == 12 && isam.getOrdering()[(4)] == 13);
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|