238 lines
9.1 KiB
C++
238 lines
9.1 KiB
C++
/* ----------------------------------------------------------------------------
|
|
|
|
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
|
* Atlanta, Georgia 30332-0415
|
|
* All Rights Reserved
|
|
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
|
|
|
* See LICENSE for the license information
|
|
|
|
* -------------------------------------------------------------------------- */
|
|
|
|
/**
|
|
* @file testGaussianJunctionTreeB.cpp
|
|
* @date Jul 8, 2010
|
|
* @author nikai
|
|
*/
|
|
|
|
#include <tests/smallExample.h>
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/slam/BetweenFactor.h>
|
|
#include <gtsam/slam/BearingRangeFactor.h>
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
#include <gtsam/nonlinear/Values.h>
|
|
#include <gtsam/nonlinear/Ordering.h>
|
|
#include <gtsam/nonlinear/Symbol.h>
|
|
#include <gtsam/linear/GaussianJunctionTree.h>
|
|
#include <gtsam/linear/GaussianSequentialSolver.h>
|
|
#include <gtsam/linear/GaussianMultifrontalSolver.h>
|
|
#include <gtsam/inference/BayesTree.h>
|
|
#include <gtsam/geometry/Pose2.h>
|
|
#include <gtsam/base/TestableAssertions.h>
|
|
#include <gtsam/base/debug.h>
|
|
#include <gtsam/base/cholesky.h>
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
|
|
#include <boost/assign/list_of.hpp>
|
|
#include <boost/assign/std/list.hpp> // for operator +=
|
|
#include <boost/assign/std/set.hpp> // for operator +=
|
|
#include <boost/assign/std/vector.hpp>
|
|
using namespace boost::assign;
|
|
|
|
#include <iostream>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
using namespace example;
|
|
|
|
using symbol_shorthand::X;
|
|
using symbol_shorthand::L;
|
|
|
|
/* ************************************************************************* *
|
|
Bayes tree for smoother with "nested dissection" ordering:
|
|
C1 x5 x6 x4
|
|
C2 x3 x2 : x4
|
|
C3 x1 : x2
|
|
C4 x7 : x6
|
|
*/
|
|
TEST( GaussianJunctionTreeB, constructor2 )
|
|
{
|
|
// create a graph
|
|
Ordering ordering; ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
|
GaussianFactorGraph fg = createSmoother(7, ordering).first;
|
|
|
|
// create an ordering
|
|
GaussianJunctionTree actual(fg);
|
|
|
|
vector<Index> frontal1; frontal1 += ordering[X(5)], ordering[X(6)], ordering[X(4)];
|
|
vector<Index> frontal2; frontal2 += ordering[X(3)], ordering[X(2)];
|
|
vector<Index> frontal3; frontal3 += ordering[X(1)];
|
|
vector<Index> frontal4; frontal4 += ordering[X(7)];
|
|
vector<Index> sep1;
|
|
vector<Index> sep2; sep2 += ordering[X(4)];
|
|
vector<Index> sep3; sep3 += ordering[X(2)];
|
|
vector<Index> sep4; sep4 += ordering[X(6)];
|
|
EXPECT(assert_equal(frontal1, actual.root()->frontal));
|
|
EXPECT(assert_equal(sep1, actual.root()->separator));
|
|
LONGS_EQUAL(5, actual.root()->size());
|
|
list<GaussianJunctionTree::sharedClique>::const_iterator child0it = actual.root()->children().begin();
|
|
list<GaussianJunctionTree::sharedClique>::const_iterator child1it = child0it; ++child1it;
|
|
GaussianJunctionTree::sharedClique child0 = *child0it;
|
|
GaussianJunctionTree::sharedClique child1 = *child1it;
|
|
EXPECT(assert_equal(frontal2, child0->frontal));
|
|
EXPECT(assert_equal(sep2, child0->separator));
|
|
LONGS_EQUAL(4, child0->size());
|
|
EXPECT(assert_equal(frontal3, child0->children().front()->frontal));
|
|
EXPECT(assert_equal(sep3, child0->children().front()->separator));
|
|
LONGS_EQUAL(2, child0->children().front()->size());
|
|
EXPECT(assert_equal(frontal4, child1->frontal));
|
|
EXPECT(assert_equal(sep4, child1->separator));
|
|
LONGS_EQUAL(2, child1->size());
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianJunctionTreeB, optimizeMultiFrontal )
|
|
{
|
|
// create a graph
|
|
GaussianFactorGraph fg;
|
|
Ordering ordering;
|
|
boost::tie(fg,ordering) = createSmoother(7);
|
|
|
|
// optimize the graph
|
|
GaussianJunctionTree tree(fg);
|
|
VectorValues actual = tree.optimize(&EliminateQR);
|
|
|
|
// verify
|
|
VectorValues expected(vector<size_t>(7,2)); // expected solution
|
|
Vector v = Vector_(2, 0., 0.);
|
|
for (int i=1; i<=7; i++)
|
|
expected[ordering[X(i)]] = v;
|
|
EXPECT(assert_equal(expected,actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianJunctionTreeB, optimizeMultiFrontal2)
|
|
{
|
|
// create a graph
|
|
example::Graph nlfg = createNonlinearFactorGraph();
|
|
Values noisy = createNoisyValues();
|
|
Ordering ordering; ordering += X(1),X(2),L(1);
|
|
GaussianFactorGraph fg = *nlfg.linearize(noisy, ordering);
|
|
|
|
// optimize the graph
|
|
GaussianJunctionTree tree(fg);
|
|
VectorValues actual = tree.optimize(&EliminateQR);
|
|
|
|
// verify
|
|
VectorValues expected = createCorrectDelta(ordering); // expected solution
|
|
EXPECT(assert_equal(expected,actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(GaussianJunctionTreeB, slamlike) {
|
|
Values init;
|
|
NonlinearFactorGraph newfactors;
|
|
NonlinearFactorGraph fullgraph;
|
|
SharedDiagonal odoNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.1, 0.1, M_PI/100.0));
|
|
SharedDiagonal brNoise = noiseModel::Diagonal::Sigmas(Vector_(2, M_PI/100.0, 0.1));
|
|
|
|
size_t i = 0;
|
|
|
|
newfactors = NonlinearFactorGraph();
|
|
newfactors.add(PriorFactor<Pose2>(X(0), Pose2(0.0, 0.0, 0.0), odoNoise));
|
|
init.insert(X(0), Pose2(0.01, 0.01, 0.01));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
for( ; i<5; ++i) {
|
|
newfactors = NonlinearFactorGraph();
|
|
newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullgraph.push_back(newfactors);
|
|
}
|
|
|
|
newfactors = NonlinearFactorGraph();
|
|
newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0), 5.0, brNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise));
|
|
init.insert(X(i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(L(0), Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(L(1), Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullgraph.push_back(newfactors);
|
|
++ i;
|
|
|
|
for( ; i<5; ++i) {
|
|
newfactors = NonlinearFactorGraph();
|
|
newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullgraph.push_back(newfactors);
|
|
}
|
|
|
|
newfactors = NonlinearFactorGraph();
|
|
newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
|
|
init.insert(X(i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullgraph.push_back(newfactors);
|
|
++ i;
|
|
|
|
// Compare solutions
|
|
Ordering ordering = *fullgraph.orderingCOLAMD(init);
|
|
GaussianFactorGraph linearized = *fullgraph.linearize(init, ordering);
|
|
|
|
GaussianJunctionTree gjt(linearized);
|
|
VectorValues deltaactual = gjt.optimize(&EliminateQR);
|
|
Values actual = init.retract(deltaactual, ordering);
|
|
|
|
GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
|
|
VectorValues delta = optimize(gbn);
|
|
Values expected = init.retract(delta, ordering);
|
|
|
|
EXPECT(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(GaussianJunctionTreeB, simpleMarginal) {
|
|
|
|
typedef BayesTree<GaussianConditional> GaussianBayesTree;
|
|
|
|
// Create a simple graph
|
|
NonlinearFactorGraph fg;
|
|
fg.add(PriorFactor<Pose2>(X(0), Pose2(), noiseModel::Isotropic::Sigma(3, 10.0)));
|
|
fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0, 0.0, 0.0), noiseModel::Diagonal::Sigmas(Vector_(3, 10.0, 1.0, 1.0))));
|
|
|
|
Values init;
|
|
init.insert(X(0), Pose2());
|
|
init.insert(X(1), Pose2(1.0, 0.0, 0.0));
|
|
|
|
Ordering ordering;
|
|
ordering += X(1), X(0);
|
|
|
|
GaussianFactorGraph gfg = *fg.linearize(init, ordering);
|
|
|
|
// Compute marginals with both sequential and multifrontal
|
|
Matrix expected = GaussianSequentialSolver(gfg).marginalCovariance(1);
|
|
|
|
Matrix actual1 = GaussianMultifrontalSolver(gfg).marginalCovariance(1);
|
|
|
|
// Compute marginal directly from marginal factor
|
|
GaussianFactor::shared_ptr marginalFactor = GaussianMultifrontalSolver(gfg).marginalFactor(1);
|
|
JacobianFactor::shared_ptr marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
|
|
Matrix actual2 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
|
|
|
|
// Compute marginal directly from BayesTree
|
|
GaussianBayesTree gbt;
|
|
gbt.insert(GaussianJunctionTree(gfg).eliminate(EliminateCholesky));
|
|
marginalFactor = gbt.marginalFactor(1, EliminateCholesky);
|
|
marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
|
|
Matrix actual3 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
|
|
|
|
EXPECT(assert_equal(expected, actual1));
|
|
EXPECT(assert_equal(expected, actual2));
|
|
EXPECT(assert_equal(expected, actual3));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|