598 lines
21 KiB
C++
598 lines
21 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 testGaussianFactorGraphB.cpp
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* @brief Unit tests for Linear Factor Graph
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* @author Christian Potthast
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**/
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#include <tests/smallExample.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianBayesTree.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Testable.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/tuple/tuple.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> // for operator +=
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using namespace boost::assign;
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#include <boost/range/adaptor/map.hpp>
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namespace br { using namespace boost::range; using namespace boost::adaptors; }
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#include <string.h>
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#include <iostream>
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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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double tol=1e-5;
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, equals ) {
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianFactorGraph fg2 = createGaussianFactorGraph();
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EXPECT(fg.equals(fg2));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, error ) {
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GaussianFactorGraph fg = createGaussianFactorGraph();
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VectorValues cfg = createZeroDelta();
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// note the error is the same as in testNonlinearFactorGraph as a
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// zero delta config in the linear graph is equivalent to noisy in
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// non-linear, which is really linear under the hood
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double actual = fg.error(cfg);
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DOUBLES_EQUAL( 5.625, actual, 1e-9 );
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_x1 )
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{
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GaussianFactorGraph fg = createGaussianFactorGraph();
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GaussianConditional::shared_ptr conditional;
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pair<GaussianBayesNet::shared_ptr, GaussianFactorGraph::shared_ptr> result =
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fg.eliminatePartialSequential(Ordering(list_of(X(1))));
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conditional = result.first->front();
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// create expected Conditional Gaussian
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Matrix I = 15*I_2x2, R11 = I, S12 = -0.111111*I, S13 = -0.444444*I;
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Vector d = Vector2(-0.133333, -0.0222222);
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GaussianConditional expected(X(1),15*d,R11,L(1),S12,X(2),S13);
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EXPECT(assert_equal(expected,*conditional,tol));
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}
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#if 0
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_x2 )
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{
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Ordering ordering; ordering += X(2),L(1),X(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = fg.eliminateOne(0, EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = 0.0894427;
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Matrix I = I_2x2/sig, R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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Vector d = Vector2(0.2, -0.14)/sig, sigma = Vector::Ones(2);
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GaussianConditional expected(ordering[X(2)],d,R11,ordering[L(1)],S12,ordering[X(1)],S13,sigma);
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EXPECT(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_l1 )
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{
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Ordering ordering; ordering += L(1),X(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = fg.eliminateOne(0, EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = sqrt(2.0)/10.;
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Matrix I = I_2x2/sig, R11 = I, S12 = -0.5*I, S13 = -0.5*I;
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Vector d = Vector2(-0.1, 0.25)/sig, sigma = Vector::Ones(2);
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GaussianConditional expected(ordering[L(1)],d,R11,ordering[X(1)],S12,ordering[X(2)],S13,sigma);
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EXPECT(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_x1_fast )
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{
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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr conditional;
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GaussianFactorGraph remaining;
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boost::tie(conditional,remaining) = fg.eliminateOne(ordering[X(1)], EliminateQR);
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// create expected Conditional Gaussian
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Matrix I = 15*I_2x2, R11 = I, S12 = -0.111111*I, S13 = -0.444444*I;
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Vector d = Vector2(-0.133333, -0.0222222), sigma = Vector::Ones(2);
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GaussianConditional expected(ordering[X(1)],15*d,R11,ordering[L(1)],S12,ordering[X(2)],S13,sigma);
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// Create expected remaining new factor
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JacobianFactor expectedFactor(1, (Matrix(4,2) <<
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4.714045207910318, 0.,
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0., 4.714045207910318,
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0., 0.,
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0., 0.).finished(),
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2, (Matrix(4,2) <<
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-2.357022603955159, 0.,
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0., -2.357022603955159,
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7.071067811865475, 0.,
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0., 7.071067811865475).finished(),
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(Vector(4) << -0.707106781186547, 0.942809041582063, 0.707106781186547, -1.414213562373094).finished(), noiseModel::Unit::Create(4));
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EXPECT(assert_equal(expected,*conditional,tol));
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EXPECT(assert_equal((const GaussianFactor&)expectedFactor,*remaining.back(),tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_x2_fast )
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{
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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = fg.eliminateOne(ordering[X(2)], EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = 0.0894427;
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Matrix I = I_2x2/sig, R11 = I, S12 = -0.2*I, S13 = -0.8*I;
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Vector d = Vector2(0.2, -0.14)/sig, sigma = Vector::Ones(2);
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GaussianConditional expected(ordering[X(2)],d,R11,ordering[X(1)],S13,ordering[L(1)],S12,sigma);
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EXPECT(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateOne_l1_fast )
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{
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Ordering ordering; ordering += X(1),L(1),X(2);
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GaussianFactorGraph fg = createGaussianFactorGraph(ordering);
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GaussianConditional::shared_ptr actual = fg.eliminateOne(ordering[L(1)], EliminateQR).first;
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// create expected Conditional Gaussian
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double sig = sqrt(2.0)/10.;
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Matrix I = I_2x2/sig, R11 = I, S12 = -0.5*I, S13 = -0.5*I;
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Vector d = Vector2(-0.1, 0.25)/sig, sigma = Vector::Ones(2);
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GaussianConditional expected(ordering[L(1)],d,R11,ordering[X(1)],S12,ordering[X(2)],S13,sigma);
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EXPECT(assert_equal(expected,*actual,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, eliminateAll )
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{
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// create expected Chordal bayes Net
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Matrix I = I_2x2;
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Ordering ordering;
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ordering += X(2),L(1),X(1);
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Vector d1 = Vector2(-0.1,-0.1);
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GaussianBayesNet expected = simpleGaussian(ordering[X(1)],d1,0.1);
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double sig1 = 0.149071;
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Vector d2 = Vector2(0.0, 0.2)/sig1, sigma2 = Vector::Ones(2);
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push_front(expected,ordering[L(1)],d2, I/sig1,ordering[X(1)], (-1)*I/sig1,sigma2);
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double sig2 = 0.0894427;
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Vector d3 = Vector2(0.2, -0.14)/sig2, sigma3 = Vector::Ones(2);
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push_front(expected,ordering[X(2)],d3, I/sig2,ordering[L(1)], (-0.2)*I/sig2, ordering[X(1)], (-0.8)*I/sig2, sigma3);
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// Check one ordering
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ordering);
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GaussianBayesNet actual = *GaussianSequentialSolver(fg1).eliminate();
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EXPECT(assert_equal(expected,actual,tol));
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GaussianBayesNet actualQR = *GaussianSequentialSolver(fg1, true).eliminate();
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EXPECT(assert_equal(expected,actualQR,tol));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, copying )
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{
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// Create a graph
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Ordering ordering; ordering += X(2),L(1),X(1);
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GaussianFactorGraph actual = createGaussianFactorGraph(ordering);
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// Copy the graph !
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GaussianFactorGraph copy = actual;
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// now eliminate the copy
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GaussianBayesNet actual1 = *GaussianSequentialSolver(copy).eliminate();
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// Create the same graph, but not by copying
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GaussianFactorGraph expected = createGaussianFactorGraph(ordering);
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// and check that original is still the same graph
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, CONSTRUCTOR_GaussianBayesNet )
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{
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Ordering ord;
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ord += X(2),L(1),X(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// render with a given ordering
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GaussianBayesNet CBN = *GaussianSequentialSolver(fg).eliminate();
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// True GaussianFactorGraph
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GaussianFactorGraph fg2(CBN);
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GaussianBayesNet CBN2 = *GaussianSequentialSolver(fg2).eliminate();
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EXPECT(assert_equal(CBN,CBN2));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, getOrdering)
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{
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Ordering original; original += L(1),X(1),X(2);
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FactorGraph<IndexFactor> symbolic(createGaussianFactorGraph(original));
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Permutation perm(*inference::PermutationCOLAMD(VariableIndex(symbolic)));
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Ordering actual = original; actual.permuteInPlace(perm);
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Ordering expected; expected += L(1),X(2),X(1);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, optimize_Cholesky )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// optimize the graph
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VectorValues actual = *GaussianSequentialSolver(fg, false).optimize();
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// verify
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VectorValues expected = createCorrectDelta(ord);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, optimize_QR )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a graph
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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// optimize the graph
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VectorValues actual = *GaussianSequentialSolver(fg, true).optimize();
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// verify
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VectorValues expected = createCorrectDelta(ord);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, combine)
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
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// get sizes
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size_t size1 = fg1.size();
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size_t size2 = fg2.size();
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// combine them
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fg1.combine(fg2);
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EXPECT(size1+size2 == fg1.size());
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, combine2)
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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// create a test graph
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GaussianFactorGraph fg1 = createGaussianFactorGraph(ord);
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// create another factor graph
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GaussianFactorGraph fg2 = createGaussianFactorGraph(ord);
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// get sizes
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size_t size1 = fg1.size();
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size_t size2 = fg2.size();
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// combine them
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GaussianFactorGraph fg3 = GaussianFactorGraph::combine2(fg1, fg2);
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EXPECT(size1+size2 == fg3.size());
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}
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/* ************************************************************************* */
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// print a vector of ints if needed for debugging
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void print(vector<int> v) {
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for (size_t k = 0; k < v.size(); k++)
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cout << v[k] << " ";
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cout << endl;
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}
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/* ************************************************************************* */
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TEST(GaussianFactorGraph, createSmoother)
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{
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GaussianFactorGraph fg1 = createSmoother(2).first;
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LONGS_EQUAL(3,fg1.size());
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GaussianFactorGraph fg2 = createSmoother(3).first;
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LONGS_EQUAL(5,fg2.size());
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}
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/* ************************************************************************* */
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double error(const VectorValues& x) {
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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GaussianFactorGraph fg = createGaussianFactorGraph(ord);
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return fg.error(x);
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, multiplication )
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{
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// create an ordering
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Ordering ord; ord += X(2),L(1),X(1);
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GaussianFactorGraph A = createGaussianFactorGraph(ord);
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VectorValues x = createCorrectDelta(ord);
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Errors actual = A * x;
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Errors expected;
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expected += Vector2(-1.0,-1.0);
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expected += Vector2(2.0,-1.0);
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expected += Vector2(0.0, 1.0);
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expected += Vector2(-1.0, 1.5);
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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// Extra test on elimination prompted by Michael's email to Frank 1/4/2010
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TEST( GaussianFactorGraph, elimination )
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{
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Ordering ord;
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ord += X(1), X(2);
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// Create Gaussian Factor Graph
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GaussianFactorGraph fg;
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Matrix Ap = I_2x2, An = I_2x2 * -1;
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Vector b = (Vector(1) << 0.0).finished();
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SharedDiagonal sigma = noiseModel::Isotropic::Sigma(1,2.0);
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fg += ord[X(1)], An, ord[X(2)], Ap, b, sigma;
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fg += ord[X(1)], Ap, b, sigma;
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fg += ord[X(2)], Ap, b, sigma;
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// Eliminate
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GaussianBayesNet bayesNet = *GaussianSequentialSolver(fg).eliminate();
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// Check sigma
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EXPECT_DOUBLES_EQUAL(1.0,bayesNet[ord[X(2)]]->get_sigmas()(0),1e-5);
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// Check matrix
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Matrix R;Vector d;
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boost::tie(R,d) = matrix(bayesNet);
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Matrix expected = (Matrix(2, 2) <<
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0.707107, -0.353553,
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0.0, 0.612372).finished();
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Matrix expected2 = (Matrix(2, 2) <<
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0.707107, -0.353553,
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0.0, -0.612372).finished();
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EXPECT(equal_with_abs_tol(expected, R, 1e-6) || equal_with_abs_tol(expected2, R, 1e-6));
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}
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/* ************************************************************************* */
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// Tests ported from ConstrainedGaussianFactorGraph
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_simple )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createSimpleConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createSimpleConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_single )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createSingleConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createSingleConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST( GaussianFactorGraph, constrained_multi1 )
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{
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// get a graph with a constraint in it
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GaussianFactorGraph fg = createMultiConstraintGraph();
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EXPECT(hasConstraints(fg));
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// eliminate and solve
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VectorValues actual = *GaussianSequentialSolver(fg).optimize();
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// verify
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VectorValues expected = createMultiConstraintValues();
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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static SharedDiagonal model = noiseModel::Isotropic::Sigma(2,1);
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/* ************************************************************************* */
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TEST(GaussianFactorGraph, replace)
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{
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Ordering ord; ord += X(1),X(2),X(3),X(4),X(5),X(6);
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SharedDiagonal noise(noiseModel::Isotropic::Sigma(3, 1.0));
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GaussianFactorGraph::sharedFactor f1(new JacobianFactor(
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ord[X(1)], I_3x3, ord[X(2)], I_3x3, Z_3x1, noise));
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GaussianFactorGraph::sharedFactor f2(new JacobianFactor(
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ord[X(2)], I_3x3, ord[X(3)], I_3x3, Z_3x1, noise));
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GaussianFactorGraph::sharedFactor f3(new JacobianFactor(
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ord[X(3)], I_3x3, ord[X(4)], I_3x3, Z_3x1, noise));
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GaussianFactorGraph::sharedFactor f4(new JacobianFactor(
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ord[X(5)], I_3x3, ord[X(6)], I_3x3, Z_3x1, noise));
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GaussianFactorGraph actual;
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actual.push_back(f1);
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actual.push_back(f2);
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actual.push_back(f3);
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actual.replace(0, f4);
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GaussianFactorGraph expected;
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expected.push_back(f4);
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expected.push_back(f2);
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expected.push_back(f3);
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|
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EXPECT(assert_equal(expected, actual));
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}
|
|
|
|
/* ************************************************************************* */
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TEST(GaussianFactorGraph, createSmoother2)
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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());
|
|
|
|
// eliminate
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|
vector<Index> x3var; x3var.push_back(ordering[X(3)]);
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vector<Index> x1var; x1var.push_back(ordering[X(1)]);
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|
GaussianBayesNet p_x3 = *GaussianSequentialSolver(
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*GaussianSequentialSolver(fg2).jointFactorGraph(x3var)).eliminate();
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|
GaussianBayesNet p_x1 = *GaussianSequentialSolver(
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*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
|
|
}
|
|
|
|
#endif
|
|
|
|
/* ************************************************************************* */
|
|
TEST(GaussianFactorGraph, hasConstraints)
|
|
{
|
|
FactorGraph<GaussianFactor> fgc1 = createMultiConstraintGraph();
|
|
EXPECT(hasConstraints(fgc1));
|
|
|
|
FactorGraph<GaussianFactor> fgc2 = createSimpleConstraintGraph() ;
|
|
EXPECT(hasConstraints(fgc2));
|
|
|
|
GaussianFactorGraph fg = createGaussianFactorGraph();
|
|
EXPECT(!hasConstraints(fg));
|
|
}
|
|
|
|
#include <gtsam/slam/ProjectionFactor.h>
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|
#include <gtsam/geometry/Pose3.h>
|
|
#include <gtsam/slam/PriorFactor.h>
|
|
#include <gtsam/sam/RangeFactor.h>
|
|
|
|
/* ************************************************************************* */
|
|
TEST( GaussianFactorGraph, conditional_sigma_failure) {
|
|
// This system derives from a failure case in DDF in which a Bayes Tree
|
|
// has non-unit sigmas for conditionals in the Bayes Tree, which
|
|
// should never happen by construction
|
|
|
|
// Reason for the failure: using Vector_() is dangerous as having a non-float gets set to zero, resulting in constraints
|
|
gtsam::Key xC1 = 0, l32 = 1, l41 = 2;
|
|
|
|
// noisemodels at nonlinear level
|
|
gtsam::SharedNoiseModel priorModel = noiseModel::Diagonal::Sigmas((Vector(6) << 0.05, 0.05, 3.0, 0.2, 0.2, 0.2).finished());
|
|
gtsam::SharedNoiseModel measModel = noiseModel::Unit::Create(2);
|
|
gtsam::SharedNoiseModel elevationModel = noiseModel::Isotropic::Sigma(1, 3.0);
|
|
|
|
double fov = 60; // degrees
|
|
int imgW = 640; // pixels
|
|
int imgH = 480; // pixels
|
|
gtsam::Cal3_S2::shared_ptr K(new gtsam::Cal3_S2(fov, imgW, imgH));
|
|
|
|
typedef GenericProjectionFactor<Pose3, Point3> ProjectionFactor;
|
|
|
|
double relElevation = 6;
|
|
|
|
Values initValues;
|
|
initValues.insert(xC1,
|
|
Pose3(Rot3(
|
|
-1., 0.0, 1.2246468e-16,
|
|
0.0, 1., 0.0,
|
|
-1.2246468e-16, 0.0, -1.),
|
|
Point3(0.511832102, 8.42819594, 5.76841725)));
|
|
initValues.insert(l32, Point3(0.364081507, 6.89766221, -0.231582751) );
|
|
initValues.insert(l41, Point3(1.61051523, 6.7373052, -0.231582751) );
|
|
|
|
NonlinearFactorGraph factors;
|
|
factors += PriorFactor<Pose3>(xC1,
|
|
Pose3(Rot3(
|
|
-1., 0.0, 1.2246468e-16,
|
|
0.0, 1., 0.0,
|
|
-1.2246468e-16, 0.0, -1),
|
|
Point3(0.511832102, 8.42819594, 5.76841725)), priorModel);
|
|
factors += ProjectionFactor(Point2(333.648615, 98.61535), measModel, xC1, l32, K);
|
|
factors += ProjectionFactor(Point2(218.508, 83.8022039), measModel, xC1, l41, K);
|
|
factors += RangeFactor<Pose3,Point3>(xC1, l32, relElevation, elevationModel);
|
|
factors += RangeFactor<Pose3,Point3>(xC1, l41, relElevation, elevationModel);
|
|
|
|
// Check that sigmas are correct (i.e., unit)
|
|
GaussianFactorGraph lfg = *factors.linearize(initValues);
|
|
|
|
GaussianBayesTree actBT = *lfg.eliminateMultifrontal();
|
|
|
|
// Check that all sigmas in an unconstrained bayes tree are set to one
|
|
for(const GaussianBayesTree::sharedClique& clique: actBT.nodes() | br::map_values) {
|
|
GaussianConditional::shared_ptr conditional = clique->conditional();
|
|
//size_t dim = conditional->rows();
|
|
//EXPECT(assert_equal(gtsam::Vector::Ones(dim), conditional->get_model()->sigmas(), tol));
|
|
EXPECT(!conditional->get_model());
|
|
}
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|