gtsam/gtsam/slam/tests/testAntiFactor.cpp

133 lines
4.9 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 testAntiFactor.cpp
* @brief Unit test for the AntiFactor
* @author Stephen Williams
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/slam/AntiFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearOptimizer.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/geometry/Pose3.h>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST( AntiFactor, NegativeHessian)
{
// The AntiFactor should produce a Hessian Factor with negative matrices
// Create linearization points
Pose3 pose1(Rot3(), Point3(0, 0, 0));
Pose3 pose2(Rot3(), Point3(2, 1, 3));
Pose3 z(Rot3(), Point3(1, 1, 1));
SharedNoiseModel sigma(noiseModel::Unit::Create(6));
// Create a configuration corresponding to the ground truth
Values values;
values.insert(1, pose1);
values.insert(2, pose2);
// Create a "standard" factor
BetweenFactor<Pose3>::shared_ptr originalFactor(new BetweenFactor<Pose3>(1, 2, z, sigma));
// Linearize it into a Jacobian Factor
GaussianFactor::shared_ptr originalJacobian = originalFactor->linearize(values);
// Convert it to a Hessian Factor
HessianFactor::shared_ptr originalHessian = HessianFactor::shared_ptr(new HessianFactor(*originalJacobian));
// Create the AntiFactor version of the original nonlinear factor
AntiFactor::shared_ptr antiFactor(new AntiFactor(originalFactor));
// Linearize the AntiFactor into a Hessian Factor
GaussianFactor::shared_ptr antiGaussian = antiFactor->linearize(values);
HessianFactor::shared_ptr antiHessian = boost::dynamic_pointer_cast<HessianFactor>(antiGaussian);
Matrix expected_information = -originalHessian->information();
Matrix actual_information = antiHessian->information();
EXPECT(assert_equal(expected_information, actual_information));
Vector expected_linear = -originalHessian->linearTerm();
Vector actual_linear = antiHessian->linearTerm();
EXPECT(assert_equal(expected_linear, actual_linear));
double expected_f = -originalHessian->constantTerm();
double actual_f = antiHessian->constantTerm();
EXPECT_DOUBLES_EQUAL(expected_f, actual_f, 1e-5);
}
/* ************************************************************************* */
TEST( AntiFactor, EquivalentBayesNet)
{
// Test the AntiFactor by creating a simple graph and eliminating into a BayesNet
// Then add an additional factor and the corresponding AntiFactor and eliminate
// The resulting BayesNet should be identical to the first
Pose3 pose1(Rot3(), Point3(0, 0, 0));
Pose3 pose2(Rot3(), Point3(2, 1, 3));
Pose3 z(Rot3(), Point3(1, 1, 1));
SharedNoiseModel sigma(noiseModel::Unit::Create(6));
NonlinearFactorGraph graph;
graph.addPrior<>(1, pose1, sigma);
graph.emplace_shared<BetweenFactor<Pose3> >(1, 2, pose1.between(pose2), sigma);
// Create a configuration corresponding to the ground truth
Values values;
values.insert(1, pose1);
values.insert(2, pose2);
// Define an elimination ordering
Ordering ordering = graph.orderingCOLAMD();
// Eliminate into a BayesNet
GaussianFactorGraph lin_graph = *graph.linearize(values);
GaussianBayesNet::shared_ptr expectedBayesNet = lin_graph.eliminateSequential(ordering);
// Back-substitute to find the optimal deltas
VectorValues expectedDeltas = expectedBayesNet->optimize();
// Add an additional factor between Pose1 and Pose2
BetweenFactor<Pose3>::shared_ptr f1(new BetweenFactor<Pose3>(1, 2, z, sigma));
graph.push_back(f1);
// Add the corresponding AntiFactor between Pose1 and Pose2
AntiFactor::shared_ptr f2(new AntiFactor(f1));
graph.push_back(f2);
// Again, Eliminate into a BayesNet
GaussianFactorGraph lin_graph1 = *graph.linearize(values);
GaussianBayesNet::shared_ptr actualBayesNet = lin_graph1.eliminateSequential(ordering);
// Back-substitute to find the optimal deltas
VectorValues actualDeltas = actualBayesNet->optimize();
// Verify the BayesNets are identical
CHECK(assert_equal(*expectedBayesNet, *actualBayesNet, 1e-5));
CHECK(assert_equal(expectedDeltas, actualDeltas, 1e-5));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */