286 lines
9.9 KiB
C++
286 lines
9.9 KiB
C++
/**
|
|
* @file testLinearContainerFactor.cpp
|
|
*
|
|
* @date Jul 6, 2012
|
|
* @author Alex Cunningham
|
|
*/
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
|
|
#include <boost/assign/std/vector.hpp>
|
|
|
|
#include <gtsam_unstable/nonlinear/LinearContainerFactor.h>
|
|
|
|
#include <gtsam/base/TestableAssertions.h>
|
|
#include <gtsam/geometry/Pose2.h>
|
|
|
|
using namespace std;
|
|
using namespace boost::assign;
|
|
using namespace gtsam;
|
|
|
|
const gtsam::noiseModel::Diagonal::shared_ptr diag_model2 = noiseModel::Diagonal::Sigmas(Vector_(2, 1.0, 1.0));
|
|
const double tol = 1e-5;
|
|
|
|
gtsam::Key l1 = 101, l2 = 102, x1 = 1, x2 = 2;
|
|
|
|
Point2 landmark1(5.0, 1.5), landmark2(7.0, 1.5);
|
|
Pose2 poseA1(0.0, 0.0, 0.0), poseA2(2.0, 0.0, 0.0);
|
|
|
|
/* ************************************************************************* */
|
|
TEST( testLinearContainerFactor, generic_jacobian_factor ) {
|
|
|
|
Ordering initOrdering; initOrdering += x1, x2, l1, l2;
|
|
|
|
Matrix A1 = Matrix_(2,2,
|
|
2.74222, -0.0067457,
|
|
0.0, 2.63624);
|
|
Matrix A2 = Matrix_(2,2,
|
|
-0.0455167, -0.0443573,
|
|
-0.0222154, -0.102489);
|
|
Vector b = Vector_(2, 0.0277052,
|
|
-0.0533393);
|
|
|
|
JacobianFactor expLinFactor(initOrdering[l1], A1, initOrdering[l2], A2, b, diag_model2);
|
|
|
|
LinearContainerFactor actFactor(expLinFactor, initOrdering);
|
|
EXPECT_LONGS_EQUAL(2, actFactor.size());
|
|
EXPECT(actFactor.isJacobian());
|
|
EXPECT(!actFactor.isHessian());
|
|
|
|
// check keys
|
|
std::vector<gtsam::Key> expKeys; expKeys += l1, l2;
|
|
EXPECT(assert_container_equality(expKeys, actFactor.keys()));
|
|
|
|
Values values;
|
|
values.insert(l1, landmark1);
|
|
values.insert(l2, landmark2);
|
|
values.insert(x1, poseA1);
|
|
values.insert(x2, poseA2);
|
|
|
|
// Check reconstruction from same ordering
|
|
GaussianFactor::shared_ptr actLinearizationA = actFactor.linearize(values, initOrdering);
|
|
EXPECT(assert_equal(*expLinFactor.clone(), *actLinearizationA, tol));
|
|
|
|
// Check reconstruction from new ordering
|
|
Ordering newOrdering; newOrdering += x1, l1, x2, l2;
|
|
GaussianFactor::shared_ptr actLinearizationB = actFactor.linearize(values, newOrdering);
|
|
JacobianFactor expLinFactor2(newOrdering[l1], A1, newOrdering[l2], A2, b, diag_model2);
|
|
EXPECT(assert_equal(*expLinFactor2.clone(), *actLinearizationB, tol));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
|
|
|
|
Ordering ordering; ordering += x1, x2, l1, l2;
|
|
|
|
Matrix A1 = Matrix_(2,2,
|
|
2.74222, -0.0067457,
|
|
0.0, 2.63624);
|
|
Matrix A2 = Matrix_(2,2,
|
|
-0.0455167, -0.0443573,
|
|
-0.0222154, -0.102489);
|
|
Vector b = Vector_(2, 0.0277052,
|
|
-0.0533393);
|
|
|
|
JacobianFactor expLinFactor(ordering[l1], A1, ordering[l2], A2, b, diag_model2);
|
|
|
|
Values values;
|
|
values.insert(l1, landmark1);
|
|
values.insert(l2, landmark2);
|
|
values.insert(x1, poseA1);
|
|
values.insert(x2, poseA2);
|
|
|
|
LinearContainerFactor actFactor(expLinFactor, ordering, values);
|
|
LinearContainerFactor actFactorNolin(expLinFactor, ordering);
|
|
|
|
EXPECT(assert_equal(actFactor, actFactor, tol));
|
|
EXPECT(assert_inequal(actFactor, actFactorNolin, tol));
|
|
EXPECT(assert_inequal(actFactorNolin, actFactor, tol));
|
|
|
|
// Check contents
|
|
Values expLinPoint;
|
|
expLinPoint.insert(l1, landmark1);
|
|
expLinPoint.insert(l2, landmark2);
|
|
CHECK(actFactor.linearizationPoint());
|
|
EXPECT(actFactor.hasLinearizationPoint());
|
|
EXPECT(assert_equal(expLinPoint, *actFactor.linearizationPoint()));
|
|
|
|
// Check error evaluation
|
|
Vector delta_l1 = Vector_(2, 1.0, 2.0);
|
|
Vector delta_l2 = Vector_(2, 3.0, 4.0);
|
|
|
|
VectorValues delta = values.zeroVectors(ordering);
|
|
delta.at(ordering[l1]) = delta_l1;
|
|
delta.at(ordering[l2]) = delta_l2;
|
|
Values noisyValues = values.retract(delta, ordering);
|
|
double expError = expLinFactor.error(delta);
|
|
EXPECT_DOUBLES_EQUAL(expError, actFactor.error(noisyValues), tol);
|
|
EXPECT_DOUBLES_EQUAL(expLinFactor.error(values.zeroVectors(ordering)), actFactor.error(values), tol);
|
|
|
|
// Check linearization with corrections for updated linearization point
|
|
Ordering newOrdering; newOrdering += x1, l1, x2, l2;
|
|
GaussianFactor::shared_ptr actLinearizationB = actFactor.linearize(noisyValues, newOrdering);
|
|
Vector bprime = b + A1 * delta_l1 + A2 * delta_l2;
|
|
JacobianFactor expLinFactor2(newOrdering[l1], A1, newOrdering[l2], A2, bprime, diag_model2);
|
|
EXPECT(assert_equal(*expLinFactor2.clone(), *actLinearizationB, tol));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( testLinearContainerFactor, generic_hessian_factor ) {
|
|
Matrix G11 = Matrix_(1,1, 1.0);
|
|
Matrix G12 = Matrix_(1,2, 2.0, 4.0);
|
|
Matrix G13 = Matrix_(1,3, 3.0, 6.0, 9.0);
|
|
|
|
Matrix G22 = Matrix_(2,2, 3.0, 5.0,
|
|
0.0, 6.0);
|
|
Matrix G23 = Matrix_(2,3, 4.0, 6.0, 8.0,
|
|
1.0, 2.0, 4.0);
|
|
|
|
Matrix G33 = Matrix_(3,3, 1.0, 2.0, 3.0,
|
|
0.0, 5.0, 6.0,
|
|
0.0, 0.0, 9.0);
|
|
|
|
Vector g1 = Vector_(1, -7.0);
|
|
Vector g2 = Vector_(2, -8.0, -9.0);
|
|
Vector g3 = Vector_(3, 1.0, 2.0, 3.0);
|
|
|
|
double f = 10.0;
|
|
|
|
Ordering initOrdering; initOrdering += x1, x2, l1, l2;
|
|
HessianFactor initFactor(initOrdering[x1], initOrdering[x2], initOrdering[l1],
|
|
G11, G12, G13, g1, G22, G23, g2, G33, g3, f);
|
|
|
|
Values values;
|
|
values.insert(l1, landmark1);
|
|
values.insert(l2, landmark2);
|
|
values.insert(x1, poseA1);
|
|
values.insert(x2, poseA2);
|
|
|
|
LinearContainerFactor actFactor(initFactor, initOrdering);
|
|
EXPECT(!actFactor.isJacobian());
|
|
EXPECT(actFactor.isHessian());
|
|
|
|
GaussianFactor::shared_ptr actLinearization1 = actFactor.linearize(values, initOrdering);
|
|
EXPECT(assert_equal(*initFactor.clone(), *actLinearization1, tol));
|
|
|
|
Ordering newOrdering; newOrdering += l1, x1, x2, l2;
|
|
HessianFactor expLinFactor(newOrdering[x1], newOrdering[x2], newOrdering[l1],
|
|
G11, G12, G13, g1, G22, G23, g2, G33, g3, f);
|
|
GaussianFactor::shared_ptr actLinearization2 = actFactor.linearize(values, newOrdering);
|
|
EXPECT(assert_equal(*expLinFactor.clone(), *actLinearization2, tol));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
|
|
// 2 variable example, one pose, one landmark (planar)
|
|
// Initial ordering: x1, l1
|
|
|
|
Matrix G11 = Matrix_(3,3,
|
|
1.0, 2.0, 3.0,
|
|
0.0, 5.0, 6.0,
|
|
0.0, 0.0, 9.0);
|
|
Matrix G12 = Matrix_(3,2,
|
|
1.0, 2.0,
|
|
3.0, 5.0,
|
|
4.0, 6.0);
|
|
Vector g1 = Vector_(3, 1.0, 2.0, 3.0);
|
|
|
|
Matrix G22 = Matrix_(2,2,
|
|
0.5, 0.2,
|
|
0.0, 0.6);
|
|
|
|
Vector g2 = Vector_(2, -8.0, -9.0);
|
|
|
|
double f = 10.0;
|
|
|
|
// Construct full matrices
|
|
Matrix G(5,5);
|
|
G << G11, G12, Matrix::Zero(2,3), G22;
|
|
|
|
Ordering ordering; ordering += x1, x2, l1;
|
|
HessianFactor initFactor(ordering[x1], ordering[l1], G11, G12, g1, G22, g2, f);
|
|
|
|
Values linearizationPoint, expLinPoints;
|
|
linearizationPoint.insert(l1, landmark1);
|
|
linearizationPoint.insert(x1, poseA1);
|
|
expLinPoints = linearizationPoint;
|
|
linearizationPoint.insert(x2, poseA2);
|
|
|
|
LinearContainerFactor actFactor(initFactor, ordering, linearizationPoint);
|
|
EXPECT(!actFactor.isJacobian());
|
|
EXPECT(actFactor.isHessian());
|
|
|
|
EXPECT(actFactor.hasLinearizationPoint());
|
|
Values actLinPoint = *actFactor.linearizationPoint();
|
|
EXPECT(assert_equal(expLinPoints, actLinPoint));
|
|
|
|
// Create delta
|
|
Vector delta_l1 = Vector_(2, 1.0, 2.0);
|
|
Vector delta_x1 = Vector_(3, 3.0, 4.0, 0.5);
|
|
Vector delta_x2 = Vector_(3, 6.0, 7.0, 0.3);
|
|
|
|
// Check error calculation
|
|
VectorValues delta = linearizationPoint.zeroVectors(ordering);
|
|
delta.at(ordering[l1]) = delta_l1;
|
|
delta.at(ordering[x1]) = delta_x1;
|
|
delta.at(ordering[x2]) = delta_x2;
|
|
EXPECT(assert_equal(Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0), delta.vector(initFactor.keys())));
|
|
Values noisyValues = linearizationPoint.retract(delta, ordering);
|
|
|
|
double expError = initFactor.error(delta);
|
|
EXPECT_DOUBLES_EQUAL(expError, actFactor.error(noisyValues), tol);
|
|
EXPECT_DOUBLES_EQUAL(initFactor.error(linearizationPoint.zeroVectors(ordering)), actFactor.error(linearizationPoint), tol);
|
|
|
|
// Compute updated versions
|
|
Vector dv = Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0);
|
|
Vector g(5); g << g1, g2;
|
|
Vector g_prime = G.selfadjointView<Eigen::Upper>() * dv + g;
|
|
|
|
// Check linearization with corrections for updated linearization point
|
|
Vector g1_prime = g_prime.head(3);
|
|
Vector g2_prime = g_prime.tail(2);
|
|
double f_prime = f + dv.transpose() * G.selfadjointView<Eigen::Upper>() * dv + dv.transpose() * g;
|
|
HessianFactor expNewFactor(ordering[x1], ordering[l1], G11, G12, g1_prime, G22, g2_prime, f_prime);
|
|
EXPECT(assert_equal(*expNewFactor.clone(), *actFactor.linearize(noisyValues, ordering), tol));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( testLinearContainerFactor, creation ) {
|
|
// Create a set of local keys (No robot label)
|
|
Key l1 = 11, l2 = 12,
|
|
l3 = 13, l4 = 14,
|
|
l5 = 15, l6 = 16,
|
|
l7 = 17, l8 = 18;
|
|
|
|
// creating an ordering to decode the linearized factor
|
|
Ordering ordering;
|
|
ordering += l1,l2,l3,l4,l5,l6,l7,l8;
|
|
|
|
// create a linear factor
|
|
SharedDiagonal model = noiseModel::Unit::Create(2);
|
|
JacobianFactor::shared_ptr linear_factor(new JacobianFactor(
|
|
ordering[l3], eye(2,2), ordering[l5], 2.0 * eye(2,2), zero(2), model));
|
|
|
|
// create a set of values - build with full set of values
|
|
gtsam::Values full_values, exp_values;
|
|
full_values.insert(l3, Point2(1.0, 2.0));
|
|
full_values.insert(l5, Point2(4.0, 3.0));
|
|
exp_values = full_values;
|
|
full_values.insert(l1, Point2(3.0, 7.0));
|
|
|
|
LinearContainerFactor actual(linear_factor, ordering, full_values);
|
|
|
|
// Verify the keys
|
|
std::vector<gtsam::Key> expKeys;
|
|
expKeys += l3, l5;
|
|
EXPECT(assert_container_equality(expKeys, actual.keys()));
|
|
|
|
// Verify subset of linearization points
|
|
EXPECT(assert_equal(exp_values, actual.linearizationPoint(), tol));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
|
|
/* ************************************************************************* */
|
|
|