LinearContainerFactor works
Alex Cunningham
parent
cfd81093bd
commit
ec3e89c888
|
|
@ -16,7 +16,7 @@ set(nonlinear_local_libs
|
|||
set(nonlinear_excluded_files
|
||||
# "${CMAKE_CURRENT_SOURCE_DIR}/tests/testTypedDiscreteFactor.cpp" # Example of excluding a test
|
||||
#"" # Add to this list, with full path, to exclude
|
||||
"${CMAKE_CURRENT_SOURCE_DIR}/tests/testLinearContainerFactor.cpp"
|
||||
#"${CMAKE_CURRENT_SOURCE_DIR}/tests/testLinearContainerFactor.cpp"
|
||||
"${CMAKE_CURRENT_SOURCE_DIR}/tests/testWhiteNoiseFactor.cpp"
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -9,11 +9,10 @@
|
|||
#include <gtsam/linear/HessianFactor.h>
|
||||
#include <gtsam/linear/JacobianFactor.h>
|
||||
#include <gtsam/linear/VectorValues.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
|
||||
#include <boost/foreach.hpp>
|
||||
|
||||
#if 0
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -31,30 +30,27 @@ void LinearContainerFactor::initializeLinearizationPoint(const Values& lineariza
|
|||
/* ************************************************************************* */
|
||||
LinearContainerFactor::LinearContainerFactor(const GaussianFactor::shared_ptr& factor,
|
||||
const boost::optional<Values>& linearizationPoint)
|
||||
: factor_(factor), linearizationPoint_(linearizationPoint) {
|
||||
// Extract keys stashed in linear factor
|
||||
BOOST_FOREACH(const Index& idx, factor_->keys())
|
||||
keys_.push_back(idx);
|
||||
: NonlinearFactor(factor->keys()), factor_(factor), linearizationPoint_(linearizationPoint) {
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
LinearContainerFactor::LinearContainerFactor(
|
||||
const JacobianFactor& factor, const Values& linearizationPoint)
|
||||
: factor_(factor.clone()) {
|
||||
: NonlinearFactor(factor.keys()), factor_(factor.clone()) {
|
||||
initializeLinearizationPoint(linearizationPoint);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
LinearContainerFactor::LinearContainerFactor(
|
||||
const HessianFactor& factor, const Values& linearizationPoint)
|
||||
: factor_(factor.clone()) {
|
||||
: NonlinearFactor(factor.keys()), factor_(factor.clone()) {
|
||||
initializeLinearizationPoint(linearizationPoint);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
LinearContainerFactor::LinearContainerFactor(
|
||||
const GaussianFactor::shared_ptr& factor, const Values& linearizationPoint)
|
||||
: factor_(factor->clone()) {
|
||||
: NonlinearFactor(factor->keys()), factor_(factor->clone()) {
|
||||
initializeLinearizationPoint(linearizationPoint);
|
||||
}
|
||||
|
||||
|
|
@ -139,12 +135,12 @@ GaussianFactor::shared_ptr LinearContainerFactor::linearize(const Values& c) con
|
|||
|
||||
/* ************************************************************************* */
|
||||
bool LinearContainerFactor::isJacobian() const {
|
||||
return boost::dynamic_pointer_cast<JacobianFactor>(factor_);
|
||||
return boost::dynamic_pointer_cast<JacobianFactor>(factor_).get();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
bool LinearContainerFactor::isHessian() const {
|
||||
return boost::dynamic_pointer_cast<HessianFactor>(factor_);
|
||||
return boost::dynamic_pointer_cast<HessianFactor>(factor_).get();
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -160,12 +156,13 @@ HessianFactor::shared_ptr LinearContainerFactor::toHessian() const {
|
|||
/* ************************************************************************* */
|
||||
GaussianFactor::shared_ptr LinearContainerFactor::negateToGaussian() const {
|
||||
GaussianFactor::shared_ptr result = factor_->negate();
|
||||
return result;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
NonlinearFactor::shared_ptr LinearContainerFactor::negateToNonlinear() const {
|
||||
GaussianFactor::shared_ptr antifactor = factor_->negate(); // already has keys in place
|
||||
return boost::make_shared<LinearContainerFactor>(antifactor, linearizationPoint_);
|
||||
return NonlinearFactor::shared_ptr(new LinearContainerFactor(antifactor, linearizationPoint_));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -183,4 +180,3 @@ NonlinearFactorGraph LinearContainerFactor::convertLinearGraph(
|
|||
/* ************************************************************************* */
|
||||
} // \namespace gtsam
|
||||
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -38,10 +38,10 @@ protected:
|
|||
|
||||
public:
|
||||
|
||||
/** Primary constructor: store a linear factor and decode the ordering */
|
||||
/** Primary constructor: store a linear factor with optional linearization point */
|
||||
LinearContainerFactor(const JacobianFactor& factor, const Values& linearizationPoint = Values());
|
||||
|
||||
/** Primary constructor: store a linear factor and decode the ordering */
|
||||
/** Primary constructor: store a linear factor with optional linearization point */
|
||||
LinearContainerFactor(const HessianFactor& factor, const Values& linearizationPoint = Values());
|
||||
|
||||
/** Constructor from shared_ptr */
|
||||
|
|
|
|||
|
|
@ -8,6 +8,8 @@
|
|||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/slam/BetweenFactor.h>
|
||||
#include <gtsam/nonlinear/LinearContainerFactor.h>
|
||||
#include <gtsam/linear/VectorValues.h>
|
||||
#include <gtsam/linear/HessianFactor.h>
|
||||
#include <gtsam/geometry/Pose2.h>
|
||||
#include <gtsam/geometry/Point3.h>
|
||||
#include <gtsam/base/TestableAssertions.h>
|
||||
|
|
@ -28,8 +30,6 @@ 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);
|
||||
|
|
@ -39,9 +39,9 @@ TEST( testLinearContainerFactor, generic_jacobian_factor ) {
|
|||
Vector b = Vector_(2, 0.0277052,
|
||||
-0.0533393);
|
||||
|
||||
JacobianFactor expLinFactor(initOrdering[l1], A1, initOrdering[l2], A2, b, diag_model2);
|
||||
JacobianFactor expLinFactor(l1, A1, l2, A2, b, diag_model2);
|
||||
|
||||
LinearContainerFactor actFactor(expLinFactor, initOrdering);
|
||||
LinearContainerFactor actFactor(expLinFactor);
|
||||
EXPECT_LONGS_EQUAL(2, actFactor.size());
|
||||
EXPECT(actFactor.isJacobian());
|
||||
EXPECT(!actFactor.isHessian());
|
||||
|
|
@ -56,22 +56,14 @@ TEST( testLinearContainerFactor, generic_jacobian_factor ) {
|
|||
values.insert(x1, poseA1);
|
||||
values.insert(x2, poseA2);
|
||||
|
||||
// Check reconstruction from same ordering
|
||||
GaussianFactor::shared_ptr actLinearizationA = actFactor.linearize(values, initOrdering);
|
||||
// Check reconstruction
|
||||
GaussianFactor::shared_ptr actLinearizationA = actFactor.linearize(values);
|
||||
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);
|
||||
|
|
@ -81,7 +73,7 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
|
|||
Vector b = Vector_(2, 0.0277052,
|
||||
-0.0533393);
|
||||
|
||||
JacobianFactor expLinFactor(ordering[l1], A1, ordering[l2], A2, b, diag_model2);
|
||||
JacobianFactor expLinFactor(l1, A1, l2, A2, b, diag_model2);
|
||||
|
||||
Values values;
|
||||
values.insert(l1, landmark1);
|
||||
|
|
@ -89,8 +81,8 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
|
|||
values.insert(x1, poseA1);
|
||||
values.insert(x2, poseA2);
|
||||
|
||||
LinearContainerFactor actFactor(expLinFactor, ordering, values);
|
||||
LinearContainerFactor actFactorNolin(expLinFactor, ordering);
|
||||
LinearContainerFactor actFactor(expLinFactor, values);
|
||||
LinearContainerFactor actFactorNolin(expLinFactor);
|
||||
|
||||
EXPECT(assert_equal(actFactor, actFactor, tol));
|
||||
EXPECT(assert_inequal(actFactor, actFactorNolin, tol));
|
||||
|
|
@ -108,19 +100,18 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
|
|||
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);
|
||||
VectorValues delta = values.zeroVectors();
|
||||
delta.at(l1) = delta_l1;
|
||||
delta.at(l2) = delta_l2;
|
||||
Values noisyValues = values.retract(delta);
|
||||
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);
|
||||
EXPECT_DOUBLES_EQUAL(expLinFactor.error(values.zeroVectors()), 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);
|
||||
GaussianFactor::shared_ptr actLinearizationB = actFactor.linearize(noisyValues);
|
||||
Vector bprime = b - A1 * delta_l1 - A2 * delta_l2;
|
||||
JacobianFactor expLinFactor2(newOrdering[l1], A1, newOrdering[l2], A2, bprime, diag_model2);
|
||||
JacobianFactor expLinFactor2(l1, A1, l2, A2, bprime, diag_model2);
|
||||
EXPECT(assert_equal(*expLinFactor2.clone(), *actLinearizationB, tol));
|
||||
}
|
||||
|
||||
|
|
@ -145,8 +136,7 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
|
|||
|
||||
double f = 10.0;
|
||||
|
||||
Ordering initOrdering; initOrdering += x1, x2, l1, l2;
|
||||
HessianFactor initFactor(initOrdering[x1], initOrdering[x2], initOrdering[l1],
|
||||
HessianFactor initFactor(x1, x2, l1,
|
||||
G11, G12, G13, g1, G22, G23, g2, G33, g3, f);
|
||||
|
||||
Values values;
|
||||
|
|
@ -155,18 +145,12 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
|
|||
values.insert(x1, poseA1);
|
||||
values.insert(x2, poseA2);
|
||||
|
||||
LinearContainerFactor actFactor(initFactor, initOrdering);
|
||||
LinearContainerFactor actFactor(initFactor);
|
||||
EXPECT(!actFactor.isJacobian());
|
||||
EXPECT(actFactor.isHessian());
|
||||
|
||||
GaussianFactor::shared_ptr actLinearization1 = actFactor.linearize(values, initOrdering);
|
||||
GaussianFactor::shared_ptr actLinearization1 = actFactor.linearize(values);
|
||||
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));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -196,8 +180,7 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
|
|||
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);
|
||||
HessianFactor initFactor(x1, l1, G11, G12, g1, G22, g2, f);
|
||||
|
||||
Values linearizationPoint, expLinPoints;
|
||||
linearizationPoint.insert(l1, landmark1);
|
||||
|
|
@ -205,7 +188,7 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
|
|||
expLinPoints = linearizationPoint;
|
||||
linearizationPoint.insert(x2, poseA2);
|
||||
|
||||
LinearContainerFactor actFactor(initFactor, ordering, linearizationPoint);
|
||||
LinearContainerFactor actFactor(initFactor, linearizationPoint);
|
||||
EXPECT(!actFactor.isJacobian());
|
||||
EXPECT(actFactor.isHessian());
|
||||
|
||||
|
|
@ -219,16 +202,16 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
|
|||
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;
|
||||
VectorValues delta = linearizationPoint.zeroVectors();
|
||||
delta.at(l1) = delta_l1;
|
||||
delta.at(x1) = delta_x1;
|
||||
delta.at(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);
|
||||
Values noisyValues = linearizationPoint.retract(delta);
|
||||
|
||||
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);
|
||||
EXPECT_DOUBLES_EQUAL(initFactor.error(linearizationPoint.zeroVectors()), actFactor.error(linearizationPoint), tol);
|
||||
|
||||
// Compute updated versions
|
||||
Vector dv = Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0);
|
||||
|
|
@ -239,8 +222,8 @@ TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
|
|||
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 - 2.0 * 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));
|
||||
HessianFactor expNewFactor(x1, l1, G11, G12, g1_prime, G22, g2_prime, f_prime);
|
||||
EXPECT(assert_equal(*expNewFactor.clone(), *actFactor.linearize(noisyValues), tol));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -251,14 +234,10 @@ TEST( testLinearContainerFactor, creation ) {
|
|||
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));
|
||||
l3, eye(2,2), 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;
|
||||
|
|
@ -267,7 +246,7 @@ TEST( testLinearContainerFactor, creation ) {
|
|||
exp_values = full_values;
|
||||
full_values.insert(l1, Point2(3.0, 7.0));
|
||||
|
||||
LinearContainerFactor actual(linear_factor, ordering, full_values);
|
||||
LinearContainerFactor actual(linear_factor, full_values);
|
||||
|
||||
// Verify the keys
|
||||
std::vector<gtsam::Key> expKeys;
|
||||
|
|
@ -284,9 +263,6 @@ TEST( testLinearContainerFactor, jacobian_relinearize )
|
|||
// Create a Between Factor from a Point3. This is actually a linear factor.
|
||||
gtsam::Key key1(1);
|
||||
gtsam::Key key2(2);
|
||||
gtsam::Ordering ordering;
|
||||
ordering.push_back(key1);
|
||||
ordering.push_back(key2);
|
||||
gtsam::Values linpoint1;
|
||||
linpoint1.insert(key1, gtsam::Point3(-22.4, +8.5, +2.4));
|
||||
linpoint1.insert(key2, gtsam::Point3(-21.0, +5.0, +21.0));
|
||||
|
|
@ -296,8 +272,8 @@ TEST( testLinearContainerFactor, jacobian_relinearize )
|
|||
gtsam::BetweenFactor<gtsam::Point3> betweenFactor(key1, key2, measured, model);
|
||||
|
||||
// Create a jacobian container factor at linpoint 1
|
||||
gtsam::JacobianFactor::shared_ptr jacobian(new gtsam::JacobianFactor(*betweenFactor.linearize(linpoint1, ordering)));
|
||||
gtsam::LinearContainerFactor jacobianContainer(jacobian, ordering, linpoint1);
|
||||
gtsam::JacobianFactor::shared_ptr jacobian(new gtsam::JacobianFactor(*betweenFactor.linearize(linpoint1)));
|
||||
gtsam::LinearContainerFactor jacobianContainer(jacobian, linpoint1);
|
||||
|
||||
// Create a second linearization point
|
||||
gtsam::Values linpoint2;
|
||||
|
|
@ -310,8 +286,8 @@ TEST( testLinearContainerFactor, jacobian_relinearize )
|
|||
EXPECT_DOUBLES_EQUAL(expected_error, actual_error, 1e-9 );
|
||||
|
||||
// Re-linearize around the new point and check the factors
|
||||
gtsam::GaussianFactor::shared_ptr expected_factor = betweenFactor.linearize(linpoint2, ordering);
|
||||
gtsam::GaussianFactor::shared_ptr actual_factor = jacobianContainer.linearize(linpoint2, ordering);
|
||||
gtsam::GaussianFactor::shared_ptr expected_factor = betweenFactor.linearize(linpoint2);
|
||||
gtsam::GaussianFactor::shared_ptr actual_factor = jacobianContainer.linearize(linpoint2);
|
||||
CHECK(gtsam::assert_equal(*expected_factor, *actual_factor));
|
||||
}
|
||||
|
||||
|
|
@ -321,9 +297,6 @@ TEST( testLinearContainerFactor, hessian_relinearize )
|
|||
// Create a Between Factor from a Point3. This is actually a linear factor.
|
||||
gtsam::Key key1(1);
|
||||
gtsam::Key key2(2);
|
||||
gtsam::Ordering ordering;
|
||||
ordering.push_back(key1);
|
||||
ordering.push_back(key2);
|
||||
gtsam::Values linpoint1;
|
||||
linpoint1.insert(key1, gtsam::Point3(-22.4, +8.5, +2.4));
|
||||
linpoint1.insert(key2, gtsam::Point3(-21.0, +5.0, +21.0));
|
||||
|
|
@ -333,8 +306,8 @@ TEST( testLinearContainerFactor, hessian_relinearize )
|
|||
gtsam::BetweenFactor<gtsam::Point3> betweenFactor(key1, key2, measured, model);
|
||||
|
||||
// Create a hessian container factor at linpoint 1
|
||||
gtsam::HessianFactor::shared_ptr hessian(new gtsam::HessianFactor(*betweenFactor.linearize(linpoint1, ordering)));
|
||||
gtsam::LinearContainerFactor hessianContainer(hessian, ordering, linpoint1);
|
||||
gtsam::HessianFactor::shared_ptr hessian(new gtsam::HessianFactor(*betweenFactor.linearize(linpoint1)));
|
||||
gtsam::LinearContainerFactor hessianContainer(hessian, linpoint1);
|
||||
|
||||
// Create a second linearization point
|
||||
gtsam::Values linpoint2;
|
||||
|
|
@ -347,8 +320,8 @@ TEST( testLinearContainerFactor, hessian_relinearize )
|
|||
EXPECT_DOUBLES_EQUAL(expected_error, actual_error, 1e-9 );
|
||||
|
||||
// Re-linearize around the new point and check the factors
|
||||
gtsam::GaussianFactor::shared_ptr expected_factor = gtsam::HessianFactor::shared_ptr(new gtsam::HessianFactor(*betweenFactor.linearize(linpoint2, ordering)));
|
||||
gtsam::GaussianFactor::shared_ptr actual_factor = hessianContainer.linearize(linpoint2, ordering);
|
||||
gtsam::GaussianFactor::shared_ptr expected_factor = gtsam::HessianFactor::shared_ptr(new gtsam::HessianFactor(*betweenFactor.linearize(linpoint2)));
|
||||
gtsam::GaussianFactor::shared_ptr actual_factor = hessianContainer.linearize(linpoint2);
|
||||
CHECK(gtsam::assert_equal(*expected_factor, *actual_factor));
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue