391 lines
14 KiB
C++
391 lines
14 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 testNonlinearFactor.cpp
|
|
* @brief Unit tests for Non-Linear Factor,
|
|
* create a non linear factor graph and a values structure for it and
|
|
* calculate the error for the factor.
|
|
* @author Christian Potthast
|
|
**/
|
|
|
|
/*STL/C++*/
|
|
#include <iostream>
|
|
|
|
#include <CppUnitLite/TestHarness.h>
|
|
|
|
// TODO: DANGEROUS, create shared pointers
|
|
#define GTSAM_MAGIC_GAUSSIAN 2
|
|
|
|
#include <gtsam/base/Testable.h>
|
|
#include <gtsam/base/Matrix.h>
|
|
#include <gtsam/base/LieVector.h>
|
|
#include <gtsam/slam/smallExample.h>
|
|
#include <gtsam/slam/simulated2D.h>
|
|
#include <gtsam/linear/GaussianFactor.h>
|
|
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
|
|
|
|
using namespace std;
|
|
using namespace gtsam;
|
|
using namespace example;
|
|
|
|
using simulated2D::PoseKey;
|
|
using simulated2D::PointKey;
|
|
|
|
typedef boost::shared_ptr<NonlinearFactor > shared_nlf;
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, equals )
|
|
{
|
|
SharedNoiseModel sigma(noiseModel::Isotropic::Sigma(2,1.0));
|
|
|
|
// create two nonlinear2 factors
|
|
Point2 z3(0.,-1.);
|
|
simulated2D::Measurement f0(z3, sigma, PoseKey(1),PointKey(1));
|
|
|
|
// measurement between x2 and l1
|
|
Point2 z4(-1.5, -1.);
|
|
simulated2D::Measurement f1(z4, sigma, PoseKey(2),PointKey(1));
|
|
|
|
CHECK(assert_equal(f0,f0));
|
|
CHECK(f0.equals(f0));
|
|
CHECK(!f0.equals(f1));
|
|
CHECK(!f1.equals(f0));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, equals2 )
|
|
{
|
|
// create a non linear factor graph
|
|
Graph fg = createNonlinearFactorGraph();
|
|
|
|
// get two factors
|
|
Graph::sharedFactor f0 = fg[0], f1 = fg[1];
|
|
|
|
CHECK(f0->equals(*f0));
|
|
// SL-FIX CHECK(!f0->equals(*f1));
|
|
// SL-FIX CHECK(!f1->equals(*f0));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, NonlinearFactor )
|
|
{
|
|
// create a non linear factor graph
|
|
Graph fg = createNonlinearFactorGraph();
|
|
|
|
// create a values structure for the non linear factor graph
|
|
Values cfg = createNoisyValues();
|
|
|
|
// get the factor "f1" from the factor graph
|
|
Graph::sharedFactor factor = fg[0];
|
|
|
|
// calculate the error_vector from the factor "f1"
|
|
// error_vector = [0.1 0.1]
|
|
Vector actual_e = boost::dynamic_pointer_cast<NoiseModelFactor>(factor)->unwhitenedError(cfg);
|
|
CHECK(assert_equal(0.1*ones(2),actual_e));
|
|
|
|
// error = 0.5 * [1 1] * [1;1] = 1
|
|
double expected = 1.0;
|
|
|
|
// calculate the error from the factor "f1"
|
|
double actual = factor->error(cfg);
|
|
DOUBLES_EQUAL(expected,actual,0.00000001);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_f1 )
|
|
{
|
|
Values c = createNoisyValues();
|
|
|
|
// Grab a non-linear factor
|
|
Graph nfg = createNonlinearFactorGraph();
|
|
Graph::sharedFactor nlf = nfg[0];
|
|
|
|
// We linearize at noisy config from SmallExample
|
|
GaussianFactor::shared_ptr actual = nlf->linearize(c, *c.orderingArbitrary());
|
|
|
|
GaussianFactorGraph lfg = createGaussianFactorGraph(*c.orderingArbitrary());
|
|
GaussianFactor::shared_ptr expected = lfg[0];
|
|
|
|
CHECK(assert_equal(*expected,*actual));
|
|
|
|
// The error |A*dx-b| approximates (h(x0+dx)-z) = -error_vector
|
|
// Hence i.e., b = approximates z-h(x0) = error_vector(x0)
|
|
//CHECK(assert_equal(nlf->error_vector(c),actual->get_b()));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_f2 )
|
|
{
|
|
Values c = createNoisyValues();
|
|
|
|
// Grab a non-linear factor
|
|
Graph nfg = createNonlinearFactorGraph();
|
|
Graph::sharedFactor nlf = nfg[1];
|
|
|
|
// We linearize at noisy config from SmallExample
|
|
GaussianFactor::shared_ptr actual = nlf->linearize(c, *c.orderingArbitrary());
|
|
|
|
GaussianFactorGraph lfg = createGaussianFactorGraph(*c.orderingArbitrary());
|
|
GaussianFactor::shared_ptr expected = lfg[1];
|
|
|
|
CHECK(assert_equal(*expected,*actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_f3 )
|
|
{
|
|
// Grab a non-linear factor
|
|
Graph nfg = createNonlinearFactorGraph();
|
|
Graph::sharedFactor nlf = nfg[2];
|
|
|
|
// We linearize at noisy config from SmallExample
|
|
Values c = createNoisyValues();
|
|
GaussianFactor::shared_ptr actual = nlf->linearize(c, *c.orderingArbitrary());
|
|
|
|
GaussianFactorGraph lfg = createGaussianFactorGraph(*c.orderingArbitrary());
|
|
GaussianFactor::shared_ptr expected = lfg[2];
|
|
|
|
CHECK(assert_equal(*expected,*actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_f4 )
|
|
{
|
|
// Grab a non-linear factor
|
|
Graph nfg = createNonlinearFactorGraph();
|
|
Graph::sharedFactor nlf = nfg[3];
|
|
|
|
// We linearize at noisy config from SmallExample
|
|
Values c = createNoisyValues();
|
|
GaussianFactor::shared_ptr actual = nlf->linearize(c, *c.orderingArbitrary());
|
|
|
|
GaussianFactorGraph lfg = createGaussianFactorGraph(*c.orderingArbitrary());
|
|
GaussianFactor::shared_ptr expected = lfg[3];
|
|
|
|
CHECK(assert_equal(*expected,*actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, size )
|
|
{
|
|
// create a non linear factor graph
|
|
Graph fg = createNonlinearFactorGraph();
|
|
|
|
// create a values structure for the non linear factor graph
|
|
Values cfg = createNoisyValues();
|
|
|
|
// get some factors from the graph
|
|
Graph::sharedFactor factor1 = fg[0], factor2 = fg[1],
|
|
factor3 = fg[2];
|
|
|
|
CHECK(factor1->size() == 1);
|
|
CHECK(factor2->size() == 2);
|
|
CHECK(factor3->size() == 2);
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_constraint1 )
|
|
{
|
|
Vector sigmas = Vector_(2, 0.2, 0.0);
|
|
SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(sigmas);
|
|
|
|
Point2 mu(1., -1.);
|
|
Graph::sharedFactor f0(new simulated2D::Prior(mu, constraint, PoseKey(1)));
|
|
|
|
Values config;
|
|
config.insert(simulated2D::PoseKey(1), Point2(1.0, 2.0));
|
|
GaussianFactor::shared_ptr actual = f0->linearize(config, *config.orderingArbitrary());
|
|
|
|
// create expected
|
|
Ordering ord(*config.orderingArbitrary());
|
|
Vector b = Vector_(2, 0., -3.);
|
|
JacobianFactor expected(ord[PoseKey(1)], Matrix_(2,2, 5.0, 0.0, 0.0, 1.0), b, constraint);
|
|
CHECK(assert_equal((const GaussianFactor&)expected, *actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST( NonlinearFactor, linearize_constraint2 )
|
|
{
|
|
Vector sigmas = Vector_(2, 0.2, 0.0);
|
|
SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(sigmas);
|
|
|
|
Point2 z3(1.,-1.);
|
|
simulated2D::Measurement f0(z3, constraint, PoseKey(1),PointKey(1));
|
|
|
|
Values config;
|
|
config.insert(simulated2D::PoseKey(1), Point2(1.0, 2.0));
|
|
config.insert(simulated2D::PointKey(1), Point2(5.0, 4.0));
|
|
GaussianFactor::shared_ptr actual = f0.linearize(config, *config.orderingArbitrary());
|
|
|
|
// create expected
|
|
Ordering ord(*config.orderingArbitrary());
|
|
Matrix A = Matrix_(2,2, 5.0, 0.0, 0.0, 1.0);
|
|
Vector b = Vector_(2, -15., -3.);
|
|
JacobianFactor expected(ord[PoseKey(1)], -1*A, ord[PointKey(1)], A, b, constraint);
|
|
CHECK(assert_equal((const GaussianFactor&)expected, *actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
class TestFactor4 : public NoiseModelFactor4<LieVector, LieVector, LieVector, LieVector> {
|
|
public:
|
|
typedef NoiseModelFactor4<LieVector, LieVector, LieVector, LieVector> Base;
|
|
TestFactor4() : Base(sharedSigmas(Vector_(1, 2.0)), PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4)) {}
|
|
|
|
virtual Vector
|
|
evaluateError(const LieVector& x1, const LieVector& x2, const LieVector& x3, const LieVector& x4,
|
|
boost::optional<Matrix&> H1 = boost::none,
|
|
boost::optional<Matrix&> H2 = boost::none,
|
|
boost::optional<Matrix&> H3 = boost::none,
|
|
boost::optional<Matrix&> H4 = boost::none) const {
|
|
if(H1) {
|
|
*H1 = Matrix_(1,1, 1.0);
|
|
*H2 = Matrix_(1,1, 2.0);
|
|
*H3 = Matrix_(1,1, 3.0);
|
|
*H4 = Matrix_(1,1, 4.0);
|
|
}
|
|
return (Vector(1) << x1 + x2 + x3 + x4).finished();
|
|
}
|
|
};
|
|
|
|
/* ************************************ */
|
|
TEST(NonlinearFactor, NoiseModelFactor4) {
|
|
TestFactor4 tf;
|
|
Values tv;
|
|
tv.insert(PoseKey(1), LieVector(1, 1.0));
|
|
tv.insert(PoseKey(2), LieVector(1, 2.0));
|
|
tv.insert(PoseKey(3), LieVector(1, 3.0));
|
|
tv.insert(PoseKey(4), LieVector(1, 4.0));
|
|
EXPECT(assert_equal(Vector_(1, 10.0), tf.unwhitenedError(tv)));
|
|
DOUBLES_EQUAL(25.0/2.0, tf.error(tv), 1e-9);
|
|
Ordering ordering; ordering += PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4);
|
|
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv, ordering)));
|
|
LONGS_EQUAL(jf.keys()[0], 0);
|
|
LONGS_EQUAL(jf.keys()[1], 1);
|
|
LONGS_EQUAL(jf.keys()[2], 2);
|
|
LONGS_EQUAL(jf.keys()[3], 3);
|
|
EXPECT(assert_equal(Matrix_(1,1, 0.5), jf.getA(jf.begin())));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.0), jf.getA(jf.begin()+1)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.5), jf.getA(jf.begin()+2)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 2.0), jf.getA(jf.begin()+3)));
|
|
EXPECT(assert_equal(Vector_(1, -5.0), jf.getb()));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
class TestFactor5 : public NoiseModelFactor5<LieVector, LieVector, LieVector, LieVector, LieVector> {
|
|
public:
|
|
typedef NoiseModelFactor5<LieVector, LieVector, LieVector, LieVector, LieVector> Base;
|
|
TestFactor5() : Base(sharedSigmas(Vector_(1, 2.0)), PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4), PoseKey(5)) {}
|
|
|
|
virtual Vector
|
|
evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5,
|
|
boost::optional<Matrix&> H1 = boost::none,
|
|
boost::optional<Matrix&> H2 = boost::none,
|
|
boost::optional<Matrix&> H3 = boost::none,
|
|
boost::optional<Matrix&> H4 = boost::none,
|
|
boost::optional<Matrix&> H5 = boost::none) const {
|
|
if(H1) {
|
|
*H1 = Matrix_(1,1, 1.0);
|
|
*H2 = Matrix_(1,1, 2.0);
|
|
*H3 = Matrix_(1,1, 3.0);
|
|
*H4 = Matrix_(1,1, 4.0);
|
|
*H5 = Matrix_(1,1, 5.0);
|
|
}
|
|
return (Vector(1) << x1 + x2 + x3 + x4 + x5).finished();
|
|
}
|
|
};
|
|
|
|
/* ************************************ */
|
|
TEST(NonlinearFactor, NoiseModelFactor5) {
|
|
TestFactor5 tf;
|
|
Values tv;
|
|
tv.insert(PoseKey(1), LieVector(1, 1.0));
|
|
tv.insert(PoseKey(2), LieVector(1, 2.0));
|
|
tv.insert(PoseKey(3), LieVector(1, 3.0));
|
|
tv.insert(PoseKey(4), LieVector(1, 4.0));
|
|
tv.insert(PoseKey(5), LieVector(1, 5.0));
|
|
EXPECT(assert_equal(Vector_(1, 15.0), tf.unwhitenedError(tv)));
|
|
DOUBLES_EQUAL(56.25/2.0, tf.error(tv), 1e-9);
|
|
Ordering ordering; ordering += PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4), PoseKey(5);
|
|
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv, ordering)));
|
|
LONGS_EQUAL(jf.keys()[0], 0);
|
|
LONGS_EQUAL(jf.keys()[1], 1);
|
|
LONGS_EQUAL(jf.keys()[2], 2);
|
|
LONGS_EQUAL(jf.keys()[3], 3);
|
|
LONGS_EQUAL(jf.keys()[4], 4);
|
|
EXPECT(assert_equal(Matrix_(1,1, 0.5), jf.getA(jf.begin())));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.0), jf.getA(jf.begin()+1)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.5), jf.getA(jf.begin()+2)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 2.0), jf.getA(jf.begin()+3)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 2.5), jf.getA(jf.begin()+4)));
|
|
EXPECT(assert_equal(Vector_(1, -7.5), jf.getb()));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
class TestFactor6 : public NoiseModelFactor6<LieVector, LieVector, LieVector, LieVector, LieVector, LieVector> {
|
|
public:
|
|
typedef NoiseModelFactor6<LieVector, LieVector, LieVector, LieVector, LieVector, LieVector> Base;
|
|
TestFactor6() : Base(sharedSigmas(Vector_(1, 2.0)), PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4), PoseKey(5), PoseKey(6)) {}
|
|
|
|
virtual Vector
|
|
evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5, const X6& x6,
|
|
boost::optional<Matrix&> H1 = boost::none,
|
|
boost::optional<Matrix&> H2 = boost::none,
|
|
boost::optional<Matrix&> H3 = boost::none,
|
|
boost::optional<Matrix&> H4 = boost::none,
|
|
boost::optional<Matrix&> H5 = boost::none,
|
|
boost::optional<Matrix&> H6 = boost::none) const {
|
|
if(H1) {
|
|
*H1 = Matrix_(1,1, 1.0);
|
|
*H2 = Matrix_(1,1, 2.0);
|
|
*H3 = Matrix_(1,1, 3.0);
|
|
*H4 = Matrix_(1,1, 4.0);
|
|
*H5 = Matrix_(1,1, 5.0);
|
|
*H6 = Matrix_(1,1, 6.0);
|
|
}
|
|
return (Vector(1) << x1 + x2 + x3 + x4 + x5 + x6).finished();
|
|
}
|
|
};
|
|
|
|
/* ************************************ */
|
|
TEST(NonlinearFactor, NoiseModelFactor6) {
|
|
TestFactor6 tf;
|
|
Values tv;
|
|
tv.insert(PoseKey(1), LieVector(1, 1.0));
|
|
tv.insert(PoseKey(2), LieVector(1, 2.0));
|
|
tv.insert(PoseKey(3), LieVector(1, 3.0));
|
|
tv.insert(PoseKey(4), LieVector(1, 4.0));
|
|
tv.insert(PoseKey(5), LieVector(1, 5.0));
|
|
tv.insert(PoseKey(6), LieVector(1, 6.0));
|
|
EXPECT(assert_equal(Vector_(1, 21.0), tf.unwhitenedError(tv)));
|
|
DOUBLES_EQUAL(110.25/2.0, tf.error(tv), 1e-9);
|
|
Ordering ordering; ordering += PoseKey(1), PoseKey(2), PoseKey(3), PoseKey(4), PoseKey(5), PoseKey(6);
|
|
JacobianFactor jf(*boost::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv, ordering)));
|
|
LONGS_EQUAL(jf.keys()[0], 0);
|
|
LONGS_EQUAL(jf.keys()[1], 1);
|
|
LONGS_EQUAL(jf.keys()[2], 2);
|
|
LONGS_EQUAL(jf.keys()[3], 3);
|
|
LONGS_EQUAL(jf.keys()[4], 4);
|
|
LONGS_EQUAL(jf.keys()[5], 5);
|
|
EXPECT(assert_equal(Matrix_(1,1, 0.5), jf.getA(jf.begin())));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.0), jf.getA(jf.begin()+1)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 1.5), jf.getA(jf.begin()+2)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 2.0), jf.getA(jf.begin()+3)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 2.5), jf.getA(jf.begin()+4)));
|
|
EXPECT(assert_equal(Matrix_(1,1, 3.0), jf.getA(jf.begin()+5)));
|
|
EXPECT(assert_equal(Vector_(1, -10.5), jf.getb()));
|
|
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|