12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
gtsam/tests/testNonlinearFactor.cpp

435 lines
15 KiB
C++
Raw Blame History

/* ----------------------------------------------------------------------------
* 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>
#include <gtsam/nonlinear/Symbol.h>
using namespace std;
using namespace gtsam;
using namespace example;
// Convenience for named keys
using symbol_shorthand::X;
using symbol_shorthand::L;
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, X(1),L(1));
// measurement between x2 and l1
Point2 z4(-1.5, -1.);
simulated2D::Measurement f1(z4, sigma, X(2),L(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, X(1)));
Values config;
config.insert(X(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[X(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, X(1),L(1));
Values config;
config.insert(X(1), Point2(1.0, 2.0));
config.insert(L(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[X(1)], -1*A, ord[L(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)), X(1), X(2), X(3), X(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();
}
ADD_CLONE_NONLINEAR_FACTOR(TestFactor4)
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor4) {
TestFactor4 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(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 += X(1), X(2), X(3), X(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)), X(1), X(2), X(3), X(4), X(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();
}
ADD_CLONE_NONLINEAR_FACTOR(TestFactor5)
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor5) {
TestFactor5 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(4), LieVector(1, 4.0));
tv.insert(X(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 += X(1), X(2), X(3), X(4), X(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)), X(1), X(2), X(3), X(4), X(5), X(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();
}
ADD_CLONE_NONLINEAR_FACTOR(TestFactor6)
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor6) {
TestFactor6 tf;
Values tv;
tv.insert(X(1), LieVector(1, 1.0));
tv.insert(X(2), LieVector(1, 2.0));
tv.insert(X(3), LieVector(1, 3.0));
tv.insert(X(4), LieVector(1, 4.0));
tv.insert(X(5), LieVector(1, 5.0));
tv.insert(X(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 += X(1), X(2), X(3), X(4), X(5), X(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()));
}
/* ************************************************************************* */
TEST( NonlinearFactor, clone_rekey )
{
shared_nlf init(new TestFactor4());
EXPECT_LONGS_EQUAL(X(1), init->keys()[0]);
EXPECT_LONGS_EQUAL(X(2), init->keys()[1]);
EXPECT_LONGS_EQUAL(X(3), init->keys()[2]);
EXPECT_LONGS_EQUAL(X(4), init->keys()[3]);
// Standard clone
shared_nlf actClone = init->clone();
EXPECT(actClone.get() != init.get()); // Ensure different pointers
EXPECT(assert_equal(*init, *actClone));
// Re-key factor - clones with different keys
std::vector<Key> new_keys(4);
new_keys[0] = X(5);
new_keys[1] = X(6);
new_keys[2] = X(7);
new_keys[3] = X(8);
shared_nlf actRekey = init->rekey(new_keys);
EXPECT(actRekey.get() != init.get()); // Ensure different pointers
// Ensure init is unchanged
EXPECT_LONGS_EQUAL(X(1), init->keys()[0]);
EXPECT_LONGS_EQUAL(X(2), init->keys()[1]);
EXPECT_LONGS_EQUAL(X(3), init->keys()[2]);
EXPECT_LONGS_EQUAL(X(4), init->keys()[3]);
// Check new keys
EXPECT_LONGS_EQUAL(X(5), actRekey->keys()[0]);
EXPECT_LONGS_EQUAL(X(6), actRekey->keys()[1]);
EXPECT_LONGS_EQUAL(X(7), actRekey->keys()[2]);
EXPECT_LONGS_EQUAL(X(8), actRekey->keys()[3]);
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
Reference in New Issue View Git Blame Copy Permalink