def test_jacobian_self_ominus_other_compact(self):
"""Test that the ``jacobian_self_ominus_other_wrt_self_compact`` and ``jacobian_self_ominus_other_wrt_other_compact`` methods are correctly implemented.
"""
np.random.seed(0)
for _ in range(10):
p1 = PoseSE3(np.random.random_sample(3),
np.random.random_sample(4))
p2 = PoseSE3(np.random.random_sample(3),
np.random.random_sample(4))
p1.normalize()
p2.normalize()
v1 = Vertex(1, p1)
v2 = Vertex(2, p2)
e = EdgeOMinusCompact([1, 2], np.eye(7), np.zeros(7), [v1, v2])
numerical_jacobians = BaseEdge.calc_jacobians(e)
analytical_jacobians = e.calc_jacobians()
self.assertEqual(len(numerical_jacobians),
len(analytical_jacobians))
for n, a in zip(numerical_jacobians, analytical_jacobians):
self.assertAlmostEqual(np.linalg.norm(n - a), 0., 5)
def test_calc_jacobians(self):
"""Test that the ``calc_jacobians`` method works as expected.
"""
p1 = PoseR2([1, 2])
p2 = PoseR2([3, 4])
estimate = PoseR2([0, 0])
v1 = Vertex(1, p1)
v2 = Vertex(2, p2)
e = EdgeOdometry([1, 2], np.eye(2), estimate, [v1, v2])
jacobians = e.calc_jacobians()
self.assertAlmostEqual(np.linalg.norm(jacobians[0] - np.eye(2)), 0.)
self.assertAlmostEqual(np.linalg.norm(jacobians[1] + np.eye(2)), 0.)
def test_plot(self):
"""Test that the ``plot`` method is not implemented.
"""
v_none = Vertex(0, None)
v_r2 = Vertex(1, PoseR2([1, 2]))
v_se2 = Vertex(2, PoseSE2([1, 2], 3))
v_r3 = Vertex(3, PoseR3([1, 2, 3]))
v_se3 = Vertex(4, PoseSE3([1, 2, 3], [0.5, 0.5, 0.5, 0.5]))
with self.assertRaises(NotImplementedError):
e = EdgeOdometry(0, 1, 0, [v_none, v_none])
e.plot()
for v in [v_r2, v_se2, v_r3, v_se3]:
e = EdgeOdometry(0, 1, 0, [v, v])
e.plot()
def test_calc_chi2(self):
"""Test that the ``calc_chi2`` method works as expected.
"""
p = PoseSE2([0, 0], 0)
v = Vertex(0, p)
e = SimpleEdge(0, 1, 0, [v])
self.assertEqual(e.calc_chi2(), 1)
def test_constructor(self):
"""Test that a ``Vertex`` object can be created.
"""
v = Vertex(1, PoseSE2([1, 2], 3))
self.assertEqual(v.id, 1)
self.assertAlmostEqual(
np.linalg.norm(v.pose.to_array() - np.array([1., 2., 3.])), 0.)
def test_calc_error(self):
"""Test that the ``calc_error`` method is not implemented.
"""
p = PoseSE2([0, 0], 0)
v = Vertex(0, p)
e = BaseEdge(0, 1, 0, [v])
with self.assertRaises(NotImplementedError):
_ = e.calc_error()
def test_constructor(self):
"""Test that a ``BaseEdge`` object can be created.
"""
p = PoseSE2([0, 0], 0)
v = Vertex(0, p)
e = BaseEdge(0, 1, 0, [v])
self.assertEqual(e.vertices[0].id, 0)
self.assertEqual(e.information, 1)
def test_plot(self):
"""Test that the ``plot`` method is not implemented.
"""
p = PoseSE2([0, 0], 0)
v = Vertex(0, p)
e = BaseEdge(0, 1, 0, [v])
with self.assertRaises(NotImplementedError):
e.plot()
def setUp(self):
r"""Setup a simple ``Graph`` in :math:`\mathbb{R}^2`.
"""
np.random.seed(0)
p1 = PoseR2(np.random.random_sample(2))
p2 = PoseR2(np.random.random_sample(2))
p3 = PoseR2(np.random.random_sample(2))
estimate = PoseR2([0, 0])
v1 = Vertex(1, p1)
v2 = Vertex(2, p2)
v3 = Vertex(3, p3)
e1 = EdgeOdometry([1, 2], np.eye(2), estimate, [v1, v2])
e2 = EdgeOdometry([3, 2], 2 * np.eye(2), estimate, [v3, v2])
self.g = Graph([e1, e2], [v1, v2, v3])
def test_jacobian_self_oplus_other(self):
"""Test that the ``jacobian_self_oplus_other_wrt_self`` and ``jacobian_self_oplus_other_wrt_other`` methods are correctly implemented.
"""
np.random.seed(0)
for _ in range(10):
p1 = PoseR2(np.random.random_sample(2))
p2 = PoseR2(np.random.random_sample(2))
v1 = Vertex(1, p1)
v2 = Vertex(2, p2)
e = EdgeOPlus([1, 2], np.eye(2), np.zeros(2), [v1, v2])
numerical_jacobians = BaseEdge.calc_jacobians(e)
analytical_jacobians = e.calc_jacobians()
self.assertEqual(len(numerical_jacobians),
len(analytical_jacobians))
for n, a in zip(numerical_jacobians, analytical_jacobians):
self.assertAlmostEqual(np.linalg.norm(n - a), 0.)
def test_calc_chi2_gradient_hessian(self):
"""Test that the ``calc_chi2_gradient_hessian`` method works as expected.
"""
p1 = PoseR2([1, 3])
p2 = PoseR2([2, 4])
estimate = PoseR2([0, 0])
v1 = Vertex(0, p1, 0)
v2 = Vertex(1, p2, 1)
e = EdgeOdometry([0, 1], np.eye(2), estimate, [v1, v2])
chi2, gradient, hessian = e.calc_chi2_gradient_hessian()
self.assertEqual(chi2, 2.)
self.assertAlmostEqual(np.linalg.norm(gradient[0] + np.ones(2)), 0.)
self.assertAlmostEqual(np.linalg.norm(gradient[1] - np.ones(2)), 0.)
self.assertAlmostEqual(np.linalg.norm(hessian[(0, 0)] - np.eye(2)), 0.)
self.assertAlmostEqual(np.linalg.norm(hessian[(0, 1)] + np.eye(2)), 0.)
self.assertAlmostEqual(np.linalg.norm(hessian[(1, 1)] - np.eye(2)), 0.)
def setUp(self):
r"""Setup a simple ``Graph`` in :math:`SE(3)`.
"""
np.random.seed(0)
p1 = PoseSE3(np.random.random_sample(3), np.random.random_sample(4))
p2 = PoseSE3(np.random.random_sample(3), np.random.random_sample(4))
p3 = PoseSE3(np.random.random_sample(3), np.random.random_sample(4))
estimate = PoseSE3([0, 0, 0], [0, 0, 0, 1])
p1.normalize()
p2.normalize()
p3.normalize()
v1 = Vertex(1, p1)
v2 = Vertex(2, p2)
v3 = Vertex(3, p3)
e1 = EdgeOdometry([1, 2], np.eye(6), estimate, [v1, v2])
e2 = EdgeOdometry([3, 2], 2 * np.eye(6), estimate, [v3, v2])
self.g = Graph([e1, e2], [v1, v2, v3])
def test_plot(self):
"""Test that a ``Vertex`` can be plotted.
"""
v_none = Vertex(0, None)
v_r2 = Vertex(1, PoseR2([1, 2]))
v_se2 = Vertex(2, PoseSE2([1, 2], 3))
v_r3 = Vertex(3, PoseR3([1, 2, 3]))
v_se3 = Vertex(4, PoseSE3([1, 2, 3], [0.5, 0.5, 0.5, 0.5]))
with self.assertRaises(NotImplementedError):
v_none.plot()
for v in [v_r2, v_se2, v_r3, v_se3]:
fig = plt.figure()
if len(v.pose.position) == 3:
fig.add_subplot(111, projection='3d')
v.plot()