def test_cmp_with_ss_impl(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
_, measurements, _, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, 10)
measurements = measurements[:, :2]
self.assertAlmostEqual(
analytical_smoothing_cost(ss_ms, measurements, prior_mean,
prior_cov, motion_model, meas_model),
1.039569495177240e03,
)
def test_cmp_with_ss_impl(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
_, measurements, ss_mf, _ = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.Extended, None)
measurements = measurements[:, :2]
ekf = Ekf(motion_model, meas_model)
mf, Pf, _, _ = ekf.filter_seq(measurements, prior_mean, prior_cov)
self.assertTrue(np.allclose(mf, ss_mf))
def test_cmp_with_ss_impl(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 1
_, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, num_iter)
measurements = measurements[:, :2]
cost_fn = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
K = measurements.shape[0]
init_traj = (np.zeros((K, prior_mean.shape[0])), None)
ieks = Ieks(motion_model, meas_model, num_iter)
mf, Pf, ms, Ps, _iter_cost = ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
self.assertTrue(np.allclose(mf, ss_mf))
self.assertTrue(np.allclose(ms, ss_ms))
num_iter = 10
_, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, num_iter)
measurements = measurements[:, :2]
ieks = Ieks(motion_model, meas_model, num_iter=num_iter)
mf, Pf, ms, Ps, _iter_cost = ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
self.assertTrue(np.allclose(mf, ss_mf))
self.assertTrue(np.allclose(ms, ss_ms))
def test_lambda_zero_results_in_plain_ieks(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 3
states, measurements = simulate_data(motion_model, meas_model,
prior_mean[:-1], 20)
lambda_ = 0.0
nu = 10
cost_improv_iter_lim = 10
cost_fn = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
K = measurements.shape[0]
init_traj = (np.zeros((K, prior_mean.shape[0])), None)
ieks = Ieks(motion_model, meas_model, num_iter)
mf, Pf, ms, Ps, _iter_cost = ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
lm_ieks = LmIeks(motion_model, meas_model, num_iter,
cost_improv_iter_lim, lambda_, nu)
lm_mf, lm_Pf, lm_ms, lm_Ps, _iter_cost = lm_ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
self.assertTrue(np.allclose(mf, lm_mf))
self.assertTrue(np.allclose(ms, lm_ms))
def test_cmp_with_ss_impl(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 1
_, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, num_iter)
measurements = measurements[:, :2]
cost_fn = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
K = measurements.shape[0]
init_traj = (np.zeros((K, prior_mean.shape[0])), None)
ieks = Ieks(motion_model, meas_model, num_iter)
_, _, x_0, _, _ = ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
_, _, x_1, _, _ = ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, (x_0, None), 1, cost_fn)
self.assertEqual(x_0.shape, x_1.shape)
num_grid_points = 10
ls = GridSearch(cost_fn, num_grid_points)
next_x, alpha, _ = ls.search_next(x_0, x_1)
self.assertEqual(next_x.shape, x_0.shape)
self.assertTrue(np.allclose(next_x, x_0 + alpha * (x_1 - x_0)))
def test_lambda_zero_results_in_plain_ipls(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 3
states, measurements = simulate_data(motion_model, meas_model,
prior_mean[:-1], 20)
sigma_point_method = SphericalCubature()
cost_fn = partial(
slr_smoothing_cost_pre_comp,
measurements=measurements,
m_1_0=prior_mean,
P_1_0_inv=np.linalg.inv(prior_cov),
)
ipls = SigmaPointIpls(motion_model, meas_model, sigma_point_method,
num_iter)
mf, Pf, ms, Ps, _ = ipls.filter_and_smooth(measurements, prior_mean,
prior_cov, cost_fn)
lm_ipls = SigmaPointLmIpls(motion_model, meas_model,
sigma_point_method, num_iter, 10, 0.0, 10)
lm_mf, lm_Pf, lm_ms, lm_Ps, _ = lm_ipls.filter_and_smooth(
measurements, prior_mean, prior_cov, cost_fn)
# lm_ieks = LmIeks(motion_model, meas_model, num_iter, cost_improv_iter_lim, lambda_, nu)
# lm_mf, lm_Pf, lm_ms, lm_Ps, _iter_cost = lm_ieks.filter_and_smooth_with_init_traj(
# measurements, prior_mean, prior_cov, init_traj, 1, cost_fn
# )
self.assertTrue(np.allclose(mf, lm_mf))
self.assertTrue(np.allclose(ms, lm_ms))
def test_cmp_time_indep_and_time_dep(self):
dt = 0.01
time_steps = 2
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
states, measurements = simulate_data(motion_model, meas_model,
prior_mean[:-1], time_steps)
a = 1 + dt * 10 * np.cumsum(np.random.randn(1, time_steps))
states = np.column_stack((states, a))
measurements = measurements[:, :2]
self.assertAlmostEqual(
analytical_smoothing_cost(states, measurements, prior_mean,
prior_cov, motion_model, meas_model),
analytical_smoothing_cost_time_dep(states, measurements,
prior_mean, prior_cov,
motion_model, meas_model),
)
def test_cmp_with_ss_impl(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 1
states, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.LM, num_iter)
measurements = measurements[:, :2]
lambda_ = 1e-2
nu = 10
cost_improv_iter_lim = 10
cost_fn = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
K = measurements.shape[0]
init_traj = (np.zeros((K, prior_mean.shape[0])), None)
lm_ieks = LmIeks(motion_model, meas_model, num_iter,
cost_improv_iter_lim, lambda_, nu)
mf, Pf, ms, Ps, _iter_cost = lm_ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
self.assertTrue(np.allclose(mf, ss_mf))
self.assertTrue(np.allclose(ms, ss_ms))
num_iter = 10
_, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.LM, num_iter)
measurements = measurements[:, :2]
lm_ieks = LmIeks(motion_model, meas_model, num_iter,
cost_improv_iter_lim, lambda_, nu)
mf, Pf, ms, Ps, _iter_cost = lm_ieks.filter_and_smooth_with_init_traj(
measurements, prior_mean, prior_cov, init_traj, 1, cost_fn)
self.assertTrue(np.allclose(mf, ss_mf, atol=1e-5))
self.assertTrue(np.allclose(ms, ss_ms))
# Summation over the time steps and columns of the cov seq.
matlab_covs_sum = np.array([1.8843, 0.3417, 20.4884, 2.2148, 498.6999])
self.assertTrue(
np.allclose(Ps.sum(0).sum(1),
matlab_covs_sum,
rtol=1e-4,
atol=1e-4))
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 1
_, measurements, ss_mf, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, num_iter)
measurements = measurements[:, :2]
cost_fn = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
def main():
log = logging.getLogger(__name__)
experiment_name = "lm_ieks"
setup_logger(f"logs/{experiment_name}.log", logging.WARNING)
log.info(f"Running experiment: {experiment_name}")
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
num_iter = 1
np.random.seed(0)
states, all_meas, _, xs_ss = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.LM, num_iter)
K = all_meas.shape[0]
covs = np.array([prior_cov] * K) * (0.90 + np.random.rand() / 5)
meas_model = MultiSensorRange(sensors, R)
measurements = all_meas[:, :2]
cost_fn_eks = partial(
analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
dir_der_eks = partial(
dir_der_analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
sigma_point_method = SphericalCubature()
cost_fn_ipls = partial(
slr_smoothing_cost_pre_comp,
measurements=measurements,
m_1_0=prior_mean,
P_1_0_inv=np.linalg.inv(prior_cov),
)
time_ieks = partial(
Ieks(motion_model, meas_model,
num_iter).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
noop_cost,
)
time_lm_ieks = partial(
LmIeks(motion_model, meas_model, num_iter, 10, 1e-2,
10).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
cost_fn_eks,
)
time_ls_ieks = partial(
LsIeks(
motion_model,
meas_model,
num_iter,
ArmijoLineSearch(cost_fn_eks, dir_der_eks, c_1=0.1),
).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
cost_fn_eks,
)
time_ipls = partial(
SigmaPointIpls(motion_model, meas_model, sigma_point_method,
num_iter).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
slr_noop_cost,
)
time_lm_ipls = partial(
SigmaPointLmIpls(motion_model,
meas_model,
sigma_point_method,
num_iter,
cost_improv_iter_lim=10,
lambda_=1e-2,
nu=10).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
cost_fn_ipls,
)
cost_fn_ls_ipls = partial(
slr_smoothing_cost_means,
measurements=measurements,
m_1_0=prior_mean,
P_1_0_inv=np.linalg.inv(prior_cov),
motion_fn=motion_model.map_set,
meas_fn=meas_model.map_set,
slr_method=SigmaPointSlr(sigma_point_method),
)
time_ls_ipls = partial(
SigmaPointLsIpls(motion_model, meas_model, sigma_point_method,
num_iter, partial(ArmijoLineSearch, c_1=0.1),
10).filter_and_smooth_with_init_traj,
measurements,
prior_mean,
prior_cov,
(xs_ss, covs),
1,
cost_fn_ls_ipls,
)
num_samples = 10
time_ieks = timeit(time_ieks,
number=num_samples) / (num_iter * num_samples)
time_lm_ieks = timeit(time_lm_ieks,
number=num_samples) / (num_iter * num_samples)
time_ls_ieks = timeit(time_ls_ieks,
number=num_samples) / (num_iter * num_samples)
time_ipls = timeit(time_ipls,
number=num_samples) / (num_iter * num_samples)
time_lm_ipls = timeit(time_lm_ipls,
number=num_samples) / (num_iter * num_samples)
time_ls_ipls = timeit(time_ls_ipls,
number=num_samples) / (num_iter * num_samples)
print(f"IEKS: {time_ieks:.2f} s, 100.0%")
print(f"LM-IEKS: {time_lm_ieks:.2f} s, {time_lm_ieks/time_ieks*100:.2f}%")
print(f"LS-IEKS: {time_ls_ieks:.2f} s, {time_ls_ieks/time_ieks*100:.2f}%")
print(f"IPLS: {time_ipls:.2f} s, {time_ipls/time_ieks*100:.2f}%")
print(f"LM-IPLS: {time_lm_ipls:.2f} s, {time_lm_ipls/time_ieks*100:.2f}%")
print(f"LS-IPLS: {time_ls_ipls:.2f} s, {time_ls_ipls/time_ieks*100:.2f}%")
def main():
dt = 0.01
qc = 0.01
qw = 10
Q = np.array(
[
[qc * dt ** 3 / 3, 0, qc * dt ** 2 / 2, 0, 0],
[0, qc * dt ** 3 / 3, 0, qc * dt ** 2 / 2, 0],
[qc * dt ** 2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt ** 2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
]
)
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std ** 2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
_, measurements, _, ss_ms = get_specific_states_from_file(Path.cwd() / "data/lm_ieks_paper", Type.GN, 10)
measurements = measurements[:, :2]
K = measurements.shape[0]
covs = np.array([prior_cov] * K)
slr_cache = SlrCache(motion_model.map_set, meas_model.map_set, SigmaPointSlr(SphericalCubature()))
slr_cache.update(ss_ms, covs)
new_proto = partial(
slr_smoothing_cost_pre_comp,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
)
new = partial(
new_proto,
traj=ss_ms,
proc_bar=slr_cache.proc_bar,
meas_bar=slr_cache.meas_bar,
proc_cov=np.array(
[err_cov_k + motion_model.proc_noise(k) for k, (_, _, err_cov_k) in enumerate(slr_cache.proc_lin)]
),
meas_cov=np.array(
[err_cov_k + meas_model.meas_noise(k) for k, (_, _, err_cov_k) in enumerate(slr_cache.meas_lin)]
),
)
old = partial(
slr_smoothing_cost,
ss_ms,
covs,
measurements,
prior_mean,
prior_cov,
motion_model,
meas_model,
SigmaPointSlr(SphericalCubature()),
)
num_samples = 10
old_time = timeit(old, number=num_samples) / num_samples
print(old_time)
new_time = timeit(new, number=num_samples) / num_samples
print(new_time)
assert np.allclose(new(), old())
def main():
args = parse_args()
log = logging.getLogger(__name__)
experiment_name = "ct_experiment_realisation"
setup_logger(f"logs/{experiment_name}.log", logging.DEBUG)
log.info(f"Running experiment: {experiment_name}")
if not args.random:
np.random.seed(2)
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
lambda_ = 1e-0
num_iter = args.num_iter
if args.meas_type == MeasType.Range:
meas_model = MultiSensorRange(sensors, R)
meas_cols = np.array([0, 1])
elif args.meas_type == MeasType.Bearings:
meas_model = MultiSensorBearings(sensors, R)
meas_cols = np.array([2, 3])
log.info("Generating states and measurements.")
if args.random:
states, measurements = simulate_data(motion_model,
meas_model,
prior_mean[:-1],
time_steps=500)
else:
states, all_meas, _, xs_ss = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.LM, num_iter)
measurements = all_meas[:, meas_cols]
if args.var_sensors:
single_sensor = sens_pos_2.reshape((1, 2))
std = 0.001
R_certain = std**2 * np.eye(1)
single_meas_model = MultiSensorBearings(single_sensor, R_certain)
# Create a set of time steps where the original two sensor measurements are replaced with single ones.
# single_meas_time_steps = set(list(range(0, 100, 5))[1:])
single_meas_time_steps = set(list(range(0, 500, 50))[1:])
meas_model = BearingsVaryingSensors(meas_model, single_meas_model,
single_meas_time_steps)
# Change measurments so that some come from the alternative model.
measurements = modify_meas(measurements, states, meas_model, True)
results = []
cost_fn_eks = partial(
analytical_smoothing_cost_time_dep,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
D_x = prior_mean.shape[0]
K = len(measurements)
init_traj = (np.zeros((K, D_x)), np.array(K * [prior_cov]))
# log.info("Running IEKS...")
# ms_ieks, Ps_ieks, cost_ieks, rmses_ieks, neeses_ieks = run_smoothing(
# Ieks(motion_model, meas_model, num_iter), states, measurements, prior_mean, prior_cov, cost_fn_eks, init_traj
# )
# results.append(
# (ms_ieks, Ps_ieks, cost_ieks[1:], "IEKS"),
# )
# log.info("Running LM-IEKS...")
# ms_lm_ieks, Ps_lm_ieks, cost_lm_ieks, rmses_lm_ieks, neeses_lm_ieks = run_smoothing(
# LmIeks(motion_model, meas_model, num_iter, 10, lambda_, 10),
# states,
# measurements,
# prior_mean,
# prior_cov,
# cost_fn_eks,
# init_traj,
# )
# results.append(
# (ms_lm_ieks, Ps_lm_ieks, cost_lm_ieks[1:], "LM-IEKS"),
# )
log.info("Running LS-IEKS...")
dir_der_eks = partial(
dir_der_analytical_smoothing_cost,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
)
ms_ls_ieks, Ps_ls_ieks, cost_ls_ieks, rmses_ls_ieks, neeses_ls_ieks = run_smoothing(
LsIeks(
motion_model,
meas_model,
num_iter,
ArmijoLineSearch(cost_fn_eks, dir_der_eks, c_1=0.1),
),
states,
measurements,
prior_mean,
prior_cov,
cost_fn_eks,
init_traj,
)
results.append((ms_ls_ieks, Ps_ls_ieks, cost_ls_ieks[1:], "LS-IEKS"), )
sigma_point_method = SphericalCubature()
# cost_fn_ipls = partial(
# slr_smoothing_cost_pre_comp, measurements=measurements, m_1_0=prior_mean, P_1_0_inv=np.linalg.inv(prior_cov)
# )
# log.info("Running IPLS...")
# ms_ipls, Ps_ipls, cost_ipls, rmses_ipls, neeses_ipls = run_smoothing(
# SigmaPointIpls(motion_model, meas_model, sigma_point_method, num_iter),
# states,
# measurements,
# prior_mean,
# prior_cov,
# None,
# init_traj,
# )
# results.append(
# (ms_ipls, Ps_ipls, cost_ipls, "IPLS"),
# )
# log.info("Running LM-IPLS...")
# ms_lm_ipls, Ps_lm_ipls, cost_lm_ipls, rmses_lm_ipls, neeses_lm_ipls = run_smoothing(
# SigmaPointLmIpls(
# motion_model, meas_model, sigma_point_method, num_iter, cost_improv_iter_lim=10, lambda_=lambda_, nu=10
# ),
# states,
# measurements,
# prior_mean,
# prior_cov,
# cost_fn_ipls,
# init_traj,
# )
# results.append(
# (ms_lm_ipls, Ps_lm_ipls, cost_lm_ipls, "LM-IPLS"),
# )
# cost_fn_ls_ipls = partial(
# slr_smoothing_cost_means,
# measurements=measurements,
# m_1_0=prior_mean,
# P_1_0_inv=np.linalg.inv(prior_cov),
# motion_fn=motion_model.map_set,
# meas_fn=meas_model.map_set,
# slr_method=SigmaPointSlr(sigma_point_method),
# )
# log.info("Running LS-IPLS...")
# ms_ls_ipls, Ps_ls_ipls, cost_ls_ipls, rmses_ls_ipls, neeses_ls_ipls = run_smoothing(
# SigmaPointLsIpls(
# motion_model, meas_model, sigma_point_method, num_iter, partial(ArmijoLineSearch, c_1=0.1), 10
# ),
# states,
# measurements,
# prior_mean,
# prior_cov,
# cost_fn_ls_ipls,
# init_traj,
# )
# results.append(
# (ms_ls_ipls, Ps_ls_ipls, cost_ls_ipls, "LS-IPLS"),
# )
for ms, _, _, label in results:
tikz_2d_traj(Path.cwd() / "tikz", ms[:, :2], label)
plot_results(
states,
results,
None,
)
def test_cmp_slr_costs(self):
dt = 0.01
qc = 0.01
qw = 10
Q = np.array([
[qc * dt**3 / 3, 0, qc * dt**2 / 2, 0, 0],
[0, qc * dt**3 / 3, 0, qc * dt**2 / 2, 0],
[qc * dt**2 / 2, 0, qc * dt, 0, 0],
[0, qc * dt**2 / 2, 0, qc * dt, 0],
[0, 0, 0, 0, dt * qw],
])
motion_model = CoordTurn(dt, Q)
sens_pos_1 = np.array([-1.5, 0.5])
sens_pos_2 = np.array([1, 1])
sensors = np.row_stack((sens_pos_1, sens_pos_2))
std = 0.5
R = std**2 * np.eye(2)
meas_model = MultiSensorRange(sensors, R)
prior_mean = np.array([0, 0, 1, 0, 0])
prior_cov = np.diag([0.1, 0.1, 1, 1, 1])
_, measurements, _, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.LM, 10)
measurements = measurements[:, :2]
K = measurements.shape[0]
np.random.seed(0)
covs = np.array([prior_cov] * K) * (0.90 + np.random.rand() / 5)
slr_cache = SlrCache(motion_model, meas_model,
SigmaPointSlr(SphericalCubature()))
pre_comp_proto = partial(
slr_smoothing_cost_pre_comp,
measurements=measurements,
m_1_0=prior_mean,
P_1_0_inv=np.linalg.inv(prior_cov),
)
on_the_fly = partial(
slr_smoothing_cost,
covs=covs,
measurements=measurements,
m_1_0=prior_mean,
P_1_0=prior_cov,
motion_model=motion_model,
meas_model=meas_model,
slr=SigmaPointSlr(SphericalCubature()),
)
slr_cache.update(ss_ms, covs)
varying_means_proto = partial(
slr_smoothing_cost_means,
measurements=measurements,
m_1_0=prior_mean,
P_1_0_inv=np.linalg.inv(prior_cov),
estimated_covs=covs,
motion_fn=motion_model.map_set,
meas_fn=meas_model.map_set,
slr_method=SigmaPointSlr(SphericalCubature()),
)
varying_means = partial(
varying_means_proto,
motion_cov_inv=slr_cache.proc_cov_inv,
meas_cov_inv=slr_cache.meas_cov_inv,
)
proc_cov_inv, meas_cov_inv = slr_cache.inv_cov()
pre_comp = partial(
pre_comp_proto,
motion_bar=slr_cache.proc_bar,
meas_bar=slr_cache.meas_bar,
motion_cov_inv=proc_cov_inv,
meas_cov_inv=meas_cov_inv,
)
self.assertAlmostEqual(pre_comp(ss_ms), on_the_fly(ss_ms))
self.assertAlmostEqual(pre_comp(ss_ms), varying_means(ss_ms))
_, measurements, _, ss_ms = get_specific_states_from_file(
Path.cwd() / "data/lm_ieks_paper", Type.GN, 1)
slr_cache.update(ss_ms, covs)
pre_comp = partial(
pre_comp_proto,
motion_bar=slr_cache.proc_bar,
meas_bar=slr_cache.meas_bar,
motion_cov_inv=slr_cache.proc_cov_inv,
meas_cov_inv=slr_cache.meas_cov_inv,
)
varying_means = partial(
varying_means_proto,
motion_cov_inv=slr_cache.proc_cov_inv,
meas_cov_inv=slr_cache.meas_cov_inv,
)
self.assertAlmostEqual(pre_comp(ss_ms), on_the_fly(ss_ms))
self.assertAlmostEqual(pre_comp(ss_ms), varying_means(ss_ms))