def from_dict(cls, init_dict):
km = GeometryModel()
urdf = urdf_filler(URDF.from_xml_string(rospy.get_param('/robot_description')))
load_urdf(km, Path(urdf.name), urdf)
km.clean_structure()
km.dispatch_events()
base_frame = init_dict['reference_frame']
eefs = [Endeffector.from_dict(km.get_data(Path(urdf.name)), base_frame, d) for d in init_dict['links']]
return cls(km, Path(urdf.name), eefs)
def test_urdf_insertion(self):
gm = GeometryModel()
sym_a = create_pos('a')
sym_b = create_pos('b')
sym_c = create_pos('c')
box_shape = Geometry('my_box', se.eye(4), 'box')
mesh_shape = Geometry('my_mesh',
se.eye(4),
'mesh',
mesh='package://kineverse/meshes/suzanne.obj')
box_link = KinematicLink('world',
translation3(sym_a, 1, 0),
geometry={'0': box_shape},
collision={'0': box_shape})
mesh_link = KinematicLink('world',
translation3(1, sym_b, 0),
geometry={'0': mesh_shape},
collision={'0': mesh_shape})
robot = URDFRobot('my_bot')
robot.links['my_box'] = box_link
robot.links['my_mesh'] = mesh_link
gm.set_data('my_bot', robot)
gm.clean_structure()
gm.dispatch_events() # Generates the pose expressions for links
sub_world = gm.get_active_geometry({sym_a})
self.assertEquals(len(sub_world.names), 1)
self.assertIn('my_bot/links/my_box', sub_world.names)
self.assertIn('my_bot/links/my_box', sub_world.named_objects)
sub_world = gm.get_active_geometry({sym_a, sym_b})
self.assertEquals(len(sub_world.names), 2)
self.assertIn('my_bot/links/my_box', sub_world.names)
self.assertIn('my_bot/links/my_mesh', sub_world.names)
self.assertIn('my_bot/links/my_box', sub_world.named_objects)
self.assertIn('my_bot/links/my_mesh', sub_world.named_objects)
sub_world = gm.get_active_geometry({sym_c})
self.assertEquals(len(sub_world.names), 0)
)
exit(1)
urdf_model = load_urdf_file(
'package://iai_pr2_description/robots/pr2_calibrated_with_ft2.xml')
# urdf_model = load_urdf_str(rospy.get_param('/robot_description'))
if urdf_model.name.lower() != 'pr2':
print(
f'The loaded robot is not the PR2. Its name is "{urdf_model.name}"'
)
exit(1)
km = GeometryModel()
load_urdf(km, Path('pr2'), urdf_model)
km.clean_structure()
reference_frame = rospy.get_param('~reference_frame',
urdf_model.get_root())
use_base = reference_frame != urdf_model.get_root()
if use_base:
insert_omni_base(km, Path('pr2'), urdf_model.get_root(),
reference_frame)
base_joint_path = Path(f'pr2/joints/to_{reference_frame}')
visualizer = ROSBPBVisualizer('~vis', base_frame=reference_frame)
model_name = load_localized_model(km, model_path, reference_frame)
km.clean_structure()
def main(create_figure=False,
vis_mode=False,
log_csv=True,
min_n_dof=1,
samples=300,
n_observations=25,
noise_lin=0.2,
noise_ang=30,
noise_steps=5):
wait_duration = rospy.Duration(0.1)
vis = ROSBPBVisualizer('ekf_vis', 'world') if vis_mode != 'none' else None
km = GeometryModel()
with open(res_pkg_path('package://iai_kitchen/urdf_obj/IAI_kitchen.urdf'),
'r') as urdf_file:
urdf_kitchen_str = urdf_file.read()
kitchen_model = urdf_filler(
URDF.from_xml_string(hacky_urdf_parser_fix(urdf_kitchen_str)))
load_urdf(km, Path('kitchen'), kitchen_model)
km.clean_structure()
km.dispatch_events()
kitchen = km.get_data('kitchen')
tracking_pools = []
for name, link in kitchen.links.items():
symbols = gm.free_symbols(link.pose)
if len(symbols) == 0:
continue
for x in range(len(tracking_pools)):
syms, l = tracking_pools[x]
if len(symbols.intersection(syms)
) != 0: # BAD ALGORITHM, DOES NOT CORRECTLY IDENTIFY SETS
tracking_pools[x] = (syms.union(symbols),
l + [(name, link.pose)])
break
else:
tracking_pools.append((symbols, [(name, link.pose)]))
# tracking_pools = [tracking_pools[7]]
# print('Identified {} tracking pools:\n{}'.format(len(tracking_pools), tracking_pools))
all_ekfs = [
EKFModel(dict(poses), km.get_constraints_by_symbols(symbols))
for symbols, poses in tracking_pools
] # np.eye(len(symbols)) * 0.001
print('Created {} EKF models'.format(len(all_ekfs)))
print('\n'.join(str(e) for e in all_ekfs))
# Sanity constraint
min_n_dof = min(min_n_dof, len(all_ekfs))
iteration_times = []
for u in range(min_n_dof, len(all_ekfs) + 1):
if rospy.is_shutdown():
break
ekfs = all_ekfs[:u]
observed_poses = {}
for ekf in ekfs:
for link_name, _ in ekf.takers:
observed_poses[link_name] = kitchen.links[link_name].pose
names, poses = zip(*sorted(observed_poses.items()))
state_symbols = union([gm.free_symbols(p) for p in poses])
ordered_state_vars = [
s for _, s in sorted((str(s), s) for s in state_symbols)
]
state_constraints = {}
for n, c in km.get_constraints_by_symbols(state_symbols).items():
if gm.is_symbol(c.expr):
s = gm.free_symbols(c.expr).pop()
fs = gm.free_symbols(c.lower).union(gm.free_symbols(c.upper))
if len(fs.difference({s})) == 0:
state_constraints[s] = (float(gm.subs(c.lower, {s: 0})),
float(gm.subs(c.upper, {s: 0})))
state_bounds = np.array([
state_constraints[s]
if s in state_constraints else [-np.pi * 0.5, np.pi * 0.5]
for s in ordered_state_vars
])
state_fn = gm.speed_up(gm.vstack(*poses), ordered_state_vars)
subworld = km.get_active_geometry(state_symbols)
# Generate observation noise
print('Generating R matrices...')
n_cov_obs = 400
full_log = []
dof_iters = []
# EXPERIMENT
for lin_std, ang_std in [(noise_lin, noise_ang * (np.pi / 180.0))]:
# zip(np.linspace(0, noise_lin, noise_steps),
# np.linspace(0, noise_ang * (np.pi / 180.0), noise_steps)):
if rospy.is_shutdown():
break
# INITIALIZE SENSOR MODEL
training_obs = []
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
observations = state_fn.call2(state)
for _ in range(n_cov_obs):
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
training_obs.append(noisy_obs)
for ekf in ekfs:
ekf.generate_R(training_obs)
# ekf.set_R(np.eye(len(ekf.ordered_vars)) * 0.1)
# Generate figure
gridsize = (4, samples)
plot_size = (4, 4)
fig = plt.figure(figsize=(gridsize[1] * plot_size[0], gridsize[0] *
plot_size[1])) if create_figure else None
gt_states = []
states = [[] for x in range(samples)]
variances = [[] for x in range(samples)]
e_obs = [[] for x in range(samples)]
print('Starting iterations')
for k in tqdm(range(samples)):
if rospy.is_shutdown():
break
state = np.random.uniform(state_bounds.T[0], state_bounds.T[1])
gt_states.append(state)
observations = state_fn.call2(state).copy()
gt_obs_d = {
n: observations[x * 4:x * 4 + 4, :4]
for x, n in enumerate(names)
}
subworld.update_world(dict(zip(ordered_state_vars, state)))
if vis_mode == 'iter' or vis_mode == 'io':
vis.begin_draw_cycle('gt', 'noise', 'estimate', 't_n',
't0')
vis.draw_world('gt', subworld, g=0, b=0)
vis.render('gt')
estimates = []
for ekf in ekfs:
particle = ekf.spawn_particle()
estimates.append(particle)
initial_state = dict(
sum([[(s, v) for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
initial_state = np.array(
[initial_state[s] for s in ordered_state_vars])
if initial_state.min() < state_bounds.T[0].min(
) or initial_state.max() > state_bounds.T[1].max():
raise Exception(
'Estimate initialization is out of bounds: {}'.format(
np.vstack([initial_state, state_bounds.T]).T))
initial_delta = state - initial_state
for y in range(n_observations):
# Add noise to observations
noisy_obs = {}
for x, noise in enumerate([
t.dot(r) for t, r in zip(
random_normal_translation(
len(poses), 0, lin_std),
random_rot_normal(len(poses), 0, ang_std))
]):
noisy_obs[names[x]] = observations[x * 4:x * 4 +
4, :4].dot(noise)
if vis_mode in {'iter', 'iter-trail'} or (vis_mode == 'io'
and y == 0):
for n, t in noisy_obs.items():
subworld.named_objects[Path(
('kitchen', 'links', n))].np_transform = t
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('noise')
vis.draw_world('noise', subworld, r=0, g=0, a=0.1)
vis.render('noise')
start_time = time()
for estimate, ekf in zip(estimates, ekfs):
if y > 0:
control = np.zeros(len(ekf.ordered_controls))
estimate.state, estimate.cov = ekf.predict(
estimate.state.flatten(), estimate.cov,
control)
obs_vector = ekf.gen_obs_vector(noisy_obs)
estimate.state, estimate.cov = ekf.update(
estimate.state, estimate.cov,
ekf.gen_obs_vector(noisy_obs))
if vis_mode in {'iter', 'iter-trail'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
else:
obs_vector = ekf.gen_obs_vector(noisy_obs)
for _ in range(1):
h_prime = ekf.h_prime_fn.call2(estimate.state)
obs_delta = obs_vector.reshape(
(len(obs_vector), 1)) - ekf.h_fn.call2(
estimate.state)
estimate.state += (h_prime.T.dot(obs_delta) *
0.1).reshape(
estimate.state.shape)
if vis_mode in {'iter', 'io'}:
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
if vis_mode != 'none' and y == 0:
vis.draw_world('t0', subworld, b=0, a=1)
vis.render('t0')
elif vis_mode in {'iter', 'iter-trail'}:
if vis_mode != 'iter-trail':
vis.begin_draw_cycle('t_n')
vis.draw_world('t_n', subworld, b=0, a=1)
vis.render('t_n')
if log_csv or fig is not None:
e_state_d = dict(
sum([[(s, v)
for s, v in zip(ekf.ordered_vars, e.state)]
for e, ekf in zip(estimates, ekfs)], []))
covs = dict(
sum([[(s, v) for s, v in zip(
ekf.ordered_vars, np.sqrt(np.trace(e.cov)))]
for e, ekf in zip(estimates, ekfs)], []))
e_state = np.hstack([
e_state_d[s] for s in ordered_state_vars
]).reshape((len(e_state_d), ))
if log_csv:
full_log.append(
np.hstack(
([lin_std,
ang_std], state, e_state.flatten(),
np.array([
covs[s] for s in ordered_state_vars
]))))
if fig is not None:
e_obs[k].append(
np.array([
np.abs(
ekf.gen_obs_vector(gt_obs_d) -
ekf.h_fn.call2(e.state)).max()
for e, ekf in zip(estimates, ekfs)
]))
states[k].append(e_state)
variances[k].append(
np.array([covs[s]
for s in ordered_state_vars]))
else:
if vis_mode == 'io':
for estimate, ekf in zip(estimates, ekfs):
subworld.update_world({
s: v
for s, v in zip(ekf.ordered_vars,
estimate.state)
})
vis.draw_world('t_n', subworld, r=0, b=0, a=1)
vis.render('t_n')
dof_iters.append(time() - start_time)
if fig is not None:
axes = [
plt.subplot2grid(gridsize, (y, 0), colspan=1, rowspan=1)
for y in range(gridsize[0])
]
axes = np.array(
sum([[
plt.subplot2grid(gridsize, (y, x),
colspan=1,
rowspan=1,
sharey=axes[y])
for y in range(gridsize[0])
] for x in range(1, gridsize[1])], axes)).reshape(
(gridsize[1], gridsize[0]))
for x, (gt_s, state, variance, obs_delta,
(ax_s, ax_d, ax_o, ax_v)) in enumerate(
zip(gt_states, states, variances, e_obs, axes)):
for y in gt_s:
ax_s.axhline(y, xmin=0.97, xmax=1.02)
ax_s.set_title('State; Sample: {}'.format(x))
ax_d.set_title('Delta from GT; Sample: {}'.format(x))
ax_o.set_title('Max Delta in Obs; Sample: {}'.format(x))
ax_v.set_title('Standard Deviation; Sample: {}'.format(x))
ax_s.plot(state)
ax_d.plot(gt_s - np.vstack(state))
ax_o.plot(obs_delta)
ax_v.plot(variance)
ax_s.grid(True)
ax_d.grid(True)
ax_o.grid(True)
ax_v.grid(True)
fig.tight_layout()
plt.savefig(
res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_{}_{}.png'
.format(lin_std, ang_std)))
iteration_times.append(dof_iters)
if log_csv:
df = pd.DataFrame(
columns=['lin_std', 'ang_std'] +
['gt_{}'.format(x) for x in range(len(state_symbols))] +
['ft_{}'.format(x) for x in range(len(state_symbols))] +
['var_{}'.format(x) for x in range(len(state_symbols))],
data=full_log)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker.csv'
),
index=False)
df = pd.DataFrame(
columns=[str(x) for x in range(1,
len(iteration_times) + 1)],
data=np.vstack(iteration_times).T)
df.to_csv(res_pkg_path(
'package://kineverse_experiment_world/test/ekf_object_tracker_performance.csv'
),
index=False)