def _kf_update_(self, weights, states, covs, h_mat, r_mat, z):
# predicted observations
#pred_z = blas.dgemv(h_mat, states)
pred_z = np.array(np.dot(h_mat, states.T).T, order='C')
# covariance is the same for all states, do the update for one matrix
upd_weights = weights.copy()
upd_cov0, kalman_info = np_kf_update_cov(covs[0], h_mat, r_mat, False)
upd_covs = np.repeat(upd_cov0[np.newaxis], covs.shape[0], axis=0)
# Update the states
upd_states, residuals = kf_update_x(states, pred_z, z,
np.array([kalman_info.kalman_gain], order='C'))
if not upd_states.flags.c_contiguous:
upd_states = np.array(upd_states, order='C')
# Evaluate the new weight
#x_pdf = np.exp(-0.5*np.power(
# blas.dgemv(np.array([kalman_info.inv_sqrt_S]), residuals), 2).sum(axis=1))/ \
# np.sqrt(kalman_info.det_S*(2*np.pi)**z.shape[0])
x_pdf = np.exp(-0.5*np.power(
np.dot(kalman_info.inv_sqrt_S, residuals.T).T, 2).sum(axis=1))/ \
np.sqrt(kalman_info.det_S*(2*np.pi)**z.shape[0])
upd_weights = weights * x_pdf
upd_weights /= upd_weights.sum()
return upd_weights, upd_states, upd_covs
def g500_kf_update(weights, states, covs, obs_matrix, obs_noise, z):
upd_weights = weights.copy()
#upd_states = np.empty(states.shape)
#upd_covs = np.empty(covs.shape)
# Covariance is the same for all the particles
upd_cov0, kalman_info = kf_update_cov(np.array([covs[0]]), obs_matrix, obs_noise, False)
upd_covs = np.repeat(upd_cov0, covs.shape[0], axis=0)
# Update the states
pred_z = blas.dgemv(obs_matrix, states)
upd_states, residuals = kf_update_x(states, pred_z, z, kalman_info.kalman_gain)
# Evaluate the new weight
x_pdf = np.exp(-0.5*np.power(
blas.dgemv(kalman_info.inv_sqrt_S, residuals), 2).sum(axis=1))/ \
np.sqrt(kalman_info.det_S*(2*np.pi)**z.shape[0])
upd_weights = weights * x_pdf
upd_weights /= upd_weights.sum()
"""
for count in range(states.shape[0]):
this_state = states[count]
this_state.shape = (1,) + this_state.shape
this_cov = covs[count]
this_cov.shape = (1,) + this_cov.shape
(upd_state, upd_covariance, kalman_info) = \
kf_update(this_state, this_cov, obs_matrix, obs_noise, z, False)
#x_pdf = misctools.mvnpdf(x, mu, sigma)
x_pdf = np.exp(-0.5*np.power(
blas.dgemv(kalman_info.inv_sqrt_S, kalman_info.residuals), 2).sum(axis=1))/ \
np.sqrt(kalman_info.det_S*(2*np.pi)**z.shape[0])
upd_weights[count] *= x_pdf
upd_states[count] = upd_state
upd_covs[count] = upd_covariance
upd_weights /= upd_weights.sum()
"""
return upd_weights, upd_states, upd_covs
def update(self, observations, observation_noise):
self.flags.ESTIMATE_IS_VALID = False
# Container for slam parent update
slam_info = STRUCT()
slam_info.likelihood = 1
num_observations, z_dim = (observations.shape + (3,))[0:2]
if not self.weights.shape[0]:
return slam_info
detection_probability = self.camera_pd(self.parent_ned,
self.parent_rpy, self.states)
#detection_probability[detection_probability<0.1] = 0
clutter_pdf = self.camera_clutter(observations)
clutter_intensity = self.vars.clutter_intensity*clutter_pdf
self.flags.LOCK.acquire()
try:
# Account for missed detection
prev_weights = self.weights.copy()
prev_states = self.states.copy()
prev_covs = self.covs.copy()
updated = STRUCT()
updated.weights = [self.weights*(1-detection_probability)]
updated.states = [self.states]
updated.covs = [self.covs]
# Do the update only for detected landmarks
detected_indices = detection_probability >= 0
detected = STRUCT()
detected.weights = ( prev_weights[detected_indices]*
detection_probability[detected_indices] )
detected.states = prev_states[detected_indices]
detected.covs = prev_covs[detected_indices]
#ZZ SLAM, step 1:
slam_info.exp_sum__pd_predwt = np.exp(-detected.weights.sum())
# SLAM, prep for step 2:
slam_info.sum__clutter_with_pd_updwt = np.zeros(num_observations)
if detected.weights.shape[0]:
# Covariance update part of the Kalman update is common to all
# observation-updates
if observations.shape[0]:
h_mat = featuredetector.tf.relative_rot_mat(self.parent_rpy)
# Predicted observation from the current states
pred_z = featuredetector.tf.relative(self.parent_ned,
self.parent_rpy, detected.states)
observation_noise = observation_noise[0]
detected.covs, kalman_info = kf_update_cov(detected.covs,
h_mat,
observation_noise,
INPLACE=True)
# We need to update the states and find the updated weights
for (_observation_, obs_count) in zip(observations,
range(num_observations)):
#new_x = copy.deepcopy(x)
# Apply the Kalman update to get the new state -
# update in-place and return the residuals
upd_states, residuals = kf_update_x(detected.states, pred_z,
_observation_,
kalman_info.kalman_gain,
INPLACE=False)
# Calculate the weight of the Gaussians for this observation
# Calculate term in the exponent
#code.interact(local=locals())
#x_pdf = np.exp(-0.5*np.power(
# blas.dgemv(kalman_info.inv_sqrt_S,
# residuals, TRANSPOSE_A=True), 2).sum(axis=1))/ \
# np.sqrt(kalman_info.det_S*(2*np.pi)**z_dim)
x_pdf = misctools.mvnpdf(_observation_, pred_z, kalman_info.S)
upd_weights = detected.weights*x_pdf
# Normalise the weights
normalisation_factor = ( clutter_intensity[obs_count] +
#self.vars.birth_intensity +
upd_weights.sum() )
upd_weights /= normalisation_factor
#print "Obs Index: ", str(obs_count+1)
#print upd_weights.sum()
# SLAM, step 2:
slam_info.sum__clutter_with_pd_updwt[obs_count] = \
normalisation_factor
# Create new state with new_x and P to add to _states_
updated.weights += [upd_weights.copy()]
updated.states += [upd_states.copy()]
updated.covs += [detected.covs.copy()]
#print upd_weights.sum()
else:
slam_info.sum__clutter_with_pd_updwt = np.array(clutter_intensity)
self.weights = np.concatenate(updated.weights)
self.states = np.concatenate(updated.states)
self.covs = np.concatenate(updated.covs)
# SLAM, finalise:
slam_info.likelihood = (slam_info.exp_sum__pd_predwt *
slam_info.sum__clutter_with_pd_updwt.prod())
assert self.weights.shape[0] == self.states.shape[0] == self.covs.shape[0], "Lost states!!"
finally:
self.flags.LOCK.release()
return slam_info
def phdUpdate(self, observation_set):
num_observations = observation_set.shape[0]
if num_observations:
z_dim = observation_set.shape[1]
else:
z_dim = 0
if not self.weights.shape[0]:
self._states_ = self.states.copy()
self._weights_ = self.weights.copy()
return
detection_probability = self.parameters.pd_fn.handle(self.states, self.parameters.pd_fn.parameters)
# clutter_pdf = [self.clutter_fn.handle(_observation_,
# self.clutter_fn.parameters) \
# for _observation_ in observation_set]
clutter_pdf = self.parameters.clutter_fn.handle(observation_set, self.parameters.clutter_fn.parameters)
# Account for missed detection
self._states_ = self.states.copy()
self._weights_ = [self.weights * (1 - detection_probability)]
# Split x and P out from the combined state vector
detected_indices = detection_probability > 0
detected_states = self.states[detected_indices]
x = detected_states.state
P = detected_states.covariance
# Scale the weights by detection probability
weights = self.weights[detected_indices] * detection_probability[detected_indices]
if x.shape[0]:
# Part of the Kalman update is common to all observation-updates
x, P, kalman_info = kalmanfilter.kf_update(
x,
P,
np.array([self.parameters.obs_fn.parameters.H]),
np.array([self.parameters.obs_fn.parameters.R]),
None,
INPLACE=True,
) # USE_NP=0)
# Container for the updated states
new_gmstate = self.states.__class__(0)
# We need to update the states and find the updated weights
for (_observation_, obs_count) in zip(observation_set, range(num_observations)):
# new_x = copy.deepcopy(x)
# Apply the Kalman update to get the new state - update in-place
# and return the residuals
new_x, residuals = kalmanfilter.kf_update_x(
x, kalman_info.pred_z, _observation_, kalman_info.kalman_gain, INPLACE=False
)
# Calculate the weight of the Gaussians for this observation
# Calculate term in the exponent
x_pdf = np.exp(-0.5 * np.power(blas.dgemv(kalman_info.inv_sqrt_S, residuals), 2).sum(axis=1)) / np.sqrt(
kalman_info.det_S * (2 * np.pi) ** z_dim
)
new_weight = weights * x_pdf
# Normalise the weights
normalisation_factor = clutter_pdf[obs_count] + new_weight.sum()
new_weight /= normalisation_factor
# Create new state with new_x and P to add to _states_
new_gmstate.set(new_x, P)
self._states_.append(new_gmstate)
self._weights_ += [new_weight]
self._weights_ = np.concatenate(self._weights_)
def phdUpdate(self, observation_set):
# Container for slam parent update
slam_info = PARAMETERS()
num_observations = observation_set.shape[0]
if num_observations:
z_dim = observation_set.shape[1]
else:
z_dim = 0
if not self.weights.shape[0]:
self._states_ = self.states.copy()
self._weights_ = self.weights.copy()
return
detection_probability = self.parameters.pd_fn.handle(self.states,
self.parameters.pd_fn.parameters)
#clutter_pdf = [self.clutter_fn.handle(_observation_,
# self.clutter_fn.parameters) \
# for _observation_ in observation_set]
clutter_pdf = self.parameters.clutter_fn.handle(observation_set,
self.parameters.clutter_fn.parameters)
# Account for missed detection
self._states_ = self.states.copy()
self._weights_ = [self.weights*(1-detection_probability)]
# SLAM, step 1:
slam_info.exp_sum__pd_predwt = np.exp(-self.weights.sum())
# Split x and P out from the combined state vector
detected_indices = detection_probability > 0.1
detected_states = self.states[detected_indices]
x = detected_states.state
P = detected_states.covariance
# Scale the weights by detection probability
weights = self.weights[detected_indices]*detection_probability[detected_indices]
# SLAM, prep for step 2:
slam_info.sum__clutter_with_pd_updwt = np.zeros(num_observations)
if x.shape[0]:
# Part of the Kalman update is common to all observation-updates
x, P, kalman_info = kf_update(x, P,
np.array([self.parameters.obs_fn.parameters.H]),
np.array([self.parameters.obs_fn.parameters.R]),
None, INPLACE=True)#USE_NP=0)
# Container for the updated states
new_gmstate = self.states.__class__(0)
# Predicted observation from the current states
pred_z = featuredetector.tf.relative(self.parameters.obs_fn.parameters.parent_state_xyz,
self.parameters.obs_fn.parameters.parent_state_rpy,
x)
#print "PREDICTED Z:"
#print pred_z
# We need to update the states and find the updated weights
for (_observation_, obs_count) in zip(observation_set,
range(num_observations)):
#new_x = copy.deepcopy(x)
# Apply the Kalman update to get the new state - update in-place
# and return the residuals
new_x, residuals = kf_update_x(x, pred_z,
_observation_, kalman_info.kalman_gain,
INPLACE=False)
code.interact(local=locals())
# Calculate the weight of the Gaussians for this observation
# Calculate term in the exponent
x_pdf = np.exp(-0.5*np.power(
blas.dgemv(kalman_info.inv_sqrt_S, residuals), 2).sum(axis=1))/ \
np.sqrt(kalman_info.det_S*(2*np.pi)**z_dim)
new_weight = weights*x_pdf
# Normalise the weights
normalisation_factor = clutter_pdf[obs_count] + new_weight.sum()
new_weight /= normalisation_factor
# SLAM, step 2:
slam_info.sum__clutter_with_pd_updwt[obs_count] = \
normalisation_factor
# Create new state with new_x and P to add to _states_
new_gmstate.set(new_x, P)
self._states_.append(new_gmstate)
self._weights_ += [new_weight]
else:
slam_info.sum__clutter_with_pd_updwt = np.array(clutter_pdf)
self._weights_ = np.concatenate(self._weights_)
# SLAM, finalise:
slam_info.likelihood = (slam_info.exp_sum__pd_predwt *
slam_info.sum__clutter_with_pd_updwt.prod())
return slam_info
