def residual_z_(z, zp):
"""
z : observation, [r, alpha]
zp : predicted observation
"""
r_z = z - zp
r_z[1] = util.wrap_around(r_z[1])
return r_z
def residual_x_(x, xp):
"""
x : state, [x, y, theta]
xp : predicted state
"""
if dim == 3 or dim == 5:
r_x = x - xp
r_x[2] = util.wrap_around(r_x[2])
return r_x
else:
return None
def SE2Dynamics(x, dt, u):
assert (len(x) == 3)
tw = dt * u[1]
# Update the agent state
if abs(tw) < 0.001:
diff = np.array([
dt * u[0] * np.cos(x[2] + tw / 2),
dt * u[0] * np.sin(x[2] + tw / 2), tw
])
else:
diff = np.array([
u[0] / u[1] * (np.sin(x[2] + tw) - np.sin(x[2])),
u[0] / u[1] * (np.cos(x[2]) - np.cos(x[2] + tw)), tw
])
new_x = x + diff
new_x[2] = util.wrap_around(new_x[2])
return new_x
def gen_rand_pose(self, o_xy, c_theta, min_lin_dist, max_lin_dist, min_ang_dist, max_ang_dist):
"""Generates random position and yaw.
Parameters
--------
o_xy : xy position of a point in the global frame which we compute a distance from.
c_theta : angular position of a point in the global frame which we compute an angular distance from.
min_lin_dist : the minimum linear distance from o_xy to a sample point.
max_lin_dist : the maximum linear distance from o_xy to a sample point.
min_ang_dist : the minimum angular distance (counter clockwise direction) from c_theta to a sample point.
max_ang_dist : the maximum angular distance (counter clockwise direction) from c_theta to a sample point.
"""
if max_ang_dist < min_ang_dist:
max_ang_dist += 2*np.pi
rand_ang = util.wrap_around(np.random.rand() * \
(max_ang_dist - min_ang_dist) + min_ang_dist + c_theta)
rand_r = np.random.rand() * (max_lin_dist - min_lin_dist) + min_lin_dist
rand_xy = np.array([rand_r*np.cos(rand_ang), rand_r*np.sin(rand_ang)]) + o_xy
is_valid = not(map_utils.is_collision(self.MAP, rand_xy))
return is_valid, [rand_xy[0], rand_xy[1], rand_ang]
def update(self, z_t, x_t):
"""
Parameters
--------
z_t : observation - radial and angular distances from the agent.
x_t : agent state (x, y, orientation) in the global frame.
"""
# Kalman Filter Update
r_pred, alpha_pred = util.relative_distance_polar(
self.state[:2], x_t[:2], x_t[2])
diff_pred = self.state[:2] - x_t[:2]
if self.dim == 2:
Hmat = np.array([[diff_pred[0], diff_pred[1]],
[-diff_pred[1] / r_pred, diff_pred[0] / r_pred]
]) / r_pred
elif self.dim == 4:
Hmat = np.array([
[diff_pred[0], diff_pred[1], 0.0, 0.0],
[-diff_pred[1] / r_pred, diff_pred[0] / r_pred, 0.0, 0.0]
]) / r_pred
else:
raise ValueError('target dimension for KF must be either 2 or 4')
innov = z_t - np.array([r_pred, alpha_pred])
innov[1] = util.wrap_around(innov[1])
R = np.matmul(np.matmul(Hmat, self.cov), Hmat.T) \
+ self.obs_noise_func((r_pred, alpha_pred))
K = np.matmul(np.matmul(self.cov, Hmat.T), LA.inv(R))
C = np.eye(self.dim) - np.matmul(K, Hmat)
cov_new = np.matmul(C, self.cov)
state_new = self.state + np.matmul(K, innov)
if True: #LA.det(cov_new) < 1e6:
self.cov = cov_new
self.state = np.clip(state_new, self.limit[0], self.limit[1])