def __init__(self, listen_to_vicon=False, vicon_channel=None, publish_to_lcm=False, use_rpydot=False, use_ekf=False, use_ukf=False, delay_comp=False): self._tvlqr_counting = False self._last_time_update = time.time() self._current_dt = 0.0 Thread(target=self._estimator_watchdog).start() self.q = [1.0, 0.0, 0.0, 0.0] # quaternion of sensor frame relative to auxiliary frame self.integralFB = [0.0, 0.0, 0.0] # integral error terms scaled by Ki self._last_rpy = [0.0, 0.0, 0.0] self._last_gyro = [0.0, 0.0, 0.0] self._last_acc = [0.0, 0.0, 0.0] self._last_imu_update = time.time() self._last_xyz = [0.0, 0.0, 0.0] self._last_xyz_raw = [0.0, 0.0, 0.0] self._last_dxyz = [0.0, 0.0, 0.0] self._last_vicon_rpy = [0.0, 0.0, 0.0] self._last_vicon_update = time.time() self._valid_vicon = False self._vicon_alpha_pos = .8 self._vicon_alpha_vel = .7 self._vicon_yaw = 0.0 self._use_rpydot = use_rpydot self._publish_to_lcm = publish_to_lcm if publish_to_lcm: self._xhat_lc = lcm.LCM() self._listen_to_vicon = listen_to_vicon self._vicon_channel = vicon_channel if listen_to_vicon: Thread(target=self._vicon_listener).start() self._input_log = list() self._last_input = [0.0, 0.0, 0.0, 0.0] self._last_input_time = time.time() self._delay_comp = delay_comp if delay_comp: self._cf_model = Crazyflie2() self._delay = 0.028 # delay in the control loop in seconds self._use_ekf = use_ekf self._use_ukf = use_ukf self._use_kalman = use_ekf or use_ukf if self._use_kalman: # states: x y z dx dy dz accxbias accybias acczbias # inputs: roll pitch yaw accx accy accz # measurements: x y z self._plant = DoubleIntegrator() self._last_kalman_update = time.time() if use_ekf: self._kalman = ExtendedKalmanFilter(dim_x=9, dim_z=3, plant=self._plant) elif use_ukf: self._kalman = UnscentedKalmanFilter(dim_x=9, dim_z=3, plant=self._plant)
class StateEstimator():
def __init__(self, listen_to_vicon=False, vicon_channel=None, publish_to_lcm=False,
use_rpydot=False, use_ekf=False, use_ukf=False,
delay_comp=False):
self._tvlqr_counting = False
self._last_time_update = time.time()
self._current_dt = 0.0
Thread(target=self._estimator_watchdog).start()
self.q = [1.0, 0.0, 0.0, 0.0] # quaternion of sensor frame relative to auxiliary frame
self.integralFB = [0.0, 0.0, 0.0] # integral error terms scaled by Ki
self._last_rpy = [0.0, 0.0, 0.0]
self._last_gyro = [0.0, 0.0, 0.0]
self._last_acc = [0.0, 0.0, 0.0]
self._last_imu_update = time.time()
self._last_xyz = [0.0, 0.0, 0.0]
self._last_xyz_raw = [0.0, 0.0, 0.0]
self._last_dxyz = [0.0, 0.0, 0.0]
self._last_vicon_rpy = [0.0, 0.0, 0.0]
self._last_vicon_update = time.time()
self._valid_vicon = False
self._vicon_alpha_pos = .8
self._vicon_alpha_vel = .7
self._vicon_yaw = 0.0
self._use_rpydot = use_rpydot
self._publish_to_lcm = publish_to_lcm
if publish_to_lcm:
self._xhat_lc = lcm.LCM()
self._listen_to_vicon = listen_to_vicon
self._vicon_channel = vicon_channel
if listen_to_vicon:
Thread(target=self._vicon_listener).start()
self._input_log = list()
self._last_input = [0.0, 0.0, 0.0, 0.0]
self._last_input_time = time.time()
self._delay_comp = delay_comp
if delay_comp:
self._cf_model = Crazyflie2()
self._delay = 0.028 # delay in the control loop in seconds
self._use_ekf = use_ekf
self._use_ukf = use_ukf
self._use_kalman = use_ekf or use_ukf
if self._use_kalman:
# states: x y z dx dy dz accxbias accybias acczbias
# inputs: roll pitch yaw accx accy accz
# measurements: x y z
self._plant = DoubleIntegrator()
self._last_kalman_update = time.time()
if use_ekf:
self._kalman = ExtendedKalmanFilter(dim_x=9, dim_z=3, plant=self._plant)
elif use_ukf:
self._kalman = UnscentedKalmanFilter(dim_x=9, dim_z=3, plant=self._plant)
def add_imu_reading(self, imu_reading):
(gx, gy, gz, ax, ay, az, dt_imu) = imu_reading
dt = time.time() - self._last_imu_update
new_quat = MahonyAHRS.MahonyAHRSupdateIMU(gx,gy,gz,ax,ay,az,dt,
self.q[0],self.q[1],self.q[2],self.q[3],
self.integralFB[0],self.integralFB[0],self.integralFB[0])
self._last_imu_update = time.time()
self.q = new_quat[0:4]
self.integralFB = new_quat[4:]
try:
self._last_rpy = quat2rpy(self.q)
if self._listen_to_vicon:
self._last_rpy[2] = self._vicon_yaw
except ValueError:
pass
self._last_gyro = [gx,gy,gz]
self._last_acc = [ax,ay,az]
def add_input(self, input_sent):
last_dt = time.time()-self._last_input_time
self._input_log.append([self._last_input,last_dt])
if len(self._input_log)>20:
self._input_log = self._input_log[10:]
self._last_input = input_sent
self._last_input_time = time.time()
def get_last_inputs(self, tspan):
control_inputs = list()
log = list(self._input_log)
dt = 0.0
for entry in log:
dt += entry[1]
if dt>tspan:
break
control_inputs.insert(0,entry)
if len(control_inputs)<1:
control_inputs = [[[0.0, 0.0, 0.0, 0.0],tspan]]
return control_inputs
def _vicon_listener(self):
_vicon_listener_lc = lcm.LCM()
_vicon_listener_lc.subscribe(self._vicon_channel,self._add_vicon_reading)
while True:
_vicon_listener_lc.handle()
def _add_vicon_reading(self, channel, data):
msg = vicon_pos_t.decode(data)
if msg.q[0] < -999:
self._valid_vicon = False
#self._last_dxyz = [0.0, 0.0, 0.0]
return
# put any filtering on yaw here if needed
self._vicon_yaw = self._vicon_alpha_pos*msg.q[5]+(1-self._vicon_alpha_pos)*self._vicon_yaw
xyz = list(msg.q)[0:3]
dxyz = [0.0, 0.0, 0.0]
if self._valid_vicon:
dt = 1.0/120.0
dt_measured = (msg.timestamp-self._last_vicon_update)/1000.0
if (dt_measured>1.5*dt):
dt = dt_measured
dxyz[0] = (1.0/dt)*(xyz[0]-self._last_xyz_raw[0])
dxyz[1] = (1.0/dt)*(xyz[1]-self._last_xyz_raw[1])
dxyz[2] = (1.0/dt)*(xyz[2]-self._last_xyz_raw[2])
self._last_xyz_raw = list(xyz)
self._last_xyz[0] = self._vicon_alpha_pos*xyz[0]+(1-self._vicon_alpha_pos)*self._last_xyz[0]
self._last_xyz[1] = self._vicon_alpha_pos*xyz[1]+(1-self._vicon_alpha_pos)*self._last_xyz[1]
self._last_xyz[2] = self._vicon_alpha_pos*xyz[2]+(1-self._vicon_alpha_pos)*self._last_xyz[2]
self._last_dxyz[0] = self._vicon_alpha_vel*dxyz[0]+(1-self._vicon_alpha_vel)*self._last_dxyz[0]
self._last_dxyz[1] = self._vicon_alpha_vel*dxyz[1]+(1-self._vicon_alpha_vel)*self._last_dxyz[1]
self._last_dxyz[2] = self._vicon_alpha_vel*dxyz[2]+(1-self._vicon_alpha_vel)*self._last_dxyz[2]
self._last_vicon_update = msg.timestamp
self._valid_vicon = True
def get_xhat(self):
if self._use_kalman:
dt = time.time() - self._last_kalman_update
self._kalman.predict(np.array(self._last_rpy + self._last_acc), dt)
if self._valid_vicon:
self._kalman.update(np.array(self._last_xyz))
self._last_kalman_update = time.time()
kalman_xhat = self._kalman.x.reshape(9).tolist()
xhat = [kalman_xhat[0],kalman_xhat[1],kalman_xhat[2],
self._last_rpy[0],self._last_rpy[1],self._last_rpy[2],
kalman_xhat[3],kalman_xhat[4],kalman_xhat[5],
self._last_gyro[0],self._last_gyro[1],self._last_gyro[2]]
# msg = dxyz_compare_t()
# msg.dxyzraw = self._last_dxyz
# msg.dxyzfiltered = kalman_xhat[3:6]
# self._xhat_lc.publish('dxyz_compare', msg.encode())
msg = kalman_args_t()
msg.input_rpy = self._last_rpy
msg.input_acc = self._last_acc
msg.input_dt = dt
msg.valid_vicon = self._valid_vicon
msg.meas_xyz = self._last_xyz
msg.smooth_xyz = self._last_xyz
msg.smooth_dxyz =self._last_dxyz
self._xhat_lc.publish('kalman_args', msg.encode())
else:
xhat = [self._last_xyz[0],self._last_xyz[1],self._last_xyz[2],
self._last_rpy[0],self._last_rpy[1],self._last_rpy[2],
self._last_dxyz[0],self._last_dxyz[1],self._last_dxyz[2],
self._last_gyro[0],self._last_gyro[1],self._last_gyro[2]]
if self._delay_comp:
control_inputs = self.get_last_inputs(self._delay)
for ci in control_inputs:
xhat = self._cf_model.simulate(xhat,ci[0],ci[1])
if self._use_rpydot:
try:
xhat[9:12] = angularvel2rpydot(self._last_rpy, body2world(self._last_rpy, self._last_gyro))
except ValueError:
xhat[9:12] = [0.0, 0.0, 0.0]
if self._publish_to_lcm:
msg = crazyflie_state_estimate_t()
msg.xhat = xhat
msg.t = self.get_time()
self._xhat_lc.publish("crazyflie_state_estimate", msg.encode())
# uncomment the following if you want to trigger hover at a predefined state
# if xhat[2] <= 0.15:
# print('This is how low')
# msg = crazyflie_controller_commands_t()
# msg.is_running = False
# self._xhat_lc.publish('crazyflie_controller_commands', msg.encode())
# msg = crazyflie_hover_commands_t()
# msg.hover = True
# self._xhat_lc.publish('crazyflie_hover_commands',msg.encode())
return xhat
def get_time(self):
if self._tvlqr_counting:
self._current_dt += (time.time()-self._last_time_update)
self._last_time_update = time.time()
# uncomment the following if you want to trigger hover at a predefined time
# if 1.1-xhat[0] <= 0.01:
# msg = crazyflie_hover_commands_t()
# msg.hover = True
# self._xhat_lc.publish('crazyflie_hover_commands',msg.encode())
return self._current_dt
def _estimator_watchdog(self):
_watchdog_lc = lcm.LCM()
_watchdog_lc.subscribe('crazyflie_state_estimator_commands',self._estimator_watchdog_update)
while True:
_watchdog_lc.handle()
def _estimator_watchdog_update(self, channel, data):
msg = crazyflie_state_estimator_commands_t.decode(data)
self._tvlqr_counting = msg.tvlqr_counting
def read_traceXY(self, file, type, verb, plot, psum, init_x, init_y):
with open(file) as f:
op = None
s = f.readlines()
i = 0
read_num = False
read_locs = False
read_uncertainties = 0
num = 0
init_xy = [[0, 0], [0, 0]]
static_xy = [[0, 0], [0, 0]]
m_var = [0, 0]
a_var = [0, 0]
d_var = [0, 0]
h_var = [0, 0]
self.r0_est_pts_x = []
self.r0_est_pts_y = []
self.r0_gt_pts_x = []
self.r0_gt_pts_y = []
self.r1_est_pts_x = []
self.r1_est_pts_y = []
self.r1_gt_pts_x = []
self.r1_gt_pts_y = []
self.r0_sim_x = []
self.r0_sim_y = []
self.r1_sim_x = []
self.r1_sim_y = []
self.err0 = 0
self.err0_count = 0
self.err0_final = 0
self.err1 = 0
self.err1_count = 0
self.err1_final = 0
while (i < len(s)):
if (len(s[i]) <= 1 or s[i][0] == '#'):
i = i + 1
continue
if (not (read_num)):
num = int(s[i].split()[0])
read_num = True
i = i + 1
continue
if (not (read_locs)):
x = s[i].split()
x0 = float(x[0])
y0 = float(x[1])
x = s[i + 1].split()
x1 = float(x[0])
y1 = float(x[1])
init_xy = [[x0, y0], [init_x, init_y]]
static_xy = [[x0, y0], [init_x, init_y]]
self.r0_gt_pts_x.extend([x0])
self.r0_gt_pts_y.extend([y0])
self.r0_est_pts_x.extend([x0])
self.r0_est_pts_y.extend([y0])
self.r1_gt_pts_x.extend([x1])
self.r1_gt_pts_y.extend([y1])
self.r1_est_pts_x.extend([init_x])
self.r1_est_pts_y.extend([init_y])
self.r0_sim_x.extend([[], x0])
self.r0_sim_y.extend([[], y0])
self.r1_sim_x.extend([[], x1])
self.r1_sim_y.extend([[], y1])
read_locs = True
i = i + 2
continue
if (read_uncertainties < self.NUM_UNCERTAINTIES):
x = s[i].split()
if (x[0] == 'O'):
m_var = [float(x[1]), float(x[2])]
elif (x[0] == 'A'):
a_var = [
math.radians(math.sqrt(float(x[1])))**2,
math.radians(math.sqrt(float(x[2])))**2
]
elif (x[0] == 'D'):
d_var = [float(x[1]), float(x[2])]
elif (x[0] == 'H'):
h_var = [
math.radians(math.sqrt(float(x[1])))**2,
math.radians(math.sqrt(float(x[2])))**2
]
read_uncertainties = read_uncertainties + 1
i = i + 1
continue
if (read_num and read_uncertainties == self.NUM_UNCERTAINTIES
and read_locs):
break
if (type == 0):
op = OdometryLocalize(init_xy, m_var, a_var, d_var, h_var)
elif (type == 1):
op = ExtendedKalmanFilter(init_xy, m_var, a_var, d_var, h_var)
#elif(type == 2):
# op = GridLocalize(init_xy, m_var, a_var, d_var, h_var)
elif (type == 3):
op = UnscentedKalmanFilter(init_xy, m_var, a_var, d_var, h_var)
elif (type == 4):
op = ParticleLocalize(init_xy, m_var, a_var, d_var, h_var)
# elif(type == 5):
# op = ExtendedKalmanFilterWPH(init_xy, m_var, a_var, d_var, h_var)
else:
print "Unknown filter type"
sys.exit()
counter = 0
while (i < len(s)):
line = s[i]
if (len(line) <= 1 or line[0] == '#'):
i = i + 1
continue
x = line.split()
if x[0] == 'M':
d0 = float(x[1])
h0 = math.radians(float(x[2]))
d1 = float(x[3])
h1 = math.radians(float(x[4]))
op.measure_movement([d0, d1], [h0, h1])
(x0, y0) = op.get_possible_pose_estimates(0)
(x1, y1) = op.get_possible_pose_estimates(1)
self.r0_sim_x.extend([x0])
self.r0_sim_y.extend([y0])
self.r1_sim_x.extend([x1])
self.r1_sim_y.extend([y1])
elif x[0] == 'D':
d0 = float(x[1])
d1 = float(x[2])
ph0 = math.radians(float(x[3]))
ph1 = math.radians(float(x[4]))
op.measure_distance([d0, d1], [ph0, ph1])
(x0, y0) = op.get_pose_estimate(0)
(x1, y1) = op.get_pose_estimate(1)
self.r0_sim_x.extend([x0])
self.r0_sim_y.extend([y0])
self.r1_sim_x.extend([x1])
self.r1_sim_y.extend([y1])
elif x[0] == 'C':
(x0, y0) = (float(x[1]), float(x[2]))
(x1, y1) = (float(x[3]), float(x[4]))
(pred_x0, pred_y0) = op.get_pose_estimate(0)
(pred_x1, pred_y1) = op.get_pose_estimate(1)
if verb or (i >= len(s) - 7 and psum):
print "Rover 0 abs. pose %d: (%f, %f)" % (counter, x0,
y0)
print "Rover 0 est. pose %d: (%f, %f)" % (
counter, pred_x0, pred_y0)
pd0 = math.sqrt((pred_x0 - x0)**2 + (pred_y0 - y0)**2)
d0 = math.sqrt((x0 - static_xy[0][0])**2 +
(y0 - static_xy[0][1])**2)
self.err0_count += 1
self.err0_final = (100.0 * (abs(pd0) / d0))
self.err0 += self.err0_final
if verb or (i >= len(s) - 7 and psum):
print "Rover 0 error: %f%%" % (100.0 * (abs(pd0) / d0))
self.r0_gt_pts_x.extend([x0])
self.r0_gt_pts_y.extend([y0])
self.r0_est_pts_x.extend([pred_x0])
self.r0_est_pts_y.extend([pred_y0])
if verb or (i >= len(s) - 7 and psum):
print "Rover 1 abs. pose %d: (%f, %f)" % (counter, x1,
y1)
print "Rover 1 est. pose %d: (%f, %f)" % (
counter, pred_x1, pred_y1)
pd1 = math.sqrt((pred_x1 - x1)**2 + (pred_y1 - y1)**2)
d1 = math.sqrt((x1 - static_xy[1][0])**2 +
(y1 - static_xy[1][1])**2)
self.err1_count += 1
self.err1_final = (100.0 * (abs(pd1) / d1))
self.err1 += self.err1_final
if verb or (i >= len(s) - 7 and psum):
print "Rover 1 error: %f%%" % (100.0 * (abs(pd1) / d1))
print "\n"
self.r1_gt_pts_x.extend([x1])
self.r1_gt_pts_y.extend([y1])
self.r1_est_pts_x.extend([pred_x1])
self.r1_est_pts_y.extend([pred_y1])
counter = counter + 1
else:
print "Invalid trace file"
i = i + 1
if verb:
print self.r0_gt_pts_x
print self.r0_gt_pts_y
if plot:
# self.plot_cont(len(self.r0_sim_x) / 2, 500.0, self.r0_sim_x, self.r0_sim_y, self.r0_gt_pts_x, self.r0_gt_pts_y, self.r1_sim_x, self.r1_sim_y, self.r1_gt_pts_x, self.r1_gt_pts_y)
self.plot_points(self.r0_gt_pts_x, self.r0_gt_pts_y,
self.r0_est_pts_x, self.r0_est_pts_y,
self.r1_gt_pts_x, self.r1_gt_pts_y,
self.r1_est_pts_x, self.r1_est_pts_y)
