def __init__(self, disabled_logs=[], dog=None):
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .2 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
def __init__(self, disabled_logs=[], dog=None):
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .2 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
self.posenet_invalid_count = 0
self.posenet_speed = 0
self.car_speed = 0
self.converter = coord.LocalCoord.from_ecef(self.kf.x[States.ECEF_POS])
self.unix_timestamp_millis = 0
def __init__(self, disabled_logs=None, dog=None):
if disabled_logs is None:
disabled_logs = []
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .1 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
self.posenet_invalid_count = 0
self.posenet_speed = 0
self.car_speed = 0
self.posenet_stds = 10 * np.ones((POSENET_STD_HIST))
self.converter = coord.LocalCoord.from_ecef(self.kf.x[States.ECEF_POS])
self.unix_timestamp_millis = 0
self.last_gps_fix = 0
class Localizer():
def __init__(self, disabled_logs=None, dog=None):
if disabled_logs is None:
disabled_logs = []
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .1 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
self.posenet_invalid_count = 0
self.posenet_speed = 0
self.car_speed = 0
self.posenet_stds = 10 * np.ones((POSENET_STD_HIST))
self.converter = coord.LocalCoord.from_ecef(self.kf.x[States.ECEF_POS])
self.unix_timestamp_millis = 0
self.last_gps_fix = 0
self.device_fell = False
@staticmethod
def msg_from_state(converter, calib_from_device, H, predicted_state,
predicted_cov):
predicted_std = np.sqrt(np.diagonal(predicted_cov))
fix_ecef = predicted_state[States.ECEF_POS]
fix_ecef_std = predicted_std[States.ECEF_POS_ERR]
vel_ecef = predicted_state[States.ECEF_VELOCITY]
vel_ecef_std = predicted_std[States.ECEF_VELOCITY_ERR]
fix_pos_geo = coord.ecef2geodetic(fix_ecef)
#fix_pos_geo_std = np.abs(coord.ecef2geodetic(fix_ecef + fix_ecef_std) - fix_pos_geo)
orientation_ecef = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION])
orientation_ecef_std = predicted_std[States.ECEF_ORIENTATION_ERR]
device_from_ecef = rot_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
calibrated_orientation_ecef = euler_from_rot(
calib_from_device.dot(device_from_ecef))
acc_calib = calib_from_device.dot(predicted_state[States.ACCELERATION])
acc_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ACCELERATION_ERR,
States.ACCELERATION_ERR]).dot(
calib_from_device.T)))
ang_vel_calib = calib_from_device.dot(
predicted_state[States.ANGULAR_VELOCITY])
ang_vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ANGULAR_VELOCITY_ERR,
States.ANGULAR_VELOCITY_ERR]).dot(
calib_from_device.T)))
vel_device = device_from_ecef.dot(vel_ecef)
device_from_ecef_eul = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
idxs = list(range(States.ECEF_ORIENTATION_ERR.start, States.ECEF_ORIENTATION_ERR.stop)) + \
list(range(States.ECEF_VELOCITY_ERR.start, States.ECEF_VELOCITY_ERR.stop))
condensed_cov = predicted_cov[idxs][:, idxs]
HH = H(*list(np.concatenate([device_from_ecef_eul, vel_ecef])))
vel_device_cov = HH.dot(condensed_cov).dot(HH.T)
vel_device_std = np.sqrt(np.diagonal(vel_device_cov))
vel_calib = calib_from_device.dot(vel_device)
vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(vel_device_cov).dot(
calib_from_device.T)))
orientation_ned = ned_euler_from_ecef(fix_ecef, orientation_ecef)
#orientation_ned_std = ned_euler_from_ecef(fix_ecef, orientation_ecef + orientation_ecef_std) - orientation_ned
ned_vel = converter.ecef2ned(fix_ecef +
vel_ecef) - converter.ecef2ned(fix_ecef)
#ned_vel_std = self.converter.ecef2ned(fix_ecef + vel_ecef + vel_ecef_std) - self.converter.ecef2ned(fix_ecef + vel_ecef)
fix = messaging.log.LiveLocationKalman.new_message()
# write measurements to msg
measurements = [
# measurement field, value, std, valid
(fix.positionGeodetic, fix_pos_geo, np.nan * np.zeros(3), True),
(fix.positionECEF, fix_ecef, fix_ecef_std, True),
(fix.velocityECEF, vel_ecef, vel_ecef_std, True),
(fix.velocityNED, ned_vel, np.nan * np.zeros(3), True),
(fix.velocityDevice, vel_device, vel_device_std, True),
(fix.accelerationDevice, predicted_state[States.ACCELERATION],
predicted_std[States.ACCELERATION_ERR], True),
(fix.orientationECEF, orientation_ecef, orientation_ecef_std,
True),
(fix.calibratedOrientationECEF, calibrated_orientation_ecef,
np.nan * np.zeros(3), True),
(fix.orientationNED, orientation_ned, np.nan * np.zeros(3), True),
(fix.angularVelocityDevice,
predicted_state[States.ANGULAR_VELOCITY],
predicted_std[States.ANGULAR_VELOCITY_ERR], True),
(fix.velocityCalibrated, vel_calib, vel_calib_std, True),
(fix.angularVelocityCalibrated, ang_vel_calib, ang_vel_calib_std,
True),
(fix.accelerationCalibrated, acc_calib, acc_calib_std, True),
]
for field, value, std, valid in measurements:
# TODO: can we write the lists faster?
field.value = to_float(value)
field.std = to_float(std)
field.valid = valid
return fix
def liveLocationMsg(self):
fix = self.msg_from_state(self.converter, self.calib_from_device,
self.H, self.kf.x, self.kf.P)
# experimentally found these values, no false positives in 20k minutes of driving
old_mean, new_mean = np.mean(
self.posenet_stds[:POSENET_STD_HIST // 2]), np.mean(
self.posenet_stds[POSENET_STD_HIST // 2:])
std_spike = new_mean / old_mean > 4 and new_mean > 7
fix.posenetOK = not (std_spike and self.car_speed > 5)
fix.deviceStable = not self.device_fell
self.device_fell = False
#fix.gpsWeek = self.time.week
#fix.gpsTimeOfWeek = self.time.tow
fix.unixTimestampMillis = self.unix_timestamp_millis
if np.linalg.norm(fix.positionECEF.std) < 50 and self.calibrated:
fix.status = 'valid'
elif np.linalg.norm(fix.positionECEF.std) < 50:
fix.status = 'uncalibrated'
else:
fix.status = 'uninitialized'
return fix
def update_kalman(self, time, kind, meas, R=None):
try:
self.kf.predict_and_observe(time, kind, meas, R)
except KalmanError:
cloudlog.error("Error in predict and observe, kalman reset")
self.reset_kalman()
def handle_gps(self, current_time, log):
# ignore the message if the fix is invalid
if log.flags % 2 == 0:
return
self.last_gps_fix = current_time
self.converter = coord.LocalCoord.from_geodetic(
[log.latitude, log.longitude, log.altitude])
ecef_pos = self.converter.ned2ecef([0, 0, 0])
ecef_vel = self.converter.ned2ecef(np.array(log.vNED)) - ecef_pos
ecef_pos_R = np.diag([(3 * log.verticalAccuracy)**2] * 3)
ecef_vel_R = np.diag([(log.speedAccuracy)**2] * 3)
#self.time = GPSTime.from_datetime(datetime.utcfromtimestamp(log.timestamp*1e-3))
self.unix_timestamp_millis = log.timestamp
gps_est_error = np.sqrt((self.kf.x[0] - ecef_pos[0])**2 +
(self.kf.x[1] - ecef_pos[1])**2 +
(self.kf.x[2] - ecef_pos[2])**2)
orientation_ecef = euler_from_quat(self.kf.x[States.ECEF_ORIENTATION])
orientation_ned = ned_euler_from_ecef(ecef_pos, orientation_ecef)
orientation_ned_gps = np.array([0, 0, np.radians(log.bearing)])
orientation_error = np.mod(
orientation_ned - orientation_ned_gps - np.pi, 2 * np.pi) - np.pi
initial_pose_ecef_quat = quat_from_euler(
ecef_euler_from_ned(ecef_pos, orientation_ned_gps))
if np.linalg.norm(ecef_vel) > 5 and np.linalg.norm(
orientation_error) > 1:
cloudlog.error(
"Locationd vs ubloxLocation orientation difference too large, kalman reset"
)
self.reset_kalman(init_pos=ecef_pos,
init_orient=initial_pose_ecef_quat)
self.update_kalman(current_time,
ObservationKind.ECEF_ORIENTATION_FROM_GPS,
initial_pose_ecef_quat)
elif gps_est_error > 50:
cloudlog.error(
"Locationd vs ubloxLocation position difference too large, kalman reset"
)
self.reset_kalman(init_pos=ecef_pos,
init_orient=initial_pose_ecef_quat)
self.update_kalman(current_time,
ObservationKind.ECEF_POS,
ecef_pos,
R=ecef_pos_R)
self.update_kalman(current_time,
ObservationKind.ECEF_VEL,
ecef_vel,
R=ecef_vel_R)
def handle_car_state(self, current_time, log):
self.speed_counter += 1
if self.speed_counter % SENSOR_DECIMATION == 0:
self.update_kalman(current_time, ObservationKind.ODOMETRIC_SPEED,
[log.vEgo])
self.car_speed = abs(log.vEgo)
if log.vEgo == 0:
self.update_kalman(current_time, ObservationKind.NO_ROT,
[0, 0, 0])
def handle_cam_odo(self, current_time, log):
self.cam_counter += 1
if self.cam_counter % VISION_DECIMATION == 0:
rot_device = self.device_from_calib.dot(log.rot)
rot_device_std = self.device_from_calib.dot(log.rotStd)
self.update_kalman(
current_time, ObservationKind.CAMERA_ODO_ROTATION,
np.concatenate([rot_device, 10 * rot_device_std]))
trans_device = self.device_from_calib.dot(log.trans)
trans_device_std = self.device_from_calib.dot(log.transStd)
self.posenet_speed = np.linalg.norm(trans_device)
self.posenet_stds[:-1] = self.posenet_stds[1:]
self.posenet_stds[-1] = trans_device_std[0]
self.update_kalman(
current_time, ObservationKind.CAMERA_ODO_TRANSLATION,
np.concatenate([trans_device, 10 * trans_device_std]))
def handle_sensors(self, current_time, log):
# TODO does not yet account for double sensor readings in the log
for sensor_reading in log:
# TODO: handle messages from two IMUs at the same time
if sensor_reading.source == SensorSource.lsm6ds3:
continue
# Gyro Uncalibrated
if sensor_reading.sensor == 5 and sensor_reading.type == 16:
self.gyro_counter += 1
if self.gyro_counter % SENSOR_DECIMATION == 0:
v = sensor_reading.gyroUncalibrated.v
self.update_kalman(current_time,
ObservationKind.PHONE_GYRO,
[-v[2], -v[1], -v[0]])
# Accelerometer
if sensor_reading.sensor == 1 and sensor_reading.type == 1:
# check if device fell, estimate 10 for g
# 40m/s**2 is a good filter for falling detection, no false positives in 20k minutes of driving
self.device_fell = self.device_fell or (np.linalg.norm(
np.array(sensor_reading.acceleration.v) -
np.array([10, 0, 0])) > 40)
self.acc_counter += 1
if self.acc_counter % SENSOR_DECIMATION == 0:
v = sensor_reading.acceleration.v
self.update_kalman(current_time,
ObservationKind.PHONE_ACCEL,
[-v[2], -v[1], -v[0]])
def handle_live_calib(self, current_time, log):
if len(log.rpyCalib):
self.calib = log.rpyCalib
self.device_from_calib = rot_from_euler(self.calib)
self.calib_from_device = self.device_from_calib.T
self.calibrated = log.calStatus == 1
def reset_kalman(self, current_time=None, init_orient=None, init_pos=None):
self.filter_time = current_time
init_x = LiveKalman.initial_x.copy()
# too nonlinear to init on completely wrong
if init_orient is not None:
init_x[3:7] = init_orient
if init_pos is not None:
init_x[:3] = init_pos
self.kf.init_state(init_x,
covs=np.diag(LiveKalman.initial_P_diag),
filter_time=current_time)
self.observation_buffer = []
self.gyro_counter = 0
self.acc_counter = 0
self.speed_counter = 0
self.cam_counter = 0
class Localizer():
def __init__(self, disabled_logs=[], dog=None):
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .2 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
self.posenet_invalid_count = 0
self.posenet_speed = 0
self.car_speed = 0
self.converter = coord.LocalCoord.from_ecef(self.kf.x[States.ECEF_POS])
self.unix_timestamp_millis = 0
self.last_gps_fix = 0
@staticmethod
def msg_from_state(converter, calib_from_device, H, predicted_state,
predicted_cov):
predicted_std = np.sqrt(np.diagonal(predicted_cov))
fix_ecef = predicted_state[States.ECEF_POS]
fix_ecef_std = predicted_std[States.ECEF_POS_ERR]
vel_ecef = predicted_state[States.ECEF_VELOCITY]
vel_ecef_std = predicted_std[States.ECEF_VELOCITY_ERR]
fix_pos_geo = coord.ecef2geodetic(fix_ecef)
#fix_pos_geo_std = np.abs(coord.ecef2geodetic(fix_ecef + fix_ecef_std) - fix_pos_geo)
orientation_ecef = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION])
orientation_ecef_std = predicted_std[States.ECEF_ORIENTATION_ERR]
acc_calib = calib_from_device.dot(predicted_state[States.ACCELERATION])
acc_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ACCELERATION_ERR,
States.ACCELERATION_ERR]).dot(
calib_from_device.T)))
ang_vel_calib = calib_from_device.dot(
predicted_state[States.ANGULAR_VELOCITY])
ang_vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ANGULAR_VELOCITY_ERR,
States.ANGULAR_VELOCITY_ERR]).dot(
calib_from_device.T)))
device_from_ecef = rot_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
vel_device = device_from_ecef.dot(vel_ecef)
device_from_ecef_eul = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
idxs = list(
range(States.ECEF_ORIENTATION_ERR.start,
States.ECEF_ORIENTATION_ERR.stop)) + list(
range(States.ECEF_VELOCITY_ERR.start,
States.ECEF_VELOCITY_ERR.stop))
condensed_cov = predicted_cov[idxs][:, idxs]
HH = H(*list(np.concatenate([device_from_ecef_eul, vel_ecef])))
vel_device_cov = HH.dot(condensed_cov).dot(HH.T)
vel_device_std = np.sqrt(np.diagonal(vel_device_cov))
vel_calib = calib_from_device.dot(vel_device)
vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(vel_device_cov).dot(
calib_from_device.T)))
orientation_ned = ned_euler_from_ecef(fix_ecef, orientation_ecef)
#orientation_ned_std = ned_euler_from_ecef(fix_ecef, orientation_ecef + orientation_ecef_std) - orientation_ned
ned_vel = converter.ecef2ned(fix_ecef +
vel_ecef) - converter.ecef2ned(fix_ecef)
#ned_vel_std = self.converter.ecef2ned(fix_ecef + vel_ecef + vel_ecef_std) - self.converter.ecef2ned(fix_ecef + vel_ecef)
fix = messaging.log.LiveLocationKalman.new_message()
fix.positionGeodetic.value = to_float(fix_pos_geo)
#fix.positionGeodetic.std = to_float(fix_pos_geo_std)
#fix.positionGeodetic.valid = True
fix.positionECEF.value = to_float(fix_ecef)
fix.positionECEF.std = to_float(fix_ecef_std)
fix.positionECEF.valid = True
fix.velocityECEF.value = to_float(vel_ecef)
fix.velocityECEF.std = to_float(vel_ecef_std)
fix.velocityECEF.valid = True
fix.velocityNED.value = to_float(ned_vel)
#fix.velocityNED.std = to_float(ned_vel_std)
#fix.velocityNED.valid = True
fix.velocityDevice.value = to_float(vel_device)
fix.velocityDevice.std = to_float(vel_device_std)
fix.velocityDevice.valid = True
fix.accelerationDevice.value = to_float(
predicted_state[States.ACCELERATION])
fix.accelerationDevice.std = to_float(
predicted_std[States.ACCELERATION_ERR])
fix.accelerationDevice.valid = True
fix.orientationECEF.value = to_float(orientation_ecef)
fix.orientationECEF.std = to_float(orientation_ecef_std)
fix.orientationECEF.valid = True
fix.orientationNED.value = to_float(orientation_ned)
#fix.orientationNED.std = to_float(orientation_ned_std)
#fix.orientationNED.valid = True
fix.angularVelocityDevice.value = to_float(
predicted_state[States.ANGULAR_VELOCITY])
fix.angularVelocityDevice.std = to_float(
predicted_std[States.ANGULAR_VELOCITY_ERR])
fix.angularVelocityDevice.valid = True
fix.velocityCalibrated.value = to_float(vel_calib)
fix.velocityCalibrated.std = to_float(vel_calib_std)
fix.velocityCalibrated.valid = True
fix.angularVelocityCalibrated.value = to_float(ang_vel_calib)
fix.angularVelocityCalibrated.std = to_float(ang_vel_calib_std)
fix.angularVelocityCalibrated.valid = True
fix.accelerationCalibrated.value = to_float(acc_calib)
fix.accelerationCalibrated.std = to_float(acc_calib_std)
fix.accelerationCalibrated.valid = True
return fix
def liveLocationMsg(self, time):
fix = self.msg_from_state(self.converter, self.calib_from_device,
self.H, self.kf.x, self.kf.P)
if abs(self.posenet_speed - self.car_speed) > max(
0.4 * self.car_speed, 5.0):
self.posenet_invalid_count += 1
else:
self.posenet_invalid_count = 0
fix.posenetOK = self.posenet_invalid_count < 4
#fix.gpsWeek = self.time.week
#fix.gpsTimeOfWeek = self.time.tow
fix.unixTimestampMillis = self.unix_timestamp_millis
if np.linalg.norm(fix.positionECEF.std) < 50 and self.calibrated:
fix.status = 'valid'
elif np.linalg.norm(fix.positionECEF.std) < 50:
fix.status = 'uncalibrated'
else:
fix.status = 'uninitialized'
return fix
def update_kalman(self, time, kind, meas, R=None):
try:
self.kf.predict_and_observe(time, kind, meas, R=R)
except KalmanError:
cloudlog.error("Error in predict and observe, kalman reset")
self.reset_kalman()
#idx = bisect_right([x[0] for x in self.observation_buffer], time)
#self.observation_buffer.insert(idx, (time, kind, meas))
#while len(self.observation_buffer) > 0 and self.observation_buffer[-1][0] - self.observation_buffer[0][0] > self.max_age:
# else:
# self.observation_buffer.pop(0)
def handle_gps(self, current_time, log):
# ignore the message if the fix is invalid
if log.flags % 2 == 0:
return
self.last_gps_fix = current_time
self.converter = coord.LocalCoord.from_geodetic(
[log.latitude, log.longitude, log.altitude])
ecef_pos = self.converter.ned2ecef([0, 0, 0])
ecef_vel = self.converter.ned2ecef_matrix.dot(np.array(log.vNED))
ecef_pos_R = np.diag([(3 * log.verticalAccuracy)**2] * 3)
ecef_vel_R = np.diag([(log.speedAccuracy)**2] * 3)
#self.time = GPSTime.from_datetime(datetime.utcfromtimestamp(log.timestamp*1e-3))
self.unix_timestamp_millis = log.timestamp
gps_est_error = np.sqrt((self.kf.x[0] - ecef_pos[0])**2 +
(self.kf.x[1] - ecef_pos[1])**2 +
(self.kf.x[2] - ecef_pos[2])**2)
orientation_ecef = euler_from_quat(self.kf.x[States.ECEF_ORIENTATION])
orientation_ned = ned_euler_from_ecef(ecef_pos, orientation_ecef)
orientation_ned_gps = np.array([0, 0, np.radians(log.bearing)])
orientation_error = np.mod(
orientation_ned - orientation_ned_gps - np.pi, 2 * np.pi) - np.pi
if np.linalg.norm(ecef_vel) > 5 and np.linalg.norm(
orientation_error) > 1:
cloudlog.error(
"Locationd vs ubloxLocation orientation difference too large, kalman reset"
)
initial_pose_ecef_quat = quat_from_euler(
ecef_euler_from_ned(ecef_pos, orientation_ned_gps))
self.reset_kalman(init_orient=initial_pose_ecef_quat)
self.update_kalman(current_time,
ObservationKind.ECEF_ORIENTATION_FROM_GPS,
initial_pose_ecef_quat)
elif gps_est_error > 50:
cloudlog.error(
"Locationd vs ubloxLocation position difference too large, kalman reset"
)
self.reset_kalman()
self.update_kalman(current_time,
ObservationKind.ECEF_POS,
ecef_pos,
R=ecef_pos_R)
self.update_kalman(current_time,
ObservationKind.ECEF_VEL,
ecef_vel,
R=ecef_vel_R)
def handle_car_state(self, current_time, log):
self.speed_counter += 1
if self.speed_counter % SENSOR_DECIMATION == 0:
self.update_kalman(current_time, ObservationKind.ODOMETRIC_SPEED,
[log.vEgo])
self.car_speed = abs(log.vEgo)
if log.vEgo == 0:
self.update_kalman(current_time, ObservationKind.NO_ROT,
[0, 0, 0])
def handle_cam_odo(self, current_time, log):
self.cam_counter += 1
if self.cam_counter % VISION_DECIMATION == 0:
rot_device = self.device_from_calib.dot(log.rot)
rot_device_std = self.device_from_calib.dot(log.rotStd)
self.update_kalman(
current_time, ObservationKind.CAMERA_ODO_ROTATION,
np.concatenate([rot_device, 10 * rot_device_std]))
trans_device = self.device_from_calib.dot(log.trans)
trans_device_std = self.device_from_calib.dot(log.transStd)
self.posenet_speed = np.linalg.norm(trans_device)
self.update_kalman(
current_time, ObservationKind.CAMERA_ODO_TRANSLATION,
np.concatenate([trans_device, 10 * trans_device_std]))
def handle_sensors(self, current_time, log):
# TODO does not yet account for double sensor readings in the log
for sensor_reading in log:
# Gyro Uncalibrated
if sensor_reading.sensor == 5 and sensor_reading.type == 16:
self.gyro_counter += 1
if self.gyro_counter % SENSOR_DECIMATION == 0:
v = sensor_reading.gyroUncalibrated.v
self.update_kalman(current_time,
ObservationKind.PHONE_GYRO,
[-v[2], -v[1], -v[0]])
# Accelerometer
if sensor_reading.sensor == 1 and sensor_reading.type == 1:
self.acc_counter += 1
if self.acc_counter % SENSOR_DECIMATION == 0:
v = sensor_reading.acceleration.v
self.update_kalman(current_time,
ObservationKind.PHONE_ACCEL,
[-v[2], -v[1], -v[0]])
def handle_live_calib(self, current_time, log):
self.calib = log.rpyCalib
self.device_from_calib = rot_from_euler(self.calib)
self.calib_from_device = self.device_from_calib.T
self.calibrated = log.calStatus == 1
def reset_kalman(self, current_time=None, init_orient=None):
self.filter_time = current_time
init_x = LiveKalman.initial_x
# too nonlinear to init on completely wrong
if init_orient is not None:
init_x[3:7] = init_orient
self.kf.init_state(init_x,
covs=np.diag(LiveKalman.initial_P_diag),
filter_time=current_time)
self.observation_buffer = []
self.gyro_counter = 0
self.acc_counter = 0
self.speed_counter = 0
self.cam_counter = 0
class Localizer():
def __init__(self, disabled_logs=[], dog=None):
self.kf = LiveKalman(GENERATED_DIR)
self.reset_kalman()
self.max_age = .2 # seconds
self.disabled_logs = disabled_logs
self.calib = np.zeros(3)
self.device_from_calib = np.eye(3)
self.calib_from_device = np.eye(3)
self.calibrated = 0
self.H = get_H()
@staticmethod
def msg_from_state(converter, calib_from_device, H, predicted_state,
predicted_cov):
predicted_std = np.sqrt(np.diagonal(predicted_cov))
fix_ecef = predicted_state[States.ECEF_POS]
fix_ecef_std = predicted_std[States.ECEF_POS_ERR]
vel_ecef = predicted_state[States.ECEF_VELOCITY]
vel_ecef_std = predicted_std[States.ECEF_VELOCITY_ERR]
fix_pos_geo = coord.ecef2geodetic(fix_ecef)
#fix_pos_geo_std = np.abs(coord.ecef2geodetic(fix_ecef + fix_ecef_std) - fix_pos_geo)
orientation_ecef = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION])
orientation_ecef_std = predicted_std[States.ECEF_ORIENTATION_ERR]
acc_calib = calib_from_device.dot(predicted_state[States.ACCELERATION])
acc_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ACCELERATION_ERR,
States.ACCELERATION_ERR]).dot(
calib_from_device.T)))
ang_vel_calib = calib_from_device.dot(
predicted_state[States.ANGULAR_VELOCITY])
ang_vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(
predicted_cov[States.ANGULAR_VELOCITY_ERR,
States.ANGULAR_VELOCITY_ERR]).dot(
calib_from_device.T)))
device_from_ecef = rot_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
vel_device = device_from_ecef.dot(vel_ecef)
device_from_ecef_eul = euler_from_quat(
predicted_state[States.ECEF_ORIENTATION]).T
idxs = list(
range(States.ECEF_ORIENTATION_ERR.start,
States.ECEF_ORIENTATION_ERR.stop)) + list(
range(States.ECEF_VELOCITY_ERR.start,
States.ECEF_VELOCITY_ERR.stop))
condensed_cov = predicted_cov[idxs][:, idxs]
HH = H(*list(np.concatenate([device_from_ecef_eul, vel_ecef])))
vel_device_cov = HH.dot(condensed_cov).dot(HH.T)
vel_device_std = np.sqrt(np.diagonal(vel_device_cov))
vel_calib = calib_from_device.dot(vel_device)
vel_calib_std = np.sqrt(
np.diagonal(
calib_from_device.dot(vel_device_cov).dot(
calib_from_device.T)))
orientation_ned = ned_euler_from_ecef(fix_ecef, orientation_ecef)
#orientation_ned_std = ned_euler_from_ecef(fix_ecef, orientation_ecef + orientation_ecef_std) - orientation_ned
ned_vel = converter.ecef2ned(fix_ecef +
vel_ecef) - converter.ecef2ned(fix_ecef)
#ned_vel_std = self.converter.ecef2ned(fix_ecef + vel_ecef + vel_ecef_std) - self.converter.ecef2ned(fix_ecef + vel_ecef)
fix = messaging.log.LiveLocationKalman.new_message()
fix.positionGeodetic.value = to_float(fix_pos_geo)
#fix.positionGeodetic.std = to_float(fix_pos_geo_std)
#fix.positionGeodetic.valid = True
fix.positionECEF.value = to_float(fix_ecef)
fix.positionECEF.std = to_float(fix_ecef_std)
fix.positionECEF.valid = True
fix.velocityECEF.value = to_float(vel_ecef)
fix.velocityECEF.std = to_float(vel_ecef_std)
fix.velocityECEF.valid = True
fix.velocityNED.value = to_float(ned_vel)
#fix.velocityNED.std = to_float(ned_vel_std)
#fix.velocityNED.valid = True
fix.velocityDevice.value = to_float(vel_device)
fix.velocityDevice.std = to_float(vel_device_std)
fix.velocityDevice.valid = True
fix.accelerationDevice.value = to_float(
predicted_state[States.ACCELERATION])
fix.accelerationDevice.std = to_float(
predicted_std[States.ACCELERATION_ERR])
fix.accelerationDevice.valid = True
fix.orientationECEF.value = to_float(orientation_ecef)
fix.orientationECEF.std = to_float(orientation_ecef_std)
fix.orientationECEF.valid = True
fix.orientationNED.value = to_float(orientation_ned)
#fix.orientationNED.std = to_float(orientation_ned_std)
#fix.orientationNED.valid = True
fix.angularVelocityDevice.value = to_float(
predicted_state[States.ANGULAR_VELOCITY])
fix.angularVelocityDevice.std = to_float(
predicted_std[States.ANGULAR_VELOCITY_ERR])
fix.angularVelocityDevice.valid = True
fix.velocityCalibrated.value = to_float(vel_calib)
fix.velocityCalibrated.std = to_float(vel_calib_std)
fix.velocityCalibrated.valid = True
fix.angularVelocityCalibrated.value = to_float(ang_vel_calib)
fix.angularVelocityCalibrated.std = to_float(ang_vel_calib_std)
fix.angularVelocityCalibrated.valid = True
fix.accelerationCalibrated.value = to_float(acc_calib)
fix.accelerationCalibrated.std = to_float(acc_calib_std)
fix.accelerationCalibrated.valid = True
return fix
def liveLocationMsg(self, time):
fix = self.msg_from_state(self.converter, self.calib_from_device,
self.H, self.kf.x, self.kf.P)
#fix.gpsWeek = self.time.week
#fix.gpsTimeOfWeek = self.time.tow
fix.unixTimestampMillis = self.unix_timestamp_millis
if self.filter_ready and self.calibrated:
fix.status = 'valid'
elif self.filter_ready:
fix.status = 'uncalibrated'
else:
fix.status = 'uninitialized'
return fix
def update_kalman(self, time, kind, meas):
if self.filter_ready:
try:
self.kf.predict_and_observe(time, kind, meas)
except KalmanError:
cloudlog.error("Error in predict and observe, kalman reset")
self.reset_kalman()
#idx = bisect_right([x[0] for x in self.observation_buffer], time)
#self.observation_buffer.insert(idx, (time, kind, meas))
#while len(self.observation_buffer) > 0 and self.observation_buffer[-1][0] - self.observation_buffer[0][0] > self.max_age:
# else:
# self.observation_buffer.pop(0)
def handle_gps(self, current_time, log):
self.converter = coord.LocalCoord.from_geodetic(
[log.latitude, log.longitude, log.altitude])
fix_ecef = self.converter.ned2ecef([0, 0, 0])
#self.time = GPSTime.from_datetime(datetime.utcfromtimestamp(log.timestamp*1e-3))
self.unix_timestamp_millis = log.timestamp
# TODO initing with bad bearing not allowed, maybe not bad?
if not self.filter_ready and log.speed > 5:
self.filter_ready = True
initial_ecef = fix_ecef
gps_bearing = math.radians(log.bearing)
initial_pose_ecef = ecef_euler_from_ned(initial_ecef,
[0, 0, gps_bearing])
initial_pose_ecef_quat = quat_from_euler(initial_pose_ecef)
gps_speed = log.speed
quat_uncertainty = 0.2**2
initial_state = LiveKalman.initial_x
initial_covs_diag = LiveKalman.initial_P_diag
initial_state[States.ECEF_POS] = initial_ecef
initial_state[States.ECEF_ORIENTATION] = initial_pose_ecef_quat
initial_state[States.ECEF_VELOCITY] = rot_from_quat(
initial_pose_ecef_quat).dot(np.array([gps_speed, 0, 0]))
initial_covs_diag[States.ECEF_POS_ERR] = 10**2
initial_covs_diag[States.ECEF_ORIENTATION_ERR] = quat_uncertainty
initial_covs_diag[States.ECEF_VELOCITY_ERR] = 1**2
self.kf.init_state(initial_state,
covs=np.diag(initial_covs_diag),
filter_time=current_time)
cloudlog.info("Filter initialized")
elif self.filter_ready:
self.update_kalman(current_time, ObservationKind.ECEF_POS,
fix_ecef)
gps_est_error = np.sqrt((self.kf.x[0] - fix_ecef[0])**2 +
(self.kf.x[1] - fix_ecef[1])**2 +
(self.kf.x[2] - fix_ecef[2])**2)
if gps_est_error > 50:
cloudlog.error(
"Locationd vs ubloxLocation difference too large, kalman reset"
)
self.reset_kalman()
def handle_car_state(self, current_time, log):
self.speed_counter += 1
if self.speed_counter % SENSOR_DECIMATION == 0:
self.update_kalman(current_time, ObservationKind.ODOMETRIC_SPEED,
[log.vEgo])
if log.vEgo == 0:
self.update_kalman(current_time, ObservationKind.NO_ROT,
[0, 0, 0])
def handle_cam_odo(self, current_time, log):
self.cam_counter += 1
if self.cam_counter % VISION_DECIMATION == 0:
rot_device = self.device_from_calib.dot(log.rot)
rot_device_std = self.device_from_calib.dot(log.rotStd)
self.update_kalman(current_time,
ObservationKind.CAMERA_ODO_ROTATION,
np.concatenate([rot_device, rot_device_std]))
trans_device = self.device_from_calib.dot(log.trans)
trans_device_std = self.device_from_calib.dot(log.transStd)
self.update_kalman(
current_time, ObservationKind.CAMERA_ODO_TRANSLATION,
np.concatenate([trans_device, trans_device_std]))
def handle_sensors(self, current_time, log):
# TODO does not yet account for double sensor readings in the log
for sensor_reading in log:
# Gyro Uncalibrated
if sensor_reading.sensor == 5 and sensor_reading.type == 16:
self.gyro_counter += 1
if self.gyro_counter % SENSOR_DECIMATION == 0:
if max(abs(self.kf.x[States.IMU_OFFSET])) > 0.07:
cloudlog.info('imu frame angles exceeded, correcting')
self.update_kalman(current_time,
ObservationKind.IMU_FRAME,
[0, 0, 0])
v = sensor_reading.gyroUncalibrated.v
self.update_kalman(current_time,
ObservationKind.PHONE_GYRO,
[-v[2], -v[1], -v[0]])
# Accelerometer
if sensor_reading.sensor == 1 and sensor_reading.type == 1:
self.acc_counter += 1
if self.acc_counter % SENSOR_DECIMATION == 0:
v = sensor_reading.acceleration.v
self.update_kalman(current_time,
ObservationKind.PHONE_ACCEL,
[-v[2], -v[1], -v[0]])
def handle_live_calib(self, current_time, log):
self.calib = log.rpyCalib
self.device_from_calib = rot_from_euler(self.calib)
self.calib_from_device = self.device_from_calib.T
self.calibrated = log.calStatus == 1
def reset_kalman(self):
self.filter_time = None
self.filter_ready = False
self.observation_buffer = []
self.gyro_counter = 0
self.acc_counter = 0
self.speed_counter = 0
self.cam_counter = 0