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309 lines
13 KiB
309 lines
13 KiB
#!/usr/bin/env python3
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import math
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import numpy as np
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import sympy as sp
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import cereal.messaging as messaging
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import common.transformations.coordinates as coord
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from common.transformations.orientation import (ecef_euler_from_ned,
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euler_from_quat,
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ned_euler_from_ecef,
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quat_from_euler,
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rot_from_quat, rot_from_euler)
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from selfdrive.locationd.kalman.helpers import ObservationKind, KalmanError
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from selfdrive.locationd.kalman.models.live_kf import LiveKalman, States
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from selfdrive.swaglog import cloudlog
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#from datetime import datetime
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#from laika.gps_time import GPSTime
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from sympy.utilities.lambdify import lambdify
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from selfdrive.locationd.kalman.helpers.sympy_helpers import euler_rotate
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VISION_DECIMATION = 2
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SENSOR_DECIMATION = 10
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def to_float(arr):
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return [float(arr[0]), float(arr[1]), float(arr[2])]
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def get_H():
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# this returns a function to eval the jacobian
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# of the observation function of the local vel
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roll = sp.Symbol('roll')
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pitch = sp.Symbol('pitch')
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yaw = sp.Symbol('yaw')
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vx = sp.Symbol('vx')
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vy = sp.Symbol('vy')
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vz = sp.Symbol('vz')
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h = euler_rotate(roll, pitch, yaw).T*(sp.Matrix([vx, vy, vz]))
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H = h.jacobian(sp.Matrix([roll, pitch, yaw, vx, vy, vz]))
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H_f = lambdify([roll, pitch, yaw, vx, vy, vz], H)
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return H_f
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class Localizer():
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def __init__(self, disabled_logs=[], dog=None):
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self.kf = LiveKalman()
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self.reset_kalman()
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self.max_age = .2 # seconds
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self.disabled_logs = disabled_logs
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self.calib = np.zeros(3)
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self.device_from_calib = np.eye(3)
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self.calib_from_device = np.eye(3)
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self.calibrated = 0
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self.H = get_H()
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def liveLocationMsg(self, time):
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predicted_state = self.kf.x
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predicted_cov = self.kf.P
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predicted_std = np.sqrt(np.diagonal(self.kf.P))
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fix_ecef = predicted_state[States.ECEF_POS]
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fix_ecef_std = predicted_std[States.ECEF_POS_ERR]
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vel_ecef = predicted_state[States.ECEF_VELOCITY]
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vel_ecef_std = predicted_std[States.ECEF_VELOCITY_ERR]
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fix_pos_geo = coord.ecef2geodetic(fix_ecef)
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#fix_pos_geo_std = np.abs(coord.ecef2geodetic(fix_ecef + fix_ecef_std) - fix_pos_geo)
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orientation_ecef = euler_from_quat(predicted_state[States.ECEF_ORIENTATION])
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orientation_ecef_std = predicted_std[States.ECEF_ORIENTATION_ERR]
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acc_calib = self.calib_from_device.dot(predicted_state[States.ACCELERATION])
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acc_calib_std = np.sqrt(np.diagonal(self.calib_from_device.dot(
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predicted_cov[States.ACCELERATION_ERR, States.ACCELERATION_ERR]).dot(
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self.calib_from_device.T)))
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ang_vel_calib = self.calib_from_device.dot(predicted_state[States.ANGULAR_VELOCITY])
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ang_vel_calib_std = np.sqrt(np.diagonal(self.calib_from_device.dot(
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predicted_cov[States.ANGULAR_VELOCITY_ERR, States.ANGULAR_VELOCITY_ERR]).dot(
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self.calib_from_device.T)))
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device_from_ecef = rot_from_quat(predicted_state[States.ECEF_ORIENTATION]).T
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vel_device = device_from_ecef.dot(vel_ecef)
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device_from_ecef_eul = euler_from_quat(predicted_state[States.ECEF_ORIENTATION]).T
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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))
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condensed_cov = predicted_cov[idxs][:,idxs]
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H = self.H(*list(np.concatenate([device_from_ecef_eul, vel_ecef])))
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vel_device_cov = H.dot(condensed_cov).dot(H.T)
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vel_device_std = np.sqrt(np.diagonal(vel_device_cov))
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vel_calib = self.calib_from_device.dot(vel_device)
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vel_calib_std = np.sqrt(np.diagonal(self.calib_from_device.dot(
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vel_device_cov).dot(
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self.calib_from_device.T)))
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orientation_ned = ned_euler_from_ecef(fix_ecef, orientation_ecef)
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#orientation_ned_std = ned_euler_from_ecef(fix_ecef, orientation_ecef + orientation_ecef_std) - orientation_ned
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ned_vel = self.converter.ecef2ned(fix_ecef + vel_ecef) - self.converter.ecef2ned(fix_ecef)
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#ned_vel_std = self.converter.ecef2ned(fix_ecef + vel_ecef + vel_ecef_std) - self.converter.ecef2ned(fix_ecef + vel_ecef)
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fix = messaging.log.LiveLocationKalman.new_message()
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fix.positionGeodetic.value = to_float(fix_pos_geo)
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#fix.positionGeodetic.std = to_float(fix_pos_geo_std)
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#fix.positionGeodetic.valid = True
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fix.positionECEF.value = to_float(fix_ecef)
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fix.positionECEF.std = to_float(fix_ecef_std)
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fix.positionECEF.valid = True
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fix.velocityECEF.value = to_float(vel_ecef)
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fix.velocityECEF.std = to_float(vel_ecef_std)
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fix.velocityECEF.valid = True
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fix.velocityNED.value = to_float(ned_vel)
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#fix.velocityNED.std = to_float(ned_vel_std)
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#fix.velocityNED.valid = True
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fix.velocityDevice.value = to_float(vel_device)
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fix.velocityDevice.std = to_float(vel_device_std)
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fix.velocityDevice.valid = True
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fix.accelerationDevice.value = to_float(predicted_state[States.ACCELERATION])
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fix.accelerationDevice.std = to_float(predicted_std[States.ACCELERATION_ERR])
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fix.accelerationDevice.valid = True
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fix.orientationECEF.value = to_float(orientation_ecef)
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fix.orientationECEF.std = to_float(orientation_ecef_std)
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fix.orientationECEF.valid = True
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fix.orientationNED.value = to_float(orientation_ned)
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#fix.orientationNED.std = to_float(orientation_ned_std)
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#fix.orientationNED.valid = True
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fix.angularVelocityDevice.value = to_float(predicted_state[States.ANGULAR_VELOCITY])
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fix.angularVelocityDevice.std = to_float(predicted_std[States.ANGULAR_VELOCITY_ERR])
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fix.angularVelocityDevice.valid = True
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fix.velocityCalibrated.value = to_float(vel_calib)
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fix.velocityCalibrated.std = to_float(vel_calib_std)
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fix.velocityCalibrated.valid = True
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fix.angularVelocityCalibrated.value = to_float(ang_vel_calib)
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fix.angularVelocityCalibrated.std = to_float(ang_vel_calib_std)
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fix.angularVelocityCalibrated.valid = True
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fix.accelerationCalibrated.value = to_float(acc_calib)
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fix.accelerationCalibrated.std = to_float(acc_calib_std)
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fix.accelerationCalibrated.valid = True
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#fix.gpsWeek = self.time.week
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#fix.gpsTimeOfWeek = self.time.tow
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fix.unixTimestampMillis = self.unix_timestamp_millis
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if self.filter_ready and self.calibrated:
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fix.status = 'valid'
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elif self.filter_ready:
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fix.status = 'uncalibrated'
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else:
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fix.status = 'uninitialized'
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return fix
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def update_kalman(self, time, kind, meas):
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if self.filter_ready:
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try:
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self.kf.predict_and_observe(time, kind, meas)
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except KalmanError:
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cloudlog.error("Error in predict and observe, kalman reset")
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self.reset_kalman()
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#idx = bisect_right([x[0] for x in self.observation_buffer], time)
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#self.observation_buffer.insert(idx, (time, kind, meas))
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#while len(self.observation_buffer) > 0 and self.observation_buffer[-1][0] - self.observation_buffer[0][0] > self.max_age:
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# else:
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# self.observation_buffer.pop(0)
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def handle_gps(self, current_time, log):
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self.converter = coord.LocalCoord.from_geodetic([log.latitude, log.longitude, log.altitude])
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fix_ecef = self.converter.ned2ecef([0, 0, 0])
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#self.time = GPSTime.from_datetime(datetime.utcfromtimestamp(log.timestamp*1e-3))
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self.unix_timestamp_millis = log.timestamp
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# TODO initing with bad bearing not allowed, maybe not bad?
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if not self.filter_ready and log.speed > 5:
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self.filter_ready = True
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initial_ecef = fix_ecef
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gps_bearing = math.radians(log.bearing)
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initial_pose_ecef = ecef_euler_from_ned(initial_ecef, [0, 0, gps_bearing])
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initial_pose_ecef_quat = quat_from_euler(initial_pose_ecef)
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gps_speed = log.speed
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quat_uncertainty = 0.2**2
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initial_pose_ecef_quat = quat_from_euler(initial_pose_ecef)
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initial_state = LiveKalman.initial_x
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initial_covs_diag = LiveKalman.initial_P_diag
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initial_state[States.ECEF_POS] = initial_ecef
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initial_state[States.ECEF_ORIENTATION] = initial_pose_ecef_quat
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initial_state[States.ECEF_VELOCITY] = rot_from_quat(initial_pose_ecef_quat).dot(np.array([gps_speed, 0, 0]))
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initial_covs_diag[States.ECEF_POS_ERR] = 10**2
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initial_covs_diag[States.ECEF_ORIENTATION_ERR] = quat_uncertainty
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initial_covs_diag[States.ECEF_VELOCITY_ERR] = 1**2
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self.kf.init_state(initial_state, covs=np.diag(initial_covs_diag), filter_time=current_time)
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cloudlog.info("Filter initialized")
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elif self.filter_ready:
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self.update_kalman(current_time, ObservationKind.ECEF_POS, fix_ecef)
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gps_est_error = np.sqrt((self.kf.x[0] - fix_ecef[0])**2 +
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(self.kf.x[1] - fix_ecef[1])**2 +
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(self.kf.x[2] - fix_ecef[2])**2)
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if gps_est_error > 50:
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cloudlog.error("Locationd vs ubloxLocation difference too large, kalman reset")
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self.reset_kalman()
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def handle_car_state(self, current_time, log):
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self.speed_counter += 1
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if self.speed_counter % SENSOR_DECIMATION == 0:
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self.update_kalman(current_time, ObservationKind.ODOMETRIC_SPEED, [log.vEgo])
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if log.vEgo == 0:
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self.update_kalman(current_time, ObservationKind.NO_ROT, [0, 0, 0])
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def handle_cam_odo(self, current_time, log):
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self.cam_counter += 1
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if self.cam_counter % VISION_DECIMATION == 0:
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rot_device = self.device_from_calib.dot(log.rot)
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rot_device_std = self.device_from_calib.dot(log.rotStd)
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self.update_kalman(current_time,
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ObservationKind.CAMERA_ODO_ROTATION,
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np.concatenate([rot_device, rot_device_std]))
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trans_device = self.device_from_calib.dot(log.trans)
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trans_device_std = self.device_from_calib.dot(log.transStd)
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self.update_kalman(current_time,
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ObservationKind.CAMERA_ODO_TRANSLATION,
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np.concatenate([trans_device, trans_device_std]))
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def handle_sensors(self, current_time, log):
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# TODO does not yet account for double sensor readings in the log
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for sensor_reading in log:
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# Gyro Uncalibrated
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if sensor_reading.sensor == 5 and sensor_reading.type == 16:
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self.gyro_counter += 1
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if self.gyro_counter % SENSOR_DECIMATION == 0:
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if max(abs(self.kf.x[States.IMU_OFFSET])) > 0.07:
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cloudlog.info('imu frame angles exceeded, correcting')
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self.update_kalman(current_time, ObservationKind.IMU_FRAME, [0, 0, 0])
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v = sensor_reading.gyroUncalibrated.v
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self.update_kalman(current_time, ObservationKind.PHONE_GYRO, [-v[2], -v[1], -v[0]])
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# Accelerometer
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if sensor_reading.sensor == 1 and sensor_reading.type == 1:
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self.acc_counter += 1
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if self.acc_counter % SENSOR_DECIMATION == 0:
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v = sensor_reading.acceleration.v
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self.update_kalman(current_time, ObservationKind.PHONE_ACCEL, [-v[2], -v[1], -v[0]])
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def handle_live_calib(self, current_time, log):
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self.calib = log.rpyCalib
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self.device_from_calib = rot_from_euler(self.calib)
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self.calib_from_device = self.device_from_calib.T
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self.calibrated = log.calStatus == 1
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def reset_kalman(self):
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self.filter_time = None
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self.filter_ready = False
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self.observation_buffer = []
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self.gyro_counter = 0
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self.acc_counter = 0
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self.speed_counter = 0
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self.cam_counter = 0
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def locationd_thread(sm, pm, disabled_logs=[]):
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if sm is None:
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sm = messaging.SubMaster(['gpsLocationExternal', 'sensorEvents', 'cameraOdometry', 'liveCalibration'])
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if pm is None:
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pm = messaging.PubMaster(['liveLocationKalman'])
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localizer = Localizer(disabled_logs=disabled_logs)
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while True:
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sm.update()
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for sock, updated in sm.updated.items():
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if updated:
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t = sm.logMonoTime[sock] * 1e-9
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if sock == "sensorEvents":
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localizer.handle_sensors(t, sm[sock])
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elif sock == "gpsLocationExternal":
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localizer.handle_gps(t, sm[sock])
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elif sock == "carState":
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localizer.handle_car_state(t, sm[sock])
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elif sock == "cameraOdometry":
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localizer.handle_cam_odo(t, sm[sock])
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elif sock == "liveCalibration":
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localizer.handle_live_calib(t, sm[sock])
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if localizer.filter_ready and sm.updated['gpsLocationExternal']:
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t = sm.logMonoTime['gpsLocationExternal']
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msg = messaging.new_message('liveLocationKalman')
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msg.logMonoTime = t
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msg.liveLocationKalman = localizer.liveLocationMsg(t * 1e-9)
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pm.send('liveLocationKalman', msg)
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def main(sm=None, pm=None):
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locationd_thread(sm, pm)
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if __name__ == "__main__":
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import os
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os.environ["OMP_NUM_THREADS"] = "1"
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main()
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