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350 lines
14 KiB
350 lines
14 KiB
#!/usr/bin/env python3
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import json
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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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from cereal import log
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from common.params import Params
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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, euler_from_rot, \
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rot_from_quat, rot_from_euler
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from rednose.helpers import KalmanError
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from selfdrive.locationd.models.live_kf import LiveKalman, States, ObservationKind
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from selfdrive.locationd.models.constants import GENERATED_DIR
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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 rednose.helpers.sympy_helpers import euler_rotate
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SensorSource = log.SensorEventData.SensorSource
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VISION_DECIMATION = 2
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SENSOR_DECIMATION = 10
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POSENET_STD_HIST = 40
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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=None, dog=None):
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if disabled_logs is None:
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disabled_logs = []
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self.kf = LiveKalman(GENERATED_DIR)
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self.reset_kalman()
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self.max_age = .1 # 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 = False
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self.H = get_H()
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self.posenet_invalid_count = 0
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self.posenet_speed = 0
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self.car_speed = 0
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self.posenet_stds = 10*np.ones((POSENET_STD_HIST))
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self.converter = coord.LocalCoord.from_ecef(self.kf.x[States.ECEF_POS])
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self.unix_timestamp_millis = 0
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self.last_gps_fix = 0
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self.device_fell = False
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@staticmethod
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def msg_from_state(converter, calib_from_device, H, predicted_state, predicted_cov, calibrated):
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predicted_std = np.sqrt(np.diagonal(predicted_cov))
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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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device_from_ecef = rot_from_quat(predicted_state[States.ECEF_ORIENTATION]).T
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calibrated_orientation_ecef = euler_from_rot(calib_from_device.dot(device_from_ecef))
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acc_calib = calib_from_device.dot(predicted_state[States.ACCELERATION])
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acc_calib_std = np.sqrt(np.diagonal(calib_from_device.dot(
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predicted_cov[States.ACCELERATION_ERR, States.ACCELERATION_ERR]).dot(
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calib_from_device.T)))
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ang_vel_calib = calib_from_device.dot(predicted_state[States.ANGULAR_VELOCITY])
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ang_vel_calib_std = np.sqrt(np.diagonal(calib_from_device.dot(
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predicted_cov[States.ANGULAR_VELOCITY_ERR, States.ANGULAR_VELOCITY_ERR]).dot(
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calib_from_device.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)) + \
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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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HH = H(*list(np.concatenate([device_from_ecef_eul, vel_ecef])))
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vel_device_cov = HH.dot(condensed_cov).dot(HH.T)
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vel_device_std = np.sqrt(np.diagonal(vel_device_cov))
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vel_calib = calib_from_device.dot(vel_device)
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vel_calib_std = np.sqrt(np.diagonal(calib_from_device.dot(
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vel_device_cov).dot(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 = converter.ecef2ned(fix_ecef + vel_ecef) - 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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# write measurements to msg
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measurements = [
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# measurement field, value, std, valid
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(fix.positionGeodetic, fix_pos_geo, np.nan*np.zeros(3), True),
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(fix.positionECEF, fix_ecef, fix_ecef_std, True),
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(fix.velocityECEF, vel_ecef, vel_ecef_std, True),
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(fix.velocityNED, ned_vel, np.nan*np.zeros(3), True),
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(fix.velocityDevice, vel_device, vel_device_std, True),
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(fix.accelerationDevice, predicted_state[States.ACCELERATION], predicted_std[States.ACCELERATION_ERR], True),
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(fix.orientationECEF, orientation_ecef, orientation_ecef_std, True),
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(fix.calibratedOrientationECEF, calibrated_orientation_ecef, np.nan*np.zeros(3), calibrated),
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(fix.orientationNED, orientation_ned, np.nan*np.zeros(3), True),
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(fix.angularVelocityDevice, predicted_state[States.ANGULAR_VELOCITY], predicted_std[States.ANGULAR_VELOCITY_ERR], True),
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(fix.velocityCalibrated, vel_calib, vel_calib_std, calibrated),
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(fix.angularVelocityCalibrated, ang_vel_calib, ang_vel_calib_std, calibrated),
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(fix.accelerationCalibrated, acc_calib, acc_calib_std, calibrated),
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]
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for field, value, std, valid in measurements:
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# TODO: can we write the lists faster?
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field.value = to_float(value)
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field.std = to_float(std)
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field.valid = valid
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return fix
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def liveLocationMsg(self):
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fix = self.msg_from_state(self.converter, self.calib_from_device, self.H, self.kf.x, self.kf.P, self.calibrated)
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# experimentally found these values, no false positives in 20k minutes of driving
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old_mean, new_mean = np.mean(self.posenet_stds[:POSENET_STD_HIST//2]), np.mean(self.posenet_stds[POSENET_STD_HIST//2:])
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std_spike = new_mean/old_mean > 4 and new_mean > 7
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fix.posenetOK = not (std_spike and self.car_speed > 5)
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fix.deviceStable = not self.device_fell
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self.device_fell = False
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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 np.linalg.norm(fix.positionECEF.std) < 50 and self.calibrated:
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fix.status = 'valid'
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elif np.linalg.norm(fix.positionECEF.std) < 50:
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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, R=None):
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try:
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self.kf.predict_and_observe(time, kind, meas, R)
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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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def handle_gps(self, current_time, log):
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# ignore the message if the fix is invalid
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if log.flags % 2 == 0:
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return
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self.last_gps_fix = current_time
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self.converter = coord.LocalCoord.from_geodetic([log.latitude, log.longitude, log.altitude])
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ecef_pos = self.converter.ned2ecef([0, 0, 0])
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ecef_vel = self.converter.ned2ecef(np.array(log.vNED)) - ecef_pos
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ecef_pos_R = np.diag([(3*log.verticalAccuracy)**2]*3)
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ecef_vel_R = np.diag([(log.speedAccuracy)**2]*3)
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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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gps_est_error = np.sqrt((self.kf.x[0] - ecef_pos[0])**2 +
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(self.kf.x[1] - ecef_pos[1])**2 +
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(self.kf.x[2] - ecef_pos[2])**2)
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orientation_ecef = euler_from_quat(self.kf.x[States.ECEF_ORIENTATION])
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orientation_ned = ned_euler_from_ecef(ecef_pos, orientation_ecef)
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orientation_ned_gps = np.array([0, 0, np.radians(log.bearingDeg)])
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orientation_error = np.mod(orientation_ned - orientation_ned_gps - np.pi, 2*np.pi) - np.pi
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initial_pose_ecef_quat = quat_from_euler(ecef_euler_from_ned(ecef_pos, orientation_ned_gps))
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if np.linalg.norm(ecef_vel) > 5 and np.linalg.norm(orientation_error) > 1:
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cloudlog.error("Locationd vs ubloxLocation orientation difference too large, kalman reset")
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self.reset_kalman(init_pos=ecef_pos, init_orient=initial_pose_ecef_quat)
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self.update_kalman(current_time, ObservationKind.ECEF_ORIENTATION_FROM_GPS, initial_pose_ecef_quat)
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elif gps_est_error > 50:
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cloudlog.error("Locationd vs ubloxLocation position difference too large, kalman reset")
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self.reset_kalman(init_pos=ecef_pos, init_orient=initial_pose_ecef_quat)
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self.update_kalman(current_time, ObservationKind.ECEF_POS, ecef_pos, R=ecef_pos_R)
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self.update_kalman(current_time, ObservationKind.ECEF_VEL, ecef_vel, R=ecef_vel_R)
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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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self.car_speed = abs(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, 10*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.posenet_speed = np.linalg.norm(trans_device)
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self.posenet_stds[:-1] = self.posenet_stds[1:]
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self.posenet_stds[-1] = trans_device_std[0]
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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, 10*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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sensor_time = 1e-9 * sensor_reading.timestamp
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# TODO: handle messages from two IMUs at the same time
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if sensor_reading.source == SensorSource.lsm6ds3:
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continue
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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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v = sensor_reading.gyroUncalibrated.v
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self.update_kalman(sensor_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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# check if device fell, estimate 10 for g
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# 40m/s**2 is a good filter for falling detection, no false positives in 20k minutes of driving
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self.device_fell = self.device_fell or (np.linalg.norm(np.array(sensor_reading.acceleration.v) - np.array([10, 0, 0])) > 40)
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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(sensor_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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if len(log.rpyCalib):
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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, current_time=None, init_orient=None, init_pos=None):
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self.filter_time = current_time
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init_x = LiveKalman.initial_x.copy()
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# too nonlinear to init on completely wrong
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if init_orient is not None:
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init_x[3:7] = init_orient
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if init_pos is not None:
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init_x[:3] = init_pos
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self.kf.init_state(init_x, covs=np.diag(LiveKalman.initial_P_diag), filter_time=current_time)
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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=None):
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if disabled_logs is None:
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disabled_logs = []
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if sm is None:
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socks = ['gpsLocationExternal', 'sensorEvents', 'cameraOdometry', 'liveCalibration', 'carState']
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sm = messaging.SubMaster(socks, ignore_alive=['gpsLocationExternal'])
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if pm is None:
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pm = messaging.PubMaster(['liveLocationKalman'])
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params = Params()
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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 and sm.valid[sock]:
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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 sm.updated['cameraOdometry']:
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t = sm.logMonoTime['cameraOdometry']
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msg = messaging.new_message('liveLocationKalman')
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msg.logMonoTime = t
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msg.liveLocationKalman = localizer.liveLocationMsg()
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msg.liveLocationKalman.inputsOK = sm.all_alive_and_valid()
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msg.liveLocationKalman.sensorsOK = sm.alive['sensorEvents'] and sm.valid['sensorEvents']
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gps_age = (t / 1e9) - localizer.last_gps_fix
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msg.liveLocationKalman.gpsOK = gps_age < 1.0
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pm.send('liveLocationKalman', msg)
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if sm.frame % 1200 == 0 and msg.liveLocationKalman.gpsOK: # once a minute
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location = {
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'latitude': msg.liveLocationKalman.positionGeodetic.value[0],
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'longitude': msg.liveLocationKalman.positionGeodetic.value[1],
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'altitude': msg.liveLocationKalman.positionGeodetic.value[2],
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}
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params.put("LastGPSPosition", json.dumps(location))
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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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