class KalmanFilterProxy:
def __init__(self, host='localhost', port=4567):
# Connection details
self._host = host
self._port = port
self.ekf = EKF()
# Server callbacks definition
self._sio = socketio.AsyncServer()
self._sio.on('connect', self._on_sim_connect)
self._sio.on('disconnect', self._on_sim_disconnect)
self._sio.on('telemetry', self._on_telemetry)
# History stacking
self.estimations_history = []
self.groundtruth_history = []
def run(self):
# Application creation
app = web.Application()
self._sio.attach(app)
# Run app itself
web.run_app(app, host=self._host, port=self._port)
@staticmethod
def _on_sim_connect(sid, environ):
print('Connected: ', sid)
@staticmethod
def _on_sim_disconnect(sid):
print('Disconnected: ', sid)
async def _on_telemetry(self, sid, data):
# Visualize data received data
print('Telemetry received: ', data)
# Communication
return_msg = dict()
if data is not None:
meas_list = str.split(data['sensor_measurement'])
# Parse data accordingly to sensor type
if meas_list[0] == "L":
measurement = Measurement(
sensor=Sensor.LIDAR,
data=np.array([float(x) for x in meas_list[1:3]]),
ts_us=int(meas_list[3]))
groundtruth = [float(x) for x in meas_list[4:8]]
elif meas_list[0] == "R":
measurement = Measurement(
sensor=Sensor.RADAR,
data=np.array([float(x) for x in meas_list[1:4]]),
ts_us=int(meas_list[4]))
groundtruth = [float(x) for x in meas_list[5:9]]
else:
raise ValueError('Not correct sensor type')
self.ekf.process_measurement(measurement)
estimation = self.ekf.get_estimation()
# Stack history of estimation vs groundtruth
self.estimations_history.append(estimation)
self.groundtruth_history.append(groundtruth)
# Calculate RMSE
estimation_rmse = rmse(self.estimations_history,
self.groundtruth_history)
# Create message
return_msg['estimate_x'] = float(estimation[0])
return_msg['estimate_y'] = float(estimation[1])
return_msg['rmse_x'] = estimation_rmse[0]
return_msg['rmse_y'] = estimation_rmse[1]
return_msg['rmse_vx'] = estimation_rmse[2]
return_msg['rmse_vy'] = estimation_rmse[3]
# Message sending
print("Sending data: ", return_msg)
await self._sio.emit('estimate_marker', return_msg)
else:
# If data is not valid, send dummy response
await self._sio.emit('manual', {})
