forked from paalmoest/drone
/
state_estimation.py
247 lines (217 loc) · 8.17 KB
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state_estimation.py
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from pykalman import KalmanFilter
import numpy as np
import time
class StateEstimationAltitude():
def __init__(self):
self.state = [0, 0]
self.covariance = np.eye(2)
# self.transition_covariance = np.array([
# [0.0000025, 0.000005],
# [0.0000005, 0.0000001],
# ])
self.observation_covariance = np.array([
[0.01]
])
self.transition_covariance = np.array([
[0.003, 0.0002],
[0.0002, 0.0002],
])
#self.transition_covariance = np.array([
# [0.00003, 0.000002],
# [0.000002, 0.000002],
#])
self.kf = KalmanFilter(
transition_covariance=self.transition_covariance, # H
observation_covariance=self.observation_covariance, # Q
)
self.previous_update = None
def update(self, observations):
if not self.previous_update:
self.previous_update = time.time()
dt = time.time() - self.previous_update
self.state, self.covariance = (
self.kf.filter_update(
self.state,
self.covariance,
observations,
transition_matrix=np.array([
[1, dt],
[0, 1]
]),
observation_matrix=np.array([
[1, 0],
]),
# observation_offset = np.array([, 0, 0])
# observation_covariance=np.array(0.1*np.eye(1))
)
)
self.previous_update = time.time()
def getAltitude(self):
return self.state[0]
class StateEstimationAltitudeCam():
def __init__(self):
self.state = [0, 0]
self.covariance = np.eye(2)
self.observation_covariance = np.array([
[1]
])
self.transition_covariance = np.array([
[0.001, 0.001],
[0.001, 0.001],
])
self.kf = KalmanFilter(
transition_covariance=self.transition_covariance, # H
observation_covariance=self.observation_covariance, # Q
)
self.previous_update = None
def update(self, observations):
if not self.previous_update:
self.previous_update = time.time()
dt = time.time() - self.previous_update
self.state, self.covariance = (
self.kf.filter_update(
self.state,
self.covariance,
observations,
transition_matrix=np.array([
[1, dt],
[0, 1]
]),
observation_matrix=np.array([
[1, 0],
]),
# observation_offset = np.array([, 0, 0])
# observation_covariance=np.array(0.1*np.eye(1))
)
)
self.previous_update = time.time()
def getAltitude(self):
return self.state[0]
class StateEstimationAccel():
def __init__(self):
"""
# x,y,z, Xvelo,Yvelo, Zvelo Xaccel, Yaccel, Zaccel, roll, pitch, yaw
"""
self.state = [0, 0, 0, 0, 0] # x, y ,z
self.covariance = np.eye(5)
transition_covariance = np.array([
[0.01, 0, 0, 0, 0],
[0, 0.1, 0, 0, 0],
[0, 0, 0.001, 0, 0],
[0, 0, 0, 0.001, 0],
[0, 0, 0, 0, 0.001],
])
self.observation_covariance = np.eye(3) * 0.1
# self.transition_covariance = np.eye(5) * 0.001
print transition_covariance
self.kf = KalmanFilter(
transition_covariance=transition_covariance, # H
observation_covariance=self.observation_covariance, # Q
)
def update(self, dt, observations):
self.state, self.covariance = (
self.kf.filter_update(
self.state,
self.covariance,
observations,
transition_matrix=np.array([
[1, dt, 0.5 * (dt**2), 0, 0],
[0, 1, dt, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
]),
observation_matrix=np.array([
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
]),
# observation_offset=np.array([[0]]),
)
)
class StateEstimationAltitude_offline():
def __init__(self):
self.state = [0, 0]
self.covariance = np.eye(2)
# self.transition_covariance = np.array([
# [0.0000025, 0.000005],
# [0.0000005, 0.0000001],
# ])
self.observation_covariance = np.array([
[0.01]
])
self.transition_covariance = np.array([
[0.00003, 0.000002],
[0.000002, 0.000002],
])
self.kf = KalmanFilter(
transition_covariance=self.transition_covariance, # H
observation_covariance=self.observation_covariance, # Q
)
self.previous_update = None
def update(self, dt, observations):
if not self.previous_update:
self.previous_update = time.time()
self.state, self.covariance = (
self.kf.filter_update(
self.state,
self.covariance,
observations,
transition_matrix=np.array([
[1, dt],
[0, 1]
]),
observation_matrix=np.array([
[1, 0],
]),
# observation_offset = np.array([, 0, 0])
# observation_covariance=np.array(0.1*np.eye(1))
)
)
self.previous_update = time.time()
def getAltitude(self):
return self.state[0]
class StateEstimationAltitude2():
def __init__(self):
self.state = [0, 0]
self.covariance = np.eye(2)
# self.transition_covariance = np.array([
# [0.0000025, 0.000005],
# [0.0000005, 0.0000001],
# ])
self.observation_covariance = np.array([
[0.01, 0],
[0, 0.5],
])
self.transition_covariance = np.array([
[0.003, 0.05],
[0, 0.02],
])
self.kf = KalmanFilter(
transition_covariance=self.transition_covariance, # H
observation_covariance=self.observation_covariance, # Q
)
self.previous_update = None
def update(self, dt, observations):
if not self.previous_update:
self.previous_update = time.time()
self.state, self.covariance = (
self.kf.filter_update(
self.state,
self.covariance,
observations,
transition_matrix=np.array([
[1, dt],
[0, 1],
]),
observation_matrix=np.array([
[1, 0],
[0, 1],
]),
# observation_offset = np.array([, 0, 0])
# o1bservation_covariance=np.array(0.1*np.eye(1))
)
)
self.previous_update = time.time()
def getAltitude(self):
return self.state[0]