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Merge pull request #127 from y011d4/modify-ekf

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fix EKF
This commit is contained in:
Atsushi Sakai
2018-11-08 20:45:10 +09:00
committed by GitHub
parent abab326b3e 2ecbaf35fb
commit ea5d3fff54
1 changed files with 19 additions and 19 deletions
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38
Localization/extended_kalman_filter/extended_kalman_filter.py
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@@ -11,8 +11,8 @@ import math
import matplotlib.pyplot as plt
# Estimation parameter of EKF
Q = np.diag([0.1, 0.1, np.deg2rad(1.0), 1.0])**2
R = np.diag([1.0, np.deg2rad(40.0)])**2
Q = np.diag([1.0, 1.0])**2
R = np.diag([0.1, 0.1, np.deg2rad(1.0), 1.0])**2
# Simulation parameter
Qsim = np.diag([0.5, 0.5])**2
@@ -53,14 +53,14 @@ def observation(xTrue, xd, u):
def motion_model(x, u):
F = np.array([[1.0, 0, 0, 0],
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
[0, 1.0, 0, 0],
[0, 0, 1.0, 0],
[0, 0, 0, 0]])
B = np.array([[DT * math.cos(x[2, 0]), 0],
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
[DT * math.sin(x[2, 0]), 0],
[0.0, DT],
[1.0, 0.0]])
x = F.dot(x) + B.dot(u)
@@ -120,13 +120,13 @@ def ekf_estimation(xEst, PEst, z, u):
# Predict
xPred = motion_model(xEst, u)
jF = jacobF(xPred, u)
PPred = jF * PEst * jF.T + Q
PPred = jF.dot(PEst).dot(jF.T) + R
# Update
jH = jacobH(xPred)
zPred = observation_model(xPred)
y = z.T - zPred
S = jH.dot(PPred).dot(jH.T) + R
S = jH.dot(PPred).dot(jH.T) + Q
K = PPred.dot(jH.T).dot(np.linalg.inv(S))
xEst = xPred + K.dot(y)
PEst = (np.eye(len(xEst)) - K.dot(jH)).dot(PPred)
@@ -165,11 +165,11 @@ def main():
time = 0.0
# State Vector [x y yaw v]'
xEst = np.array(np.zeros((4, 1)))
xTrue = np.array(np.zeros((4, 1)))
xEst = np.zeros((4, 1))
xTrue = np.zeros((4, 1))
PEst = np.eye(4)
xDR = np.array(np.zeros((4, 1))) # Dead reckoning
xDR = np.zeros((4, 1)) # Dead reckoning
# history
hxEst = xEst
@@ -194,12 +194,12 @@ def main():
if show_animation:
plt.cla()
plt.plot(hz[:, 0], hz[:, 1], ".g")
plt.plot(np.array(hxTrue[0, :]).flatten(),
np.array(hxTrue[1, :]).flatten(), "-b")
plt.plot(np.array(hxDR[0, :]).flatten(),
np.array(hxDR[1, :]).flatten(), "-k")
plt.plot(np.array(hxEst[0, :]).flatten(),
np.array(hxEst[1, :]).flatten(), "-r")
plt.plot(hxTrue[0, :].flatten(),
hxTrue[1, :].flatten(), "-b")
plt.plot(hxDR[0, :].flatten(),
hxDR[1, :].flatten(), "-k")
plt.plot(hxEst[0, :].flatten(),
hxEst[1, :].flatten(), "-r")
plot_covariance_ellipse(xEst, PEst)
plt.axis("equal")
plt.grid(True)