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Atsushi Sakai
2018-03-27 23:20:38 -07:00
parent 8163031306
commit 00140e5a8d
1 changed files with 18 additions and 13 deletions
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31
SLAM/GraphBasedSLAM/graph_based_slam.py
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@@ -14,18 +14,18 @@ import matplotlib.pyplot as plt
# Simulation parameter
Qsim = np.diag([0.0, math.radians(0.0)])**2
Rsim = np.diag([0.0, math.radians(00.0)])**2
Qsim = np.diag([0.1, math.radians(1.0)])**2
Rsim = np.diag([0.1, math.radians(5.0)])**2
DT = 5.0 # time tick [s]
SIM_TIME = 20.0 # simulation time [s]
DT = 1.0 # time tick [s]
SIM_TIME = 40.0 # simulation time [s]
MAX_RANGE = 20.0 # maximum observation range
STATE_SIZE = 3 # State size [x,y,yaw]
# Covariance parameter of Graph Based SLAM
C_SIGMA1 = 1.0
C_SIGMA2 = 0.1
C_SIGMA3 = 0.1
C_SIGMA2 = 1.0
C_SIGMA3 = math.radians(35.0)
MAX_ITR = 20 # Maximuma iteration
@@ -50,8 +50,8 @@ class Edge():
def cal_observation_sigma(d):
sigma = np.zeros((3, 3))
sigma[0, 0] = (d * C_SIGMA1)**2
sigma[1, 1] = (d * C_SIGMA2)**2
sigma[0, 0] = C_SIGMA1**2
sigma[1, 1] = C_SIGMA2**2
sigma[2, 2] = C_SIGMA3**2
return sigma
@@ -87,6 +87,9 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
sig2 = cal_observation_sigma(d2)
edge.omega = np.linalg.inv(Rt1 * sig1 * Rt1.T + Rt2 * sig2 * Rt2.T)
# print(edge.omega)
# edge.omega = np.eye(3)
edge.d1, edge.d2 = d1, d2
edge.yaw1, edge.yaw2 = yaw1, yaw2
edge.angle1, edge.angle2 = angle1, angle2
@@ -159,7 +162,8 @@ def graph_based_slam(x_init, hz):
print("start graph based slam")
x_opt = copy.deepcopy(x_init)
n = len(hz) * STATE_SIZE
# n = len(hz) * STATE_SIZE
n = x_opt.shape[1] * STATE_SIZE
for itr in range(MAX_ITR):
edges = calc_edges(x_opt, hz)
@@ -176,7 +180,7 @@ def graph_based_slam(x_init, hz):
dx = - np.linalg.inv(H).dot(b)
for i in range(len(hz)):
for i in range((x_opt.shape[1])):
x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0]
diff = dx.T.dot(dx)
@@ -270,8 +274,7 @@ def main():
hxTrue = xTrue
hxDR = xTrue
hz = [np.matrix(np.zeros((1, 4)))]
hz[0][0, 3] = -1
# hz = []
hz = []
while SIM_TIME >= time:
time += DT
@@ -283,6 +286,8 @@ def main():
hxTrue = np.hstack((hxTrue, xTrue))
hz.append(z)
hz.append(hz[-1])
x_opt = graph_based_slam(hxDR, hz)
if show_animation:
@@ -291,7 +296,7 @@ def main():
plt.plot(RFID[:, 0], RFID[:, 1], "*k")
plt.plot(np.array(hxTrue[0, :]).flatten(),
np.array(hxTrue[1, :]).flatten(), "-b")
np.array(hxTrue[1, :]).flatten(), "xb")
plt.plot(np.array(hxDR[0, :]).flatten(),
np.array(hxDR[1, :]).flatten(), "-k")
plt.plot(np.array(x_opt[0, :]).flatten(),