def test_pf2():
N = 1000
sensor_std_err = .2
landmarks = [[-1, 2], [20, 4], [-20, 6], [18, 25]]
pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)
xs = []
for x in range(18):
zs = []
pos = (x + 1, x + 1)
for landmark in landmarks:
d = np.sqrt((landmark[0] - pos[0])**2 + (landmark[1] - pos[1])**2)
zs.append(d + randn() * sensor_std_err)
# move diagonally forward to (x+1, x+1)
pf.predict((0.00, 1.414), (.2, .05))
pf.update(z=zs)
pf.resample()
mu, var = pf.estimate()
xs.append(mu)
xs = np.array(xs)
plt.plot(xs[:, 0], xs[:, 1])
plt.show()
def test_pf():
#seed(1234)
N = 10000
R = .2
landmarks = [[-1, 2], [20, 4], [10, 30], [18, 25]]
#landmarks = [[-1, 2], [2,4]]
pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, R)
plot_pf(pf, 20, 20, weights=False)
dt = .01
plt.pause(dt)
for x in range(18):
zs = []
pos = (x + 3, x + 3)
for landmark in landmarks:
d = np.sqrt((landmark[0] - pos[0])**2 + (landmark[1] - pos[1])**2)
zs.append(d + randn() * R)
pf.predict((0.01, 1.414), (.2, .05))
pf.update(z=zs)
pf.resample()
#print(x, np.array(list(zip(pf.particles, pf.weights))))
mu, var = pf.estimate()
plot_pf(pf, 20, 20, weights=False)
plt.plot(pos[0], pos[1], marker='*', color='r', ms=10)
plt.scatter(mu[0], mu[1], color='g', s=100)
plt.tight_layout()
plt.pause(dt)
