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_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)
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)
def run_pf1(N, iters=18, sensor_std_err=.1,
do_plot=True, plot_particles=False,
xlim=(0, 20), ylim=(0, 20),
initial_x=None):
landmarks = np.array([[-1, 2], [5, 10], [12,14], [18,21]])
NL = len(landmarks)
pf = RobotLocalizationParticleFilter(
N, 20, 20, landmarks, sensor_std_err)
if initial_x is not None:
pf.create_gaussian_particles(mean=initial_x,
var=(5, 5, np.pi/4))
if plot_particles:
alpha = .20
if N > 5000:
alpha *= np.sqrt(5000)/np.sqrt(N)
plt.scatter(pf.particles[:, 0], pf.particles[:, 1],
alpha=alpha, color='g')
xs = []
pos = np.array([0., 0.])
for x in range(iters):
pos += (1, 1) # robot position
# distance from robot to each landmark
zs = norm(landmarks - pos, axis=1) + randn(NL)*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)
if plot_particles:
plt.scatter(pf.particles[:, 0], pf.particles[:, 1],
color='k', marker=',', s=1)
p1 = plt.scatter(pos[0], pos[1], marker='+',
color='k', s=180, lw=3)
p2 = plt.scatter(mu[0], mu[1], marker='s', color='r')
xs = np.array(xs)
#plt.plot(xs[:, 0], xs[:, 1])
plt.legend([p1, p2], ['Actual', 'PF'], loc=4, numpoints=1)
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.show()
print('final position error, variance:\n\t', mu, var)