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fix inaccurate covariance calculation to add a new landmark in FastSLAM1,2 (#334)

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Atsushi Sakai
2020-06-05 21:21:35 +09:00
committed by GitHub
parent 03a92fc23e
commit 26b2c552ef
2 changed files with 96 additions and 80 deletions
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108
SLAM/FastSLAM1/fast_slam1.py
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@@ -16,16 +16,16 @@ Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2
R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
# Simulation parameter
Qsim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
Rsim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
OFFSET_YAWRATE_NOISE = 0.01
Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
OFFSET_YAW_RATE_NOISE = 0.01
DT = 0.1 # time tick [s]
SIM_TIME = 50.0 # simulation time [s]
MAX_RANGE = 20.0 # maximum observation range
M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association.
STATE_SIZE = 3 # State size [x,y,yaw]
LM_SIZE = 2 # LM srate size [x,y]
LM_SIZE = 2 # LM state size [x,y]
N_PARTICLE = 100 # number of particle
NTH = N_PARTICLE / 1.5 # Number of particle for re-sampling
@@ -34,15 +34,15 @@ show_animation = True
class Particle:
def __init__(self, N_LM):
def __init__(self, n_landmark):
self.w = 1.0 / N_PARTICLE
self.x = 0.0
self.y = 0.0
self.yaw = 0.0
# landmark x-y positions
self.lm = np.zeros((N_LM, LM_SIZE))
self.lm = np.zeros((n_landmark, LM_SIZE))
# landmark position covariance
self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE))
self.lmP = np.zeros((n_landmark * LM_SIZE, LM_SIZE))
def fast_slam1(particles, u, z):
@@ -56,11 +56,11 @@ def fast_slam1(particles, u, z):
def normalize_weight(particles):
sumw = sum([p.w for p in particles])
sum_w = sum([p.w for p in particles])
try:
for i in range(N_PARTICLE):
particles[i].w /= sumw
particles[i].w /= sum_w
except ZeroDivisionError:
for i in range(N_PARTICLE):
particles[i].w = 1.0 / N_PARTICLE
@@ -101,7 +101,7 @@ def predict_particles(particles, u):
return particles
def add_new_lm(particle, z, Q_cov):
def add_new_landmark(particle, z, Q_cov):
r = z[0]
b = z[1]
lm_id = int(z[2])
@@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov):
particle.lm[lm_id, 1] = particle.y + r * s
# covariance
Gz = np.array([[c, -r * s],
[s, r * c]])
particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
dx = r * c
dy = r * s
d2 = dx**2 + dy**2
d = math.sqrt(d2)
Gz = np.array([[dx / d, dy / d],
[-dy / d2, dx / d2]])
particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv(
Gz) @ Q_cov @ np.linalg.inv(Gz.T)
return particle
@@ -146,10 +150,10 @@ def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf):
S = Hf @ PHt + Q_cov
S = (S + S.T) * 0.5
SChol = np.linalg.cholesky(S).T
SCholInv = np.linalg.inv(SChol)
W1 = PHt @ SCholInv
W = W1 @ SCholInv.T
s_chol = np.linalg.cholesky(S).T
s_chol_inv = np.linalg.inv(s_chol)
W1 = PHt @ s_chol_inv
W = W1 @ s_chol_inv.T
x = xf + W @ v
P = Pf - W1 @ W1.T
@@ -187,7 +191,7 @@ def compute_weight(particle, z, Q_cov):
try:
invS = np.linalg.inv(Sf)
except np.linalg.linalg.LinAlgError:
print("singuler")
print("singular")
return 1.0
num = math.exp(-0.5 * dx.T @ invS @ dx)
@@ -201,12 +205,12 @@ def compute_weight(particle, z, Q_cov):
def update_with_observation(particles, z):
for iz in range(len(z[0, :])):
lmid = int(z[2, iz])
landmark_id = int(z[2, iz])
for ip in range(N_PARTICLE):
# new landmark
if abs(particles[ip].lm[lmid, 0]) <= 0.01:
particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)
if abs(particles[ip].lm[landmark_id, 0]) <= 0.01:
particles[ip] = add_new_landmark(particles[ip], z[:, iz], Q)
# known landmark
else:
w = compute_weight(particles[ip], z[:, iz], Q)
@@ -229,28 +233,29 @@ def resampling(particles):
pw = np.array(pw)
Neff = 1.0 / (pw @ pw.T) # Effective particle number
# print(Neff)
n_eff = 1.0 / (pw @ pw.T) # Effective particle number
# print(n_eff)
if Neff < NTH: # resampling
wcum = np.cumsum(pw)
if n_eff < NTH: # resampling
w_cum = np.cumsum(pw)
base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE
resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
inds = []
ind = 0
for ip in range(N_PARTICLE):
while (ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind]):
while (ind < w_cum.shape[0] - 1) \
and (resample_id[ip] > w_cum[ind]):
ind += 1
inds.append(ind)
tparticles = particles[:]
tmp_particles = particles[:]
for i in range(len(inds)):
particles[i].x = tparticles[inds[i]].x
particles[i].y = tparticles[inds[i]].y
particles[i].yaw = tparticles[inds[i]].yaw
particles[i].lm = tparticles[inds[i]].lm[:, :]
particles[i].lmP = tparticles[inds[i]].lmP[:, :]
particles[i].x = tmp_particles[inds[i]].x
particles[i].y = tmp_particles[inds[i]].y
particles[i].yaw = tmp_particles[inds[i]].yaw
particles[i].lm = tmp_particles[inds[i]].lm[:, :]
particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
particles[i].w = 1.0 / N_PARTICLE
return particles
@@ -259,37 +264,39 @@ def resampling(particles):
def calc_input(time):
if time <= 3.0: # wait at first
v = 0.0
yawrate = 0.0
yaw_rate = 0.0
else:
v = 1.0 # [m/s]
yawrate = 0.1 # [rad/s]
yaw_rate = 0.1 # [rad/s]
u = np.array([v, yawrate]).reshape(2, 1)
u = np.array([v, yaw_rate]).reshape(2, 1)
return u
def observation(xTrue, xd, u, RFID):
def observation(xTrue, xd, u, rfid):
# calc true state
xTrue = motion_model(xTrue, u)
# add noise to range observation
z = np.zeros((3, 0))
for i in range(len(RFID[:, 0])):
for i in range(len(rfid[:, 0])):
dx = RFID[i, 0] - xTrue[0, 0]
dy = RFID[i, 1] - xTrue[1, 0]
dx = rfid[i, 0] - xTrue[0, 0]
dy = rfid[i, 1] - xTrue[1, 0]
d = math.hypot(dx, dy)
angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
if d <= MAX_RANGE:
dn = d + np.random.randn() * Qsim[0, 0] ** 0.5 # add noise
anglen = angle + np.random.randn() * Qsim[1, 1] ** 0.5 # add noise
zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise
angle_with_noize = angle + np.random.randn() * Q_sim[
1, 1] ** 0.5 # add noise
zi = np.array([dn, pi_2_pi(angle_with_noize), i]).reshape(3, 1)
z = np.hstack((z, zi))
# add noise to input
ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ** 0.5
ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] ** 0.5 + OFFSET_YAWRATE_NOISE
ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
ud2 = u[1, 0] + np.random.randn() * R_sim[
1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
ud = np.array([ud1, ud2]).reshape(2, 1)
xd = motion_model(xd, ud)
@@ -332,7 +339,7 @@ def main():
[-5.0, 5.0],
[-10.0, 15.0]
])
N_LM = RFID.shape[0]
n_landmark = RFID.shape[0]
# State Vector [x y yaw v]'
xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation
@@ -344,7 +351,7 @@ def main():
hxTrue = xTrue
hxDR = xTrue
particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
while SIM_TIME >= time:
time += DT
@@ -366,8 +373,9 @@ def main():
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.gcf().canvas.mpl_connect(
'key_release_event', lambda event:
[exit(0) if event.key == 'escape' else None])
plt.plot(RFID[:, 0], RFID[:, 1], "*k")
for i in range(N_PARTICLE):

68
SLAM/FastSLAM2/fast_slam2.py
View File

@@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov):
particle.lm[lm_id, 1] = particle.y + r * s
# covariance
Gz = np.array([[c, -r * s],
[s, r * c]])
particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
dx = r * c
dy = r * s
d2 = dx ** 2 + dy ** 2
d = math.sqrt(d2)
Gz = np.array([[dx / d, dy / d],
[-dy / d2, dx / d2]])
particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv(
Gz) @ Q_cov @ np.linalg.inv(Gz.T)
return particle
@@ -224,11 +228,11 @@ def proposal_sampling(particle, z, Q_cov):
def update_with_observation(particles, z):
for iz in range(len(z[0, :])):
lmid = int(z[2, iz])
landmark_id = int(z[2, iz])
for ip in range(N_PARTICLE):
# new landmark
if abs(particles[ip].lm[lmid, 0]) <= 0.01:
if abs(particles[ip].lm[landmark_id, 0]) <= 0.01:
particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)
# known landmark
else:
@@ -254,27 +258,28 @@ def resampling(particles):
pw = np.array(pw)
Neff = 1.0 / (pw @ pw.T) # Effective particle number
n_eff = 1.0 / (pw @ pw.T) # Effective particle number
if Neff < NTH: # resampling
wcum = np.cumsum(pw)
if n_eff < NTH: # resampling
w_cum = np.cumsum(pw)
base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
resamplei_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
inds = []
ind = 0
for ip in range(N_PARTICLE):
while (ind < wcum.shape[0] - 1) and (resamplei_id[ip] > wcum[ind]):
while (ind < w_cum.shape[0] - 1) \
and (resample_id[ip] > w_cum[ind]):
ind += 1
inds.append(ind)
tparticles = particles[:]
tmp_particles = particles[:]
for i in range(len(inds)):
particles[i].x = tparticles[inds[i]].x
particles[i].y = tparticles[inds[i]].y
particles[i].yaw = tparticles[inds[i]].yaw
particles[i].lm = tparticles[inds[i]].lm[:, :]
particles[i].lmP = tparticles[inds[i]].lmP[:, :]
particles[i].x = tmp_particles[inds[i]].x
particles[i].y = tmp_particles[inds[i]].y
particles[i].yaw = tmp_particles[inds[i]].yaw
particles[i].lm = tmp_particles[inds[i]].lm[:, :]
particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
particles[i].w = 1.0 / N_PARTICLE
return particles
@@ -283,12 +288,12 @@ def resampling(particles):
def calc_input(time):
if time <= 3.0: # wait at first
v = 0.0
yawrate = 0.0
yaw_rate = 0.0
else:
v = 1.0 # [m/s]
yawrate = 0.1 # [rad/s]
yaw_rate = 0.1 # [rad/s]
u = np.array([v, yawrate]).reshape(2, 1)
u = np.array([v, yaw_rate]).reshape(2, 1)
return u
@@ -308,13 +313,15 @@ def observation(xTrue, xd, u, RFID):
angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
if d <= MAX_RANGE:
dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise
anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise
zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
angle_noise = np.random.randn() * Q_sim[1, 1] ** 0.5
angle_with_noise = angle + angle_noise # add noise
zi = np.array([dn, pi_2_pi(angle_with_noise), i]).reshape(3, 1)
z = np.hstack((z, zi))
# add noise to input
ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
ud2 = u[1, 0] + np.random.randn() * R_sim[
1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
ud = np.array([ud1, ud2]).reshape(2, 1)
xd = motion_model(xd, ud)
@@ -357,7 +364,7 @@ def main():
[-5.0, 5.0],
[-10.0, 15.0]
])
N_LM = RFID.shape[0]
n_landmark = RFID.shape[0]
# State Vector [x y yaw v]'
xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation
@@ -369,7 +376,7 @@ def main():
hxTrue = xTrue
hxDR = xTrue
particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
while SIM_TIME >= time:
time += DT
@@ -391,14 +398,15 @@ def main():
if show_animation: # pragma: no cover
plt.cla()
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.gcf().canvas.mpl_connect(
'key_release_event',
lambda event: [exit(0) if event.key == 'escape' else None])
plt.plot(RFID[:, 0], RFID[:, 1], "*k")
for iz in range(len(z[:, 0])):
lmid = int(z[2, iz])
plt.plot([xEst[0], RFID[lmid, 0]], [
xEst[1], RFID[lmid, 1]], "-k")
landmark_id = int(z[2, iz])
plt.plot([xEst[0], RFID[landmark_id, 0]], [
xEst[1], RFID[landmark_id, 1]], "-k")
for i in range(N_PARTICLE):
plt.plot(particles[i].x, particles[i].y, ".r")