keys.push_back(2)
print 'size: ', keys.size()
print keys.at(0)
print keys.at(1)
noise = gtsam.noiseModel_Isotropic.Precision(2, 3.0)
noise.print_('noise:')
print 'noise print:', noise
f = gtsam.JacobianFactor(7, np.ones([2, 2]), model=noise, b=np.ones(2))
print 'JacobianFactor(7):\n', f
print "A = ", f.getA()
print "b = ", f.getb()
f = gtsam.JacobianFactor(np.ones(2))
f.print_('jacoboian b_in:')
print "JacobianFactor initalized with b_in:", f
diag = gtsam.noiseModel_Diagonal.Sigmas(np.array([1., 2., 3.]))
fv = gtsam.PriorFactorVector(1, np.array([4., 5., 6.]), diag)
print "priorfactorvector: ", fv
print "base noise: ", fv.get_noiseModel()
print "casted to gaussian2: ", gtsam.dynamic_cast_noiseModel_Diagonal_noiseModel_Base(
fv.get_noiseModel())
X = gtsam.symbol(65, 19)
print X
print gtsam.symbolChr(X)
print gtsam.symbolIndex(X)
def mhjcbb(sim,
num_tracks=10,
prob=0.95,
posterior_pose_md_threshold=1.5,
prune2_skip=10,
max_observed_diff=3):
slams = [[gtsam.ISAM2(), set()]]
prune2_count = 1
observed = set()
for x, (odom, obs) in enumerate(sim.step()):
for isam2, observed in slams:
graph = gtsam.NonlinearFactorGraph()
values = gtsam.Values()
if x == 0:
prior_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_x, sim.sigma_y, sim.sigma_theta]))
prior_factor = gtsam.PriorFactorPose2(X(0), odom, prior_model)
graph.add(prior_factor)
values.insert(X(0), odom)
else:
odom_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_x, sim.sigma_y, sim.sigma_theta]))
odom_factor = gtsam.BetweenFactorPose2(X(x - 1), X(x), odom,
odom_model)
graph.add(odom_factor)
pose0 = isam2.calculateEstimatePose2(X(x - 1))
values.insert(X(x), pose0.compose(odom))
isam2.update(graph, values)
################################################################################
mhjcbb = gtsam.da_MHJCBB2(num_tracks, prob,
posterior_pose_md_threshold)
for isam2, observed, in slams:
mhjcbb.initialize(isam2)
for l, br in obs.items():
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
mhjcbb.add(gtsam.Rot2(br[0]), br[1], br_model)
mhjcbb.match()
################################################################################
new_slams = []
for i in range(mhjcbb.size()):
track, keys = mhjcbb.get(i)
keys = [gtsam.symbolIndex(keys.at(i)) for i in range(keys.size())]
isam2 = gtsam.ISAM2()
isam2.update(slams[track][0].getFactorsUnsafe(),
slams[track][0].calculateEstimate())
graph = gtsam.NonlinearFactorGraph()
values = gtsam.Values()
observed = set(slams[track][1])
for (l_true, br), l in zip(obs.items(), keys):
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
br_factor = gtsam.BearingRangeFactor2D(X(x), L(l),
gtsam.Rot2(br[0]),
br[1], br_model)
graph.add(br_factor)
if l not in observed:
pose1 = isam2.calculateEstimatePose2(X(x))
point = gtsam.Point2(br[1] * np.cos(br[0]),
br[1] * np.sin(br[0]))
values.insert(L(l), pose1.transform_from(point))
observed.add(l)
isam2.update(graph, values)
new_slams.append([isam2, observed])
slams = new_slams
slams = prune1(slams, x, posterior_pose_md_threshold)
if len(slams[0][1]) > prune2_count * prune2_skip:
slams = prune2(slams, max_observed_diff)
prune2_count += 1
result = []
for isam2, observed in slams:
traj_est = [
isam2.calculateEstimatePose2(X(x)) for x in range(len(sim.traj))
]
traj_est = np.array([(p.x(), p.y(), p.theta()) for p in traj_est])
landmark_est = [isam2.calculateEstimatePoint2(L(l)) for l in observed]
landmark_est = np.array([(p.x(), p.y()) for p in landmark_est])
result.append((traj_est, landmark_est))
return result
def from_values(values):
estimate_keys = [values.keys().at(i) for i in range(values.keys().size())]
quadric_keys = [k for k in estimate_keys if chr(gtsam.symbolChr(k)) == 'q']
quadrics = {int(gtsam.symbolIndex(k)): quadricslam.ConstrainedDualQuadric.getFromValues(values, k) for k in quadric_keys}
return Quadrics(quadrics)
def slam(sim, da='jcbb', prob=0.95):
isam2 = gtsam.ISAM2()
graph = gtsam.NonlinearFactorGraph()
values = gtsam.Values()
observed = set()
for x, (odom, obs) in enumerate(sim.step()):
if x == 0:
prior_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_x, sim.sigma_y, sim.sigma_theta]))
prior_factor = gtsam.PriorFactorPose2(X(0), odom, prior_model)
graph.add(prior_factor)
values.insert(X(0), odom)
else:
odom_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_x, sim.sigma_y, sim.sigma_theta]))
odom_factor = gtsam.BetweenFactorPose2(X(x - 1), X(x), odom,
odom_model)
graph.add(odom_factor)
pose0 = isam2.calculateEstimatePose2(X(x - 1))
values.insert(X(x), pose0.compose(odom))
isam2.update(graph, values)
graph.resize(0)
values.clear()
estimate = isam2.calculateEstimate()
if da == 'dr':
for l_true, br in obs.items():
l = len(observed)
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
br_factor = gtsam.BearingRangeFactor2D(X(x), L(l),
gtsam.Rot2(br[0]),
br[1], br_model)
graph.add(br_factor)
if l not in observed:
pose1 = isam2.calculateEstimatePose2(X(x))
point = gtsam.Point2(br[1] * np.cos(br[0]),
br[1] * np.sin(br[0]))
values.insert(L(l), pose1.transform_from(point))
observed.add(l)
elif da == 'perfect':
for l_true, br in obs.items():
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
br_factor = gtsam.BearingRangeFactor2D(X(x), L(l_true),
gtsam.Rot2(br[0]),
br[1], br_model)
graph.add(br_factor)
if l_true not in observed:
pose1 = isam2.calculateEstimatePose2(X(x))
point = gtsam.Point2(br[1] * np.cos(br[0]),
br[1] * np.sin(br[0]))
values.insert(L(l_true), pose1.transform_from(point))
observed.add(l_true)
elif da == 'jcbb':
################################################################################
jcbb = gtsam.da_JCBB2(isam2, prob)
for l, br in obs.items():
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
jcbb.add(gtsam.Rot2(br[0]), br[1], br_model)
keys = jcbb.match()
################################################################################
keys = [gtsam.symbolIndex(keys.at(i)) for i in range(keys.size())]
for (l_true, br), l in zip(obs.items(), keys):
br_model = gtsam.noiseModel_Diagonal.Sigmas(
np.array([sim.sigma_bearing, sim.sigma_range]))
br_factor = gtsam.BearingRangeFactor2D(X(x), L(l),
gtsam.Rot2(br[0]),
br[1], br_model)
graph.add(br_factor)
if l not in observed:
pose1 = isam2.calculateEstimatePose2(X(x))
point = gtsam.Point2(br[1] * np.cos(br[0]),
br[1] * np.sin(br[0]))
values.insert(L(l), pose1.transform_from(point))
observed.add(l)
isam2.update(graph, values)
graph.resize(0)
values.clear()
traj_est = [
isam2.calculateEstimatePose2(X(x)) for x in range(len(sim.traj))
]
traj_est = np.array([(p.x(), p.y(), p.theta()) for p in traj_est])
landmark_est = [isam2.calculateEstimatePoint2(L(l)) for l in observed]
landmark_est = np.array([(p.x(), p.y()) for p in landmark_est])
return [[traj_est, landmark_est]]
def plot_system(self, graph, estimate):
"""
Plots the trajectory and quadrics
Color represents the sum of boundingbox factor errors at each pose
"""
# extract variables and factors
trajectory = Trajectory.from_values(estimate)
quadrics = Quadrics.from_values(estimate)
# collect nonlinear factors from graph
box_factors = [
quadricslam.BoundingBoxFactor.getFromGraph(graph, i)
for i in range(graph.size()) if graph.at(i).keys().size() == 2
and chr(gtsam.symbolChr(graph.at(i).keys().at(1))) == 'q'
]
# odom_factors = [graph.at(i) for i in range(graph.size()) if graph.at(i).keys().size() == 2 and chr(gtsam.symbolChr(graph.at(i).keys().at(1))) == 'x']
# extract x-y positions
xy = np.array([[pose.x(), pose.y()] for pose in trajectory.data()])
# extract error at each pose
errors = []
for pose_key in trajectory.keys():
# get factors for current pose
bbfs = [
f for f in box_factors
if gtsam.symbolIndex(f.keys().at(0)) == pose_key
]
# get sum of each bbf error at pose
error = np.sum([
np.square(bbf.unwhitenedError(estimate)).sum() for bbf in bbfs
])
errors.append(error)
# open and clear figure
figure = plt.figure(self._figname)
figure.clf()
# plot trajectory x-y
plt.plot(xy[:, 0], xy[:, 1], linestyle='-', c='c', zorder=1)
plt.scatter(xy[:, 0], xy[:, 1], s=25, c=errors, zorder=9)
plt.colorbar()
# plot quadrics
for quadric in quadrics.data():
plt.plot(quadric.pose().x(),
quadric.pose().y(),
marker='o',
markersize=5,
c='m',
fillstyle='none')
plt.plot(quadric.pose().x(),
quadric.pose().y(),
marker='o',
markersize=5,
c='m',
alpha=0.5)
# show plot
plt.show()