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Moved preintegration into separate example, inherit from it

release/4.3a0
Frank 2016-01-27 12:03:26 -08:00
parent b6ead53c25
commit 1ba304a2e3
2 changed files with 126 additions and 92 deletions
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98
python/gtsam_examples/ImuFactorExample.py
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@ -1,5 +1,5 @@
"""
A script validating the ImuFactor prediction and inference.
A script validating the ImuFactor inference.
"""
import math
@ -10,106 +10,20 @@ from mpl_toolkits.mplot3d import Axes3D
import gtsam
from gtsam_utils import plotPose3
from PreintegrationExample import PreintegrationExample
class ImuFactorExample(object):
@staticmethod
def defaultParams(g):
"""Create default parameters with Z *up* and realistic noise parameters"""
params = gtsam.PreintegrationParams.MakeSharedU(g)
kGyroSigma = math.radians(0.5) / 60 # 0.5 degree ARW
kAccelSigma = 0.1 / 60 # 10 cm VRW
params.gyroscopeCovariance = kGyroSigma ** 2 * np.identity(3, np.float)
params.accelerometerCovariance = kAccelSigma ** 2 * np.identity(3, np.float)
params.integrationCovariance = 0.0000001 ** 2 * np.identity(3, np.float)
return params
def __init__(self):
# setup interactive plotting
plt.ion()
# Setup loop scenario
# Forward velocity 2m/s
# Pitch up with angular velocity 6 degree/sec (negative in FLU)
v = 2
w = math.radians(30)
W = np.array([0, -w, 0])
V = np.array([v, 0, 0])
self.scenario = gtsam.ConstantTwistScenario(W, V)
self.dt = 1e-2
# Calculate time to do 1 loop
self.radius = v / w
self.timeForOneLoop = 2.0 * math.pi / w
self.labels = list('xyz')
self.colors = list('rgb')
# Create runner
self.g = 10 # simple gravity constant
self.params = self.defaultParams(self.g)
ptr = gtsam.ScenarioPointer(self.scenario)
accBias = np.array([0, 0.1, 0])
gyroBias = np.array([0, 0, 0])
self.actualBias = gtsam.ConstantBias(accBias, gyroBias)
print(self.actualBias)
self.runner = gtsam.ScenarioRunner(ptr, self.params, self.dt, self.actualBias)
def plot(self, t, measuredOmega, measuredAcc):
# plot angular velocity
omega_b = self.scenario.omega_b(t)
plt.figure(1)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(3, 1, i + 1)
plt.scatter(t, omega_b[i], color='k', marker='.')
plt.scatter(t, measuredOmega[i], color=color, marker='.')
plt.xlabel(label)
# plot acceleration in nav
plt.figure(2)
acceleration_n = self.scenario.acceleration_n(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(3, 1, i + 1)
plt.scatter(t, acceleration_n[i], color=color, marker='.')
plt.xlabel(label)
# plot acceleration in body
plt.figure(3)
acceleration_b = self.scenario.acceleration_b(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(3, 1, i + 1)
plt.scatter(t, acceleration_b[i], color=color, marker='.')
plt.xlabel(label)
# plot ground truth pose, as well as prediction from integrated IMU measurements
actualPose = self.scenario.pose(t)
plotPose3(4, actualPose, 0.3)
pim = self.runner.integrate(t, self.actualBias, True)
predictedNavState = self.runner.predict(pim, self.actualBias)
plotPose3(4, predictedNavState.pose(), 0.1)
ax = plt.gca()
ax.set_xlim3d(-self.radius, self.radius)
ax.set_ylim3d(-self.radius, self.radius)
ax.set_zlim3d(0, self.radius * 2)
# plot actual specific force, as well as corrupted
plt.figure(5)
actual = self.runner.actualSpecificForce(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(3, 1, i + 1)
plt.scatter(t, actual[i], color='k', marker='.')
plt.scatter(t, measuredAcc[i], color=color, marker='.')
plt.xlabel(label)
plt.pause(0.01)
class ImuFactorExample(PreintegrationExample):
def run(self):
# simulate the loop up to the top
T = self.timeForOneLoop
pim = gtsam.PreintegratedImuMeasurements(self.params, self.actualBias)
for i, t in enumerate(np.arange(0, T, self.dt)):
measuredOmega = self.runner.measuredAngularVelocity(t)
measuredAcc = self.runner.measuredSpecificForce(t)
if i % 25 == 0:
self.plot(t, measuredOmega, measuredAcc)
self.plotImu(t, measuredOmega, measuredAcc)
self.plotGroundTruthPose(t)
plt.ioff()
plt.show()

120
python/gtsam_examples/PreintegrationExample.py Normal file
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@ -0,0 +1,120 @@
"""
A script validating the Preintegration of IMU measurements
"""
import math
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import gtsam
from gtsam_utils import plotPose3
IMU_FIG = 1
GROUND_TRUTH_FIG = 2
class PreintegrationExample(object):
@staticmethod
def defaultParams(g):
"""Create default parameters with Z *up* and realistic noise parameters"""
params = gtsam.PreintegrationParams.MakeSharedU(g)
kGyroSigma = math.radians(0.5) / 60 # 0.5 degree ARW
kAccelSigma = 0.1 / 60 # 10 cm VRW
params.gyroscopeCovariance = kGyroSigma ** 2 * np.identity(3, np.float)
params.accelerometerCovariance = kAccelSigma ** 2 * np.identity(3, np.float)
params.integrationCovariance = 0.0000001 ** 2 * np.identity(3, np.float)
return params
def __init__(self):
# setup interactive plotting
plt.ion()
# Setup loop scenario
# Forward velocity 2m/s
# Pitch up with angular velocity 6 degree/sec (negative in FLU)
v = 2
w = math.radians(30)
W = np.array([0, -w, 0])
V = np.array([v, 0, 0])
self.scenario = gtsam.ConstantTwistScenario(W, V)
self.dt = 1e-2
# Calculate time to do 1 loop
self.radius = v / w
self.timeForOneLoop = 2.0 * math.pi / w
self.labels = list('xyz')
self.colors = list('rgb')
# Create runner
self.g = 10 # simple gravity constant
self.params = self.defaultParams(self.g)
ptr = gtsam.ScenarioPointer(self.scenario)
accBias = np.array([0, 0.1, 0])
gyroBias = np.array([0, 0, 0])
self.actualBias = gtsam.ConstantBias(accBias, gyroBias)
self.runner = gtsam.ScenarioRunner(ptr, self.params, self.dt, self.actualBias)
def plotImu(self, t, measuredOmega, measuredAcc):
plt.figure(IMU_FIG)
# plot angular velocity
omega_b = self.scenario.omega_b(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(4, 3, i + 1)
plt.scatter(t, omega_b[i], color='k', marker='.')
plt.scatter(t, measuredOmega[i], color=color, marker='.')
plt.xlabel('angular velocity ' + label)
# plot acceleration in nav
acceleration_n = self.scenario.acceleration_n(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(4, 3, i + 4)
plt.scatter(t, acceleration_n[i], color=color, marker='.')
plt.xlabel('acceleration in nav ' + label)
# plot acceleration in body
acceleration_b = self.scenario.acceleration_b(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(4, 3, i + 7)
plt.scatter(t, acceleration_b[i], color=color, marker='.')
plt.xlabel('acceleration in body ' + label)
# plot actual specific force, as well as corrupted
actual = self.runner.actualSpecificForce(t)
for i, (label, color) in enumerate(zip(self.labels, self.colors)):
plt.subplot(4, 3, i + 10)
plt.scatter(t, actual[i], color='k', marker='.')
plt.scatter(t, measuredAcc[i], color=color, marker='.')
plt.xlabel('specific force ' + label)
def plotGroundTruthPose(self, t):
# plot ground truth pose, as well as prediction from integrated IMU measurements
actualPose = self.scenario.pose(t)
plotPose3(GROUND_TRUTH_FIG, actualPose, 0.3)
ax = plt.gca()
ax.set_xlim3d(-self.radius, self.radius)
ax.set_ylim3d(-self.radius, self.radius)
ax.set_zlim3d(0, self.radius * 2)
plt.pause(0.01)
def run(self):
# simulate the loop up to the top
T = self.timeForOneLoop
for i, t in enumerate(np.arange(0, T, self.dt)):
measuredOmega = self.runner.measuredAngularVelocity(t)
measuredAcc = self.runner.measuredSpecificForce(t)
if i % 25 == 0:
self.plotImu(t, measuredOmega, measuredAcc)
self.plotGroundTruthPose(t)
pim = self.runner.integrate(t, self.actualBias, True)
predictedNavState = self.runner.predict(pim, self.actualBias)
plotPose3(GROUND_TRUTH_FIG, predictedNavState.pose(), 0.1)
plt.ioff()
plt.show()
if __name__ == '__main__':
PreintegrationExample().run()