115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
"""
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A script validating the ImuFactor prediction and inference.
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"""
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D
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import gtsam
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from gtsam_utils import plotPose3
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class ImuFactorExample(object):
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@staticmethod
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def defaultParams(g):
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"""Create default parameters with Z *up* and realistic noise parameters"""
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params = gtsam.PreintegrationParams.MakeSharedU(g)
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kGyroSigma = math.radians(0.5) / 60 # 0.5 degree ARW
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kAccelSigma = 0.1 / 60 # 10 cm VRW
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params.gyroscopeCovariance = kGyroSigma ** 2 * np.identity(3, np.float)
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params.accelerometerCovariance = kAccelSigma ** 2 * np.identity(3, np.float)
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params.integrationCovariance = 0.0000001 ** 2 * np.identity(3, np.float)
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return params
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def __init__(self):
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# setup interactive plotting
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plt.ion()
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# Setup loop scenario
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# Forward velocity 2m/s
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# Pitch up with angular velocity 6 degree/sec (negative in FLU)
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v = 2
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w = math.radians(30)
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W = np.array([0, -w, 0])
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V = np.array([v, 0, 0])
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self.scenario = gtsam.ConstantTwistScenario(W, V)
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self.dt = 0.25
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self.realTimeFactor = 10.0
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# Calculate time to do 1 loop
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self.radius = v / w
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self.timeForOneLoop = 2 * math.pi / w
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self.labels = list('xyz')
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self.colors = list('rgb')
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# Create runner
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dt = 0.1
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self.g = 10 # simple gravity constant
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self.params = self.defaultParams(self.g)
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self.runner = gtsam.ScenarioRunner(gtsam.ScenarioPointer(self.scenario), self.params, dt)
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self.estimatedBias = gtsam.ConstantBias()
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def plot(self, t, measuredOmega, measuredAcc):
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# plot angular velocity
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omega_b = self.scenario.omega_b(t)
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plt.figure(1)
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for i, (label, color) in enumerate(zip(self.labels, self.colors)):
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plt.subplot(3, 1, i + 1)
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plt.scatter(t, omega_b[i], color='k', marker='.')
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plt.scatter(t, measuredOmega[i], color=color, marker='.')
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plt.xlabel(label)
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# plot acceleration in nav
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plt.figure(2)
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acceleration_n = self.scenario.acceleration_n(t)
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for i, (label, color) in enumerate(zip(self.labels, self.colors)):
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plt.subplot(3, 1, i + 1)
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plt.scatter(t, acceleration_n[i], color=color, marker='.')
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plt.xlabel(label)
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# plot acceleration in body
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plt.figure(3)
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acceleration_b = self.scenario.acceleration_b(t)
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for i, (label, color) in enumerate(zip(self.labels, self.colors)):
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plt.subplot(3, 1, i + 1)
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plt.scatter(t, acceleration_b[i], color=color, marker='.')
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plt.xlabel(label)
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# plot ground truth pose, as well as prediction from integrated IMU measurements
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actualPose = self.scenario.pose(t)
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plotPose3(4, actualPose, 1.0)
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pim = self.runner.integrate(t, self.estimatedBias, False)
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predictedNavState = self.runner.predict(pim, self.estimatedBias)
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plotPose3(4, predictedNavState.pose(), 1.0)
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ax = plt.gca()
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ax.set_xlim3d(-self.radius, self.radius)
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ax.set_ylim3d(-self.radius, self.radius)
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ax.set_zlim3d(0, self.radius * 2)
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# plot actual specific force, as well as corrupted
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plt.figure(5)
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actual = self.runner.actualSpecificForce(t)
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for i, (label, color) in enumerate(zip(self.labels, self.colors)):
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plt.subplot(3, 1, i + 1)
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plt.scatter(t, actual[i], color='k', marker='.')
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plt.scatter(t, measuredAcc[i], color=color, marker='.')
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plt.xlabel(label)
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plt.pause(self.dt / self.realTimeFactor)
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def run(self):
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# simulate the loop up to the top
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for t in np.arange(0, self.timeForOneLoop, self.dt):
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measuredOmega = self.runner.measuredAngularVelocity(t)
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measuredAcc = self.runner.measuredSpecificForce(t)
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self.plot(t, measuredOmega, measuredAcc)
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plt.ioff()
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plt.show()
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if __name__ == '__main__':
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ImuFactorExample().run()
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