update ImuFactorExample.py
Varun Agrawal
parent
bc95b41efc
commit
7114cf93d3
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@ -12,15 +12,17 @@ Author: Frank Dellaert, Varun Agrawal
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from __future__ import print_function
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import argparse
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import math
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import gtsam
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import matplotlib.pyplot as plt
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import numpy as np
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from gtsam.gtsam.symbol_shorthand import B, V, X
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from gtsam.utils.plot import plot_pose3
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from mpl_toolkits.mplot3d import Axes3D
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import numpy as np
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import gtsam
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from gtsam.gtsam.symbol_shorthand import B, V, X
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from gtsam.utils.plot import plot_pose3
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from PreintegrationExample import POSES_FIG, PreintegrationExample
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@ -32,24 +34,27 @@ np.set_printoptions(precision=3, suppress=True)
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class ImuFactorExample(PreintegrationExample):
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def __init__(self):
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def __init__(self, twist_scenario="sick_twist"):
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self.velocity = np.array([2, 0, 0])
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self.priorNoise = gtsam.noiseModel.Isotropic.Sigma(6, 0.1)
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self.velNoise = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
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# Choose one of these twists to change scenario:
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zero_twist = (np.zeros(3), np.zeros(3))
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forward_twist = (np.zeros(3), self.velocity)
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loop_twist = (np.array([0, -math.radians(30), 0]), self.velocity)
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sick_twist = (
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np.array([math.radians(30), -math.radians(30), 0]), self.velocity)
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twist_scenarios = dict(
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zero_twist=(np.zeros(3), np.zeros(3)),
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forward_twist=(np.zeros(3), self.velocity),
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loop_twist=(np.array([0, -math.radians(30), 0]), self.velocity),
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sick_twist=(np.array([math.radians(30), -math.radians(30), 0]),
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self.velocity)
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)
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accBias = np.array([-0.3, 0.1, 0.2])
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gyroBias = np.array([0.1, 0.3, -0.1])
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bias = gtsam.imuBias.ConstantBias(accBias, gyroBias)
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dt = 1e-2
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super(ImuFactorExample, self).__init__(sick_twist, bias, dt)
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super(ImuFactorExample, self).__init__(twist_scenarios[twist_scenario],
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bias, dt)
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def addPrior(self, i, graph):
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state = self.scenario.navState(i)
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@ -58,65 +63,73 @@ class ImuFactorExample(PreintegrationExample):
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graph.push_back(gtsam.PriorFactorVector(
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V(i), state.velocity(), self.velNoise))
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def run(self):
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def run(self, T=12, compute_covariances=False, verbose=True):
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graph = gtsam.NonlinearFactorGraph()
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# initialize data structure for pre-integrated IMU measurements
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pim = gtsam.PreintegratedImuMeasurements(self.params, self.actualBias)
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T = 12
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num_poses = T + 1 # assumes 1 factor per second
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num_poses = T # assumes 1 factor per second
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initial = gtsam.Values()
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initial.insert(BIAS_KEY, self.actualBias)
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for i in range(num_poses):
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state_i = self.scenario.navState(float(i))
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poseNoise = gtsam.Pose3.Expmap(np.random.randn(3)*0.1)
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pose = state_i.pose().compose(poseNoise)
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velocity = state_i.velocity() + np.random.randn(3)*0.1
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initial.insert(X(i), pose)
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initial.insert(V(i), velocity)
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# simulate the loop
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i = 0 # state index
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actual_state_i = self.scenario.navState(0)
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initial_state_i = self.scenario.navState(0)
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initial.insert(X(i), initial_state_i.pose())
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initial.insert(V(i), initial_state_i.velocity())
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# add prior on beginning
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self.addPrior(0, graph)
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for k, t in enumerate(np.arange(0, T, self.dt)):
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# get measurements and add them to PIM
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measuredOmega = self.runner.measuredAngularVelocity(t)
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measuredAcc = self.runner.measuredSpecificForce(t)
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pim.integrateMeasurement(measuredAcc, measuredOmega, self.dt)
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poseNoise = gtsam.Pose3.Expmap(np.random.randn(3)*0.1)
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actual_state_i = gtsam.NavState(
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actual_state_i.pose().compose(poseNoise),
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actual_state_i.velocity() + np.random.randn(3)*0.1)
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# Plot IMU many times
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if k % 10 == 0:
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self.plotImu(t, measuredOmega, measuredAcc)
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# Plot every second
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if k % int(1 / self.dt) == 0:
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self.plotGroundTruthPose(t)
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if (k+1) % int(1 / self.dt) == 0:
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# Plot every second
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self.plotGroundTruthPose(t, scale=1)
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plt.title("Ground Truth Trajectory")
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# create IMU factor every second
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if (k + 1) % int(1 / self.dt) == 0:
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factor = gtsam.ImuFactor(X(i), V(i), X(
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i + 1), V(i + 1), BIAS_KEY, pim)
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# create IMU factor every second
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factor = gtsam.ImuFactor(X(i), V(i),
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X(i + 1), V(i + 1),
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BIAS_KEY, pim)
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graph.push_back(factor)
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if True:
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if verbose:
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print(factor)
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print(pim.predict(actual_state_i, self.actualBias))
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pim.resetIntegration()
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rotationNoise = gtsam.Rot3.Expmap(np.random.randn(3)*0.1)
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translationNoise = gtsam.Point3(*np.random.randn(3)*1)
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poseNoise = gtsam.Pose3(rotationNoise, translationNoise)
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actual_state_i = self.scenario.navState(t + self.dt)
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print("Actual state at {0}:\n{1}".format(
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t+self.dt, actual_state_i))
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noisy_state_i = gtsam.NavState(
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actual_state_i.pose().compose(poseNoise),
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actual_state_i.velocity() + np.random.randn(3)*0.1)
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initial.insert(X(i+1), noisy_state_i.pose())
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initial.insert(V(i+1), noisy_state_i.velocity())
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i += 1
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# add priors on beginning and end
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self.addPrior(0, graph)
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self.addPrior(num_poses - 1, graph)
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# add priors on end
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# self.addPrior(num_poses - 1, graph)
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initial.print_("Initial values:")
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# optimize using Levenberg-Marquardt optimization
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params = gtsam.LevenbergMarquardtParams()
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@ -124,29 +137,46 @@ class ImuFactorExample(PreintegrationExample):
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
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result = optimizer.optimize()
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# Calculate and print marginal covariances
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marginals = gtsam.Marginals(graph, result)
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print("Covariance on bias:\n", marginals.marginalCovariance(BIAS_KEY))
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for i in range(num_poses):
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print("Covariance on pose {}:\n{}\n".format(
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i, marginals.marginalCovariance(X(i))))
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print("Covariance on vel {}:\n{}\n".format(
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i, marginals.marginalCovariance(V(i))))
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result.print_("Optimized values:")
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if compute_covariances:
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# Calculate and print marginal covariances
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marginals = gtsam.Marginals(graph, result)
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print("Covariance on bias:\n",
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marginals.marginalCovariance(BIAS_KEY))
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for i in range(num_poses):
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print("Covariance on pose {}:\n{}\n".format(
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i, marginals.marginalCovariance(X(i))))
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print("Covariance on vel {}:\n{}\n".format(
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i, marginals.marginalCovariance(V(i))))
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# Plot resulting poses
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i = 0
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while result.exists(X(i)):
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pose_i = result.atPose3(X(i))
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plot_pose3(POSES_FIG, pose_i, 0.1)
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plot_pose3(POSES_FIG+1, pose_i, 1)
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i += 1
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plt.title("Estimated Trajectory")
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gtsam.utils.plot.set_axes_equal(POSES_FIG)
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gtsam.utils.plot.set_axes_equal(POSES_FIG+1)
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print(result.atConstantBias(BIAS_KEY))
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print("Bias Values", result.atConstantBias(BIAS_KEY))
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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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parser = argparse.ArgumentParser("ImuFactorExample.py")
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parser.add_argument("--twist_scenario",
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default="sick_twist",
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choices=("zero_twist", "forward_twist", "loop_twist", "sick_twist"))
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parser.add_argument("--time", "-T", default=12,
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type=int, help="Total time in seconds")
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parser.add_argument("--compute_covariances",
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default=False, action='store_true')
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parser.add_argument("--verbose", default=False, action='store_true')
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args = parser.parse_args()
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ImuFactorExample(args.twist_scenario).run(
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args.time, args.compute_covariances, args.verbose)
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