refactor to remove all pylint errors
Varun Agrawal
parent
e320bfa3b2
commit
67a26c1f93
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@ -10,28 +10,30 @@ A script validating and demonstrating the ImuFactor inference.
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Author: Frank Dellaert, Varun Agrawal
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"""
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# pylint: disable=no-name-in-module,unused-import,arguments-differ
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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 mpl_toolkits.mplot3d import Axes3D
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import gtsam
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from 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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from PreintegrationExample import POSES_FIG, PreintegrationExample
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BIAS_KEY = B(0)
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np.set_printoptions(precision=3, suppress=True)
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class ImuFactorExample(PreintegrationExample):
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"""Class to run example of the Imu Factor."""
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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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@ -42,9 +44,8 @@ class ImuFactorExample(PreintegrationExample):
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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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sick_twist=(np.array([math.radians(30), -math.radians(30),
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0]), self.velocity))
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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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@ -55,19 +56,44 @@ class ImuFactorExample(PreintegrationExample):
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bias, dt)
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def addPrior(self, i, graph):
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"""Add priors at time step `i`."""
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state = self.scenario.navState(i)
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graph.push_back(gtsam.PriorFactorPose3(
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X(i), state.pose(), self.priorNoise))
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graph.push_back(gtsam.PriorFactorVector(
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V(i), state.velocity(), self.velNoise))
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graph.push_back(
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gtsam.PriorFactorPose3(X(i), state.pose(), self.priorNoise))
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graph.push_back(
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gtsam.PriorFactorVector(V(i), state.velocity(), self.velNoise))
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def optimize(self, graph, initial):
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"""Optimize using Levenberg-Marquardt optimization."""
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params = gtsam.LevenbergMarquardtParams()
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params.setVerbosityLM("SUMMARY")
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
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result = optimizer.optimize()
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return result
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def plot(self, result):
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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 + 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 + 1)
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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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def run(self, T=12, compute_covariances=False, verbose=True):
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"""Main runner."""
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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 # 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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@ -91,14 +117,13 @@ class ImuFactorExample(PreintegrationExample):
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if k % 10 == 0:
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self.plotImu(t, measuredOmega, measuredAcc)
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if (k+1) % int(1 / self.dt) == 0:
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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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factor = gtsam.ImuFactor(X(i), V(i),
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X(i + 1), V(i + 1),
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factor = gtsam.ImuFactor(X(i), V(i), 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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@ -108,34 +133,34 @@ class ImuFactorExample(PreintegrationExample):
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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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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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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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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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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 end
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self.addPrior(num_poses - 1, graph)
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initial.print_("Initial values:")
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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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params.setVerbosityLM("SUMMARY")
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
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result = optimizer.optimize()
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result = self.optimize(graph, initial)
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result.print_("Optimized values:")
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result.print("Optimized values:")
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print("------------------")
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print(graph.error(initial))
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print(graph.error(result))
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print("------------------")
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if compute_covariances:
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# Calculate and print marginal covariances
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@ -148,33 +173,26 @@ class ImuFactorExample(PreintegrationExample):
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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+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+1)
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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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self.plot(result)
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if __name__ == '__main__':
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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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choices=("zero_twist", "forward_twist", "loop_twist",
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"sick_twist"))
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parser.add_argument("--time",
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"-T",
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default=12,
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type=int,
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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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default=False,
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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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ImuFactorExample(args.twist_scenario).run(args.time,
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args.compute_covariances,
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args.verbose)
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