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Merge pull request #872 from borglab/fix/imu-examples

release/4.3a0
Varun Agrawal 2021-10-29 09:34:09 -04:00 committed by GitHub
parent 0f8353f7ec b15297ae40
commit 41dc3f876b
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3 changed files with 133 additions and 62 deletions
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2
gtsam/navigation/navigation.i
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@ -88,6 +88,8 @@ virtual class PreintegratedRotationParams {
virtual class PreintegrationParams : gtsam::PreintegratedRotationParams {
PreintegrationParams(Vector n_gravity);
gtsam::Vector n_gravity;
static gtsam::PreintegrationParams* MakeSharedD(double g);
static gtsam::PreintegrationParams* MakeSharedU(double g);
static gtsam::PreintegrationParams* MakeSharedD(); // default g = 9.81

114
python/gtsam/examples/ImuFactorExample.py
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@ -10,31 +10,51 @@ A script validating and demonstrating the ImuFactor inference.
Author: Frank Dellaert, Varun Agrawal
"""
# pylint: disable=no-name-in-module,unused-import,arguments-differ
# pylint: disable=no-name-in-module,unused-import,arguments-differ,import-error,wrong-import-order
from __future__ import print_function
import argparse
import math
import gtsam
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import gtsam
from gtsam.symbol_shorthand import B, V, X
from gtsam.utils.plot import plot_pose3
from mpl_toolkits.mplot3d import Axes3D
from PreintegrationExample import POSES_FIG, PreintegrationExample
BIAS_KEY = B(0)
GRAVITY = 9.81
np.set_printoptions(precision=3, suppress=True)
def parse_args() -> argparse.Namespace:
"""Parse command line arguments."""
parser = argparse.ArgumentParser("ImuFactorExample.py")
parser.add_argument("--twist_scenario",
default="sick_twist",
choices=("zero_twist", "forward_twist", "loop_twist",
"sick_twist"))
parser.add_argument("--time",
"-T",
default=12,
type=int,
help="Total navigation time in seconds")
parser.add_argument("--compute_covariances",
default=False,
action='store_true')
parser.add_argument("--verbose", default=False, action='store_true')
args = parser.parse_args()
return args
class ImuFactorExample(PreintegrationExample):
"""Class to run example of the Imu Factor."""
def __init__(self, twist_scenario="sick_twist"):
def __init__(self, twist_scenario: str = "sick_twist"):
self.velocity = np.array([2, 0, 0])
self.priorNoise = gtsam.noiseModel.Isotropic.Sigma(6, 0.1)
self.velNoise = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
@ -51,19 +71,30 @@ class ImuFactorExample(PreintegrationExample):
gyroBias = np.array([0.1, 0.3, -0.1])
bias = gtsam.imuBias.ConstantBias(accBias, gyroBias)
params = gtsam.PreintegrationParams.MakeSharedU(GRAVITY)
# Some arbitrary noise sigmas
gyro_sigma = 1e-3
accel_sigma = 1e-3
I_3x3 = np.eye(3)
params.setGyroscopeCovariance(gyro_sigma**2 * I_3x3)
params.setAccelerometerCovariance(accel_sigma**2 * I_3x3)
params.setIntegrationCovariance(1e-7**2 * I_3x3)
dt = 1e-2
super(ImuFactorExample, self).__init__(twist_scenarios[twist_scenario],
bias, dt)
bias, params, dt)
def addPrior(self, i, graph):
"""Add priors at time step `i`."""
def addPrior(self, i: int, graph: gtsam.NonlinearFactorGraph):
"""Add a prior on the navigation state at time `i`."""
state = self.scenario.navState(i)
graph.push_back(
gtsam.PriorFactorPose3(X(i), state.pose(), self.priorNoise))
graph.push_back(
gtsam.PriorFactorVector(V(i), state.velocity(), self.velNoise))
def optimize(self, graph, initial):
def optimize(self, graph: gtsam.NonlinearFactorGraph,
initial: gtsam.Values):
"""Optimize using Levenberg-Marquardt optimization."""
params = gtsam.LevenbergMarquardtParams()
params.setVerbosityLM("SUMMARY")
@ -71,24 +102,49 @@ class ImuFactorExample(PreintegrationExample):
result = optimizer.optimize()
return result
def plot(self, result):
"""Plot resulting poses."""
def plot(self,
values: gtsam.Values,
title: str = "Estimated Trajectory",
fignum: int = POSES_FIG + 1,
show: bool = False):
"""
Plot poses in values.
Args:
values: The values object with the poses to plot.
title: The title of the plot.
fignum: The matplotlib figure number.
POSES_FIG is a value from the PreintegrationExample which we simply increment to generate a new figure.
show: Flag indicating whether to display the figure.
"""
i = 0
while result.exists(X(i)):
pose_i = result.atPose3(X(i))
plot_pose3(POSES_FIG + 1, pose_i, 1)
while values.exists(X(i)):
pose_i = values.atPose3(X(i))
plot_pose3(fignum, pose_i, 1)
i += 1
plt.title("Estimated Trajectory")
plt.title(title)
gtsam.utils.plot.set_axes_equal(POSES_FIG + 1)
gtsam.utils.plot.set_axes_equal(fignum)
print("Bias Values", result.atConstantBias(BIAS_KEY))
print("Bias Values", values.atConstantBias(BIAS_KEY))
plt.ioff()
plt.show()
def run(self, T=12, compute_covariances=False, verbose=True):
"""Main runner."""
if show:
plt.show()
def run(self,
T: int = 12,
compute_covariances: bool = False,
verbose: bool = True):
"""
Main runner.
Args:
T: Total trajectory time.
compute_covariances: Flag indicating whether to compute marginal covariances.
verbose: Flag indicating if printing should be verbose.
"""
graph = gtsam.NonlinearFactorGraph()
# initialize data structure for pre-integrated IMU measurements
@ -173,25 +229,11 @@ class ImuFactorExample(PreintegrationExample):
print("Covariance on vel {}:\n{}\n".format(
i, marginals.marginalCovariance(V(i))))
self.plot(result)
self.plot(result, show=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser("ImuFactorExample.py")
parser.add_argument("--twist_scenario",
default="sick_twist",
choices=("zero_twist", "forward_twist", "loop_twist",
"sick_twist"))
parser.add_argument("--time",
"-T",
default=12,
type=int,
help="Total time in seconds")
parser.add_argument("--compute_covariances",
default=False,
action='store_true')
parser.add_argument("--verbose", default=False, action='store_true')
args = parser.parse_args()
args = parse_args()
ImuFactorExample(args.twist_scenario).run(args.time,
args.compute_covariances,

79
python/gtsam/examples/PreintegrationExample.py
View File

@ -5,10 +5,14 @@ All Rights Reserved
See LICENSE for the license information
A script validating the Preintegration of IMU measurements
A script validating the Preintegration of IMU measurements.
Authors: Frank Dellaert, Varun Agrawal.
"""
import math
# pylint: disable=invalid-name,unused-import,wrong-import-order
from typing import Optional, Sequence
import gtsam
import matplotlib.pyplot as plt
@ -18,25 +22,28 @@ from mpl_toolkits.mplot3d import Axes3D
IMU_FIG = 1
POSES_FIG = 2
GRAVITY = 10
class PreintegrationExample(object):
class PreintegrationExample:
"""Base class for all preintegration examples."""
@staticmethod
def defaultParams(g):
def defaultParams(g: float):
"""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
kGyroSigma = np.radians(0.5) / 60 # 0.5 degree ARW
kAccelSigma = 0.1 / 60 # 10 cm VRW
params.setGyroscopeCovariance(
kGyroSigma ** 2 * np.identity(3, float))
params.setAccelerometerCovariance(
kAccelSigma ** 2 * np.identity(3, float))
params.setIntegrationCovariance(
0.0000001 ** 2 * np.identity(3, float))
params.setGyroscopeCovariance(kGyroSigma**2 * np.identity(3, float))
params.setAccelerometerCovariance(kAccelSigma**2 *
np.identity(3, float))
params.setIntegrationCovariance(0.0000001**2 * np.identity(3, float))
return params
def __init__(self, twist=None, bias=None, dt=1e-2):
def __init__(self,
twist: Optional[np.ndarray] = None,
bias: Optional[gtsam.imuBias.ConstantBias] = None,
params: Optional[gtsam.PreintegrationParams] = None,
dt: float = 1e-2):
"""Initialize with given twist, a pair(angularVelocityVector, velocityVector)."""
# setup interactive plotting
@ -48,7 +55,7 @@ class PreintegrationExample(object):
else:
# default = loop with forward velocity 2m/s, while pitching up
# with angular velocity 30 degree/sec (negative in FLU)
W = np.array([0, -math.radians(30), 0])
W = np.array([0, -np.radians(30), 0])
V = np.array([2, 0, 0])
self.scenario = gtsam.ConstantTwistScenario(W, V)
@ -58,9 +65,11 @@ class PreintegrationExample(object):
self.labels = list('xyz')
self.colors = list('rgb')
# Create runner
self.g = 10 # simple gravity constant
self.params = self.defaultParams(self.g)
if params:
self.params = params
else:
# Default params with simple gravity constant
self.params = self.defaultParams(g=GRAVITY)
if bias is not None:
self.actualBias = bias
@ -69,13 +78,22 @@ class PreintegrationExample(object):
gyroBias = np.array([0, 0, 0])
self.actualBias = gtsam.imuBias.ConstantBias(accBias, gyroBias)
self.runner = gtsam.ScenarioRunner(
self.scenario, self.params, self.dt, self.actualBias)
# Create runner
self.runner = gtsam.ScenarioRunner(self.scenario, self.params, self.dt,
self.actualBias)
fig, self.axes = plt.subplots(4, 3)
fig.set_tight_layout(True)
def plotImu(self, t, measuredOmega, measuredAcc):
def plotImu(self, t: float, measuredOmega: Sequence,
measuredAcc: Sequence):
"""
Plot IMU measurements.
Args:
t: The time at which the measurement was recoreded.
measuredOmega: Measured angular velocity.
measuredAcc: Measured linear acceleration.
"""
plt.figure(IMU_FIG)
# plot angular velocity
@ -108,12 +126,21 @@ class PreintegrationExample(object):
ax.scatter(t, measuredAcc[i], color=color, marker='.')
ax.set_xlabel('specific force ' + label)
def plotGroundTruthPose(self, t, scale=0.3, time_interval=0.01):
# plot ground truth pose, as well as prediction from integrated IMU measurements
def plotGroundTruthPose(self,
t: float,
scale: float = 0.3,
time_interval: float = 0.01):
"""
Plot ground truth pose, as well as prediction from integrated IMU measurements.
Args:
t: Time at which the pose was obtained.
scale: The scaling factor for the pose axes.
time_interval: The time to wait before showing the plot.
"""
actualPose = self.scenario.pose(t)
plot_pose3(POSES_FIG, actualPose, scale)
t = actualPose.translation()
self.maxDim = max([max(np.abs(t)), self.maxDim])
translation = actualPose.translation()
self.maxDim = max([max(np.abs(translation)), self.maxDim])
ax = plt.gca()
ax.set_xlim3d(-self.maxDim, self.maxDim)
ax.set_ylim3d(-self.maxDim, self.maxDim)
@ -121,8 +148,8 @@ class PreintegrationExample(object):
plt.pause(time_interval)
def run(self, T=12):
# simulate the loop
def run(self, T: int = 12):
"""Simulate the loop."""
for i, t in enumerate(np.arange(0, T, self.dt)):
measuredOmega = self.runner.measuredAngularVelocity(t)
measuredAcc = self.runner.measuredSpecificForce(t)