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Fix warning message in the unit tests/examples

release/4.3a0
Jeremy Aguilon 2019-03-06 17:47:34 -05:00
parent efa36f8901
commit 7d2e4d2e64
3 changed files with 91 additions and 70 deletions
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39
cython/gtsam_unstable/examples/FixedLagSmootherExample.py
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@ -19,7 +19,7 @@ import gtsam_unstable
def _timestamp_key_value(key, value):
"""
Creates a key value pair for a FixedLagSmootherKeyTimeStampMap
"""
return gtsam_unstable.FixedLagSmootherKeyTimestampMapValue(
key, value
@ -29,8 +29,7 @@ def _timestamp_key_value(key, value):
def BatchFixedLagSmootherExample():
"""
Runs a batch fixed smoother on an agent with two odometry
sensors that is simply moving along the x axis in constant
speed.
sensors that is simply moving to the
"""
# Define a batch fixed lag smoother, which uses
@ -38,14 +37,12 @@ def BatchFixedLagSmootherExample():
lag = 2.0
smoother_batch = gtsam_unstable.BatchFixedLagSmoother(lag)
# Create containers to store the factors and linearization points
# that will be sent to the smoothers
new_factors = gtsam.NonlinearFactorGraph()
new_values = gtsam.Values()
new_timestamps = gtsam_unstable.FixedLagSmootherKeyTimestampMap()
# Create a prior on the first pose, placing it at the origin
prior_mean = gtsam.Pose2(0, 0, 0)
prior_noise = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
@ -57,9 +54,6 @@ def BatchFixedLagSmootherExample():
delta_time = 0.25
time = 0.25
# Iterates from 0.25s to 3.0s, adding 0.25s each loop
# In each iteration, the agent moves at a constant speed
# and its two odometers measure the change.
while time <= 3.0:
previous_key = 1000 * (time - delta_time)
current_key = 1000 * time
@ -72,32 +66,37 @@ def BatchFixedLagSmootherExample():
current_pose = gtsam.Pose2(time * 2, 0, 0)
new_values.insert(current_key, current_pose)
# Add odometry factors from two different sources with different error stats
# Add odometry factors from two different sources with different error
# stats
odometry_measurement_1 = gtsam.Pose2(0.61, -0.08, 0.02)
odometry_noise_1 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.1, 0.1, 0.05]))
odometry_noise_1 = gtsam.noiseModel_Diagonal.Sigmas(
np.array([0.1, 0.1, 0.05]))
new_factors.push_back(gtsam.BetweenFactorPose2(
previous_key, current_key, odometry_measurement_1, odometry_noise_1
))
odometry_measurement_2 = gtsam.Pose2(0.47, 0.03, 0.01)
odometry_noise_2 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.05, 0.05, 0.05]))
odometry_noise_2 = gtsam.noiseModel_Diagonal.Sigmas(
np.array([0.05, 0.05, 0.05]))
new_factors.push_back(gtsam.BetweenFactorPose2(
previous_key, current_key, odometry_measurement_2, odometry_noise_2
))
# Update the smoothers with the new factors
smoother_batch.update(new_factors, new_values, new_timestamps)
print("Timestamp = " + str(time) + ", Key = " + str(current_key))
print(smoother_batch.calculateEstimatePose2(current_key))
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
# Update the smoothers with the new factors. In this case,
# one iteration must pass for Levenberg-Marquardt to accurately
# estimate
if time >= 0.50:
smoother_batch.update(new_factors, new_values, new_timestamps)
print("Timestamp = " + str(time) + ", Key = " + str(current_key))
print(smoother_batch.calculateEstimatePose2(current_key))
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
time += delta_time
if __name__ == '__main__':
BatchFixedLagSmootherExample()
print("Example complete")

86
cython/gtsam_unstable/tests/test_FixedLagSmootherExample.py
View File

@ -3,10 +3,13 @@ import gtsam
import gtsam_unstable
import numpy as np
def _timestamp_key_value(key, value):
return gtsam_unstable.FixedLagSmootherKeyTimestampMapValue(
key, value
)
class TestFixedLagSmootherExample(unittest.TestCase):
'''
Tests the fixed lag smoother wrapper
@ -23,19 +26,20 @@ class TestFixedLagSmootherExample(unittest.TestCase):
lag = 2.0
smoother_batch = gtsam_unstable.BatchFixedLagSmoother(lag)
# Create containers to store the factors and linearization points
# that will be sent to the smoothers
new_factors = gtsam.NonlinearFactorGraph()
new_values = gtsam.Values()
new_timestamps = gtsam_unstable.FixedLagSmootherKeyTimestampMap()
# Create a prior on the first pose, placing it at the origin
prior_mean = gtsam.Pose2(0, 0, 0)
prior_noise = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
prior_noise = gtsam.noiseModel_Diagonal.Sigmas(
np.array([0.3, 0.3, 0.1]))
X1 = 0
new_factors.push_back(gtsam.PriorFactorPose2(X1, prior_mean, prior_noise))
new_factors.push_back(
gtsam.PriorFactorPose2(
X1, prior_mean, prior_noise))
new_values.insert(X1, prior_mean)
new_timestamps.insert(_timestamp_key_value(X1, 0.0))
@ -45,19 +49,19 @@ class TestFixedLagSmootherExample(unittest.TestCase):
i = 0
ground_truth = [
gtsam.Pose2(0.49792, 0.007802, 0.015),
gtsam.Pose2(0.99547, 0.023019, 0.03),
gtsam.Pose2(1.4928, 0.045725, 0.045),
gtsam.Pose2(1.9898, 0.075888, 0.06),
gtsam.Pose2(2.4863, 0.1135, 0.075),
gtsam.Pose2(2.9821, 0.15856, 0.09),
gtsam.Pose2(3.4772, 0.21105, 0.105),
gtsam.Pose2(3.9715, 0.27096, 0.12),
gtsam.Pose2(4.4648, 0.33827, 0.135),
gtsam.Pose2(4.957, 0.41298, 0.15),
gtsam.Pose2(5.4481, 0.49506, 0.165),
gtsam.Pose2(5.9379, 0.5845, 0.18),
gtsam.Pose2(0.995821, 0.0231012, 0.0300001),
gtsam.Pose2(1.49284, 0.0457247, 0.045),
gtsam.Pose2(1.98981, 0.0758879, 0.06),
gtsam.Pose2(2.48627, 0.113502, 0.075),
gtsam.Pose2(2.98211, 0.158558, 0.09),
gtsam.Pose2(3.47722, 0.211047, 0.105),
gtsam.Pose2(3.97149, 0.270956, 0.12),
gtsam.Pose2(4.4648, 0.338272, 0.135),
gtsam.Pose2(4.95705, 0.41298, 0.15),
gtsam.Pose2(5.44812, 0.495063, 0.165),
gtsam.Pose2(5.9379, 0.584503, 0.18),
]
# Iterates from 0.25s to 3.0s, adding 0.25s each loop
# In each iteration, the agent moves at a constant speed
# and its two odometers measure the change. The smoothed
@ -70,35 +74,49 @@ class TestFixedLagSmootherExample(unittest.TestCase):
new_timestamps.insert(_timestamp_key_value(current_key, time))
# Add a guess for this pose to the new values
# Assume that the robot moves at 2 m/s. Position is time[s] * 2[m/s]
# Assume that the robot moves at 2 m/s. Position is time[s] *
# 2[m/s]
current_pose = gtsam.Pose2(time * 2, 0, 0)
new_values.insert(current_key, current_pose)
# Add odometry factors from two different sources with different error stats
# Add odometry factors from two different sources with different
# error stats
odometry_measurement_1 = gtsam.Pose2(0.61, -0.08, 0.02)
odometry_noise_1 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.1, 0.1, 0.05]))
new_factors.push_back(gtsam.BetweenFactorPose2(
previous_key, current_key, odometry_measurement_1, odometry_noise_1
))
odometry_noise_1 = gtsam.noiseModel_Diagonal.Sigmas(
np.array([0.1, 0.1, 0.05]))
new_factors.push_back(
gtsam.BetweenFactorPose2(
previous_key,
current_key,
odometry_measurement_1,
odometry_noise_1))
odometry_measurement_2 = gtsam.Pose2(0.47, 0.03, 0.01)
odometry_noise_2 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.05, 0.05, 0.05]))
new_factors.push_back(gtsam.BetweenFactorPose2(
previous_key, current_key, odometry_measurement_2, odometry_noise_2
))
odometry_noise_2 = gtsam.noiseModel_Diagonal.Sigmas(
np.array([0.05, 0.05, 0.05]))
new_factors.push_back(
gtsam.BetweenFactorPose2(
previous_key,
current_key,
odometry_measurement_2,
odometry_noise_2))
# Update the smoothers with the new factors
smoother_batch.update(new_factors, new_values, new_timestamps)
# Update the smoothers with the new factors. In this case,
# one iteration must pass for Levenberg-Marquardt to accurately
# estimate
if time >= 0.50:
smoother_batch.update(new_factors, new_values, new_timestamps)
estimate = smoother_batch.calculateEstimatePose2(current_key)
self.assertTrue(estimate.equals(ground_truth[i], 1e-4))
estimate = smoother_batch.calculateEstimatePose2(current_key)
self.assertTrue(estimate.equals(ground_truth[i], 1e-4))
i += 1
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
time += delta_time
i += 1
if __name__ == "__main__":
unittest.main()

36
gtsam_unstable/examples/FixedLagSmootherExample.cpp
View File

@ -111,23 +111,27 @@ int main(int argc, char** argv) {
noiseModel::Diagonal::shared_ptr odometryNoise2 = noiseModel::Diagonal::Sigmas(Vector3(0.05, 0.05, 0.05));
newFactors.push_back(BetweenFactor<Pose2>(previousKey, currentKey, odometryMeasurement2, odometryNoise2));
// Update the smoothers with the new factors
smootherBatch.update(newFactors, newValues, newTimestamps);
smootherISAM2.update(newFactors, newValues, newTimestamps);
for(size_t i = 1; i < 2; ++i) { // Optionally perform multiple iSAM2 iterations
smootherISAM2.update();
// Update the smoothers with the new factors.
// In this example, Levenberg-Marquadt needs one iteration
// to pass to accurately estimate.
if (time >= 0.50) {
smootherBatch.update(newFactors, newValues, newTimestamps);
smootherISAM2.update(newFactors, newValues, newTimestamps);
for(size_t i = 1; i < 2; ++i) { // Optionally perform multiple iSAM2 iterations
smootherISAM2.update();
}
// Print the optimized current pose
cout << setprecision(5) << "Timestamp = " << time << endl;
smootherBatch.calculateEstimate<Pose2>(currentKey).print("Batch Estimate:");
smootherISAM2.calculateEstimate<Pose2>(currentKey).print("iSAM2 Estimate:");
cout << endl;
// Clear contains for the next iteration
newTimestamps.clear();
newValues.clear();
newFactors.resize(0);
}
// Print the optimized current pose
cout << setprecision(5) << "Timestamp = " << time << endl;
smootherBatch.calculateEstimate<Pose2>(currentKey).print("Batch Estimate:");
smootherISAM2.calculateEstimate<Pose2>(currentKey).print("iSAM2 Estimate:");
cout << endl;
// Clear contains for the next iteration
newTimestamps.clear();
newValues.clear();
newFactors.resize(0);
}
// And to demonstrate the fixed-lag aspect, print the keys contained in each smoother after 3.0 seconds