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"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Cal3Unified unit tests.
Author: Frank Dellaert & Duy Nguyen Ta (Python)
"""
import unittest
import numpy as np
import gtsam
import gtsam_unstable
from gtsam.utils.test_case import GtsamTestCase
class TestFixedLagSmootherExample(GtsamTestCase):
'''
Tests the fixed lag smoother wrapper
'''
def test_FixedLagSmootherExample(self):
'''
Simple test that checks for equality between C++ example
file and the Python implementation. See
gtsam_unstable/examples/FixedLagSmootherExample.cpp
'''
# Define a batch fixed lag smoother, which uses
# Levenberg-Marquardt to perform the nonlinear optimization
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]))
X1 = 0
new_factors.push_back(
gtsam.PriorFactorPose2(
X1, prior_mean, prior_noise))
new_values.insert(X1, prior_mean)
new_timestamps.insert((X1, 0.0))
delta_time = 0.25
time = 0.25
i = 0
ground_truth = [
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
# result is then compared to the ground truth
while time <= 3.0:
previous_key = int(1000 * (time - delta_time))
current_key = int(1000 * time)
# assign current key to the current timestamp
new_timestamps.insert((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]
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
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_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))
# 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))
i += 1
new_timestamps.clear()
new_values.clear()
new_factors.resize(0)
time += delta_time
if __name__ == "__main__":
unittest.main()
|
