105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import unittest
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import gtsam
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import gtsam_unstable
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import numpy as np
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def _timestamp_key_value(key, value):
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return gtsam_unstable.FixedLagSmootherKeyTimestampMapValue(
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key, value
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)
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class TestFixedLagSmootherExample(unittest.TestCase):
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'''
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Tests the fixed lag smoother wrapper
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'''
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def test_FixedLagSmootherExample(self):
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'''
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Simple test that checks for equality between C++ example
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file and the Python implementation. See
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gtsam_unstable/examples/FixedLagSmootherExample.cpp
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'''
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# Define a batch fixed lag smoother, which uses
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# Levenberg-Marquardt to perform the nonlinear optimization
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lag = 2.0
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smoother_batch = gtsam_unstable.BatchFixedLagSmoother(lag)
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# Create containers to store the factors and linearization points
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# that will be sent to the smoothers
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new_factors = gtsam.NonlinearFactorGraph()
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new_values = gtsam.Values()
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new_timestamps = gtsam_unstable.FixedLagSmootherKeyTimestampMap()
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# Create a prior on the first pose, placing it at the origin
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prior_mean = gtsam.Pose2(0, 0, 0)
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prior_noise = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
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X1 = 0
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new_factors.push_back(gtsam.PriorFactorPose2(X1, prior_mean, prior_noise))
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new_values.insert(X1, prior_mean)
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new_timestamps.insert(_timestamp_key_value(X1, 0.0))
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delta_time = 0.25
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time = 0.25
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i = 0
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ground_truth = [
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gtsam.Pose2(0.49792, 0.007802, 0.015),
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gtsam.Pose2(0.99547, 0.023019, 0.03),
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gtsam.Pose2(1.4928, 0.045725, 0.045),
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gtsam.Pose2(1.9898, 0.075888, 0.06),
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gtsam.Pose2(2.4863, 0.1135, 0.075),
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gtsam.Pose2(2.9821, 0.15856, 0.09),
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gtsam.Pose2(3.4772, 0.21105, 0.105),
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gtsam.Pose2(3.9715, 0.27096, 0.12),
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gtsam.Pose2(4.4648, 0.33827, 0.135),
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gtsam.Pose2(4.957, 0.41298, 0.15),
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gtsam.Pose2(5.4481, 0.49506, 0.165),
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gtsam.Pose2(5.9379, 0.5845, 0.18),
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]
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# Iterates from 0.25s to 3.0s, adding 0.25s each loop
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# In each iteration, the agent moves at a constant speed
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# and its two odometers measure the change. The smoothed
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# result is then compared to the ground truth
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while time <= 3.0:
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previous_key = 1000 * (time - delta_time)
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current_key = 1000 * time
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# assign current key to the current timestamp
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new_timestamps.insert(_timestamp_key_value(current_key, time))
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# Add a guess for this pose to the new values
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# Assume that the robot moves at 2 m/s. Position is time[s] * 2[m/s]
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current_pose = gtsam.Pose2(time * 2, 0, 0)
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new_values.insert(current_key, current_pose)
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# Add odometry factors from two different sources with different error stats
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odometry_measurement_1 = gtsam.Pose2(0.61, -0.08, 0.02)
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odometry_noise_1 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.1, 0.1, 0.05]))
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new_factors.push_back(gtsam.BetweenFactorPose2(
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previous_key, current_key, odometry_measurement_1, odometry_noise_1
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))
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odometry_measurement_2 = gtsam.Pose2(0.47, 0.03, 0.01)
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odometry_noise_2 = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.05, 0.05, 0.05]))
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new_factors.push_back(gtsam.BetweenFactorPose2(
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previous_key, current_key, odometry_measurement_2, odometry_noise_2
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))
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# Update the smoothers with the new factors
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smoother_batch.update(new_factors, new_values, new_timestamps)
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estimate = smoother_batch.calculateEstimatePose2(current_key)
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self.assertTrue(estimate.equals(ground_truth[i], 1e-4))
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new_timestamps.clear()
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new_values.clear()
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new_factors.resize(0)
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time += delta_time
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i += 1
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if __name__ == "__main__":
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unittest.main()
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