103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
"""
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GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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See LICENSE for the license information
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Demonstration of the fixed-lag smoothers using a planar robot example
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and multiple odometry-like sensors
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Author: Frank Dellaert (C++), Jeremy Aguilon (Python)
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"""
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import numpy as np
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import gtsam
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import gtsam_unstable
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def _timestamp_key_value(key, value):
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"""
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"""
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return gtsam_unstable.FixedLagSmootherKeyTimestampMapValue(
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key, value
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)
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def BatchFixedLagSmootherExample():
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"""
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Runs a batch fixed smoother on an agent with two odometry
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sensors that is simply moving to the
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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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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
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# 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(
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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(
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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. In this case,
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# one iteration must pass for Levenberg-Marquardt to accurately
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# estimate
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if time >= 0.50:
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smoother_batch.update(new_factors, new_values, new_timestamps)
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print("Timestamp = " + str(time) + ", Key = " + str(current_key))
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print(smoother_batch.calculateEstimatePose2(current_key))
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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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if __name__ == '__main__':
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BatchFixedLagSmootherExample()
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print("Example complete")
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