77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
"""
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GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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See LICENSE for the license information
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Pose2SLAM unit tests.
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Author: Frank Dellaert & Duy Nguyen Ta (Python)
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"""
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import unittest
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from math import pi
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import numpy as np
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import gtsam
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from gtsam.utils.test_case import GtsamTestCase
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class TestPose2SLAMExample(GtsamTestCase):
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def test_Pose2SLAMExample(self):
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# Assumptions
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# - All values are axis aligned
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# - Robot poses are facing along the X axis (horizontal, to the right in images)
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# - We have full odometry for measurements
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# - The robot is on a grid, moving 2 meters each step
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# Create graph container and add factors to it
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graph = gtsam.NonlinearFactorGraph()
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# Add prior
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# gaussian for prior
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priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior at origin
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priorNoise = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
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# add directly to graph
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graph.add(gtsam.PriorFactorPose2(1, priorMean, priorNoise))
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# Add odometry
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# general noisemodel for odometry
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odometryNoise = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
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graph.add(gtsam.BetweenFactorPose2(
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1, 2, gtsam.Pose2(2.0, 0.0, 0.0), odometryNoise))
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graph.add(gtsam.BetweenFactorPose2(
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2, 3, gtsam.Pose2(2.0, 0.0, pi / 2), odometryNoise))
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graph.add(gtsam.BetweenFactorPose2(
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3, 4, gtsam.Pose2(2.0, 0.0, pi / 2), odometryNoise))
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graph.add(gtsam.BetweenFactorPose2(
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4, 5, gtsam.Pose2(2.0, 0.0, pi / 2), odometryNoise))
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# Add pose constraint
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model = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
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graph.add(gtsam.BetweenFactorPose2(5, 2, gtsam.Pose2(2.0, 0.0, pi / 2), model))
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# Initialize to noisy points
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initialEstimate = gtsam.Values()
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initialEstimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
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initialEstimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
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initialEstimate.insert(3, gtsam.Pose2(4.1, 0.1, pi / 2))
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initialEstimate.insert(4, gtsam.Pose2(4.0, 2.0, pi))
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initialEstimate.insert(5, gtsam.Pose2(2.1, 2.1, -pi / 2))
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# Optimize using Levenberg-Marquardt optimization with an ordering from
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# colamd
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate)
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result = optimizer.optimizeSafely()
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# Plot Covariance Ellipses
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marginals = gtsam.Marginals(graph, result)
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P = marginals.marginalCovariance(1)
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pose_1 = result.atPose2(1)
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self.gtsamAssertEquals(pose_1, gtsam.Pose2(), 1e-4)
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if __name__ == "__main__":
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unittest.main()
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