52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
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import unittest
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from gtsam import *
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from math import *
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import numpy as np
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class TestLocalizationExample(unittest.TestCase):
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def test_LocalizationExample(self):
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# Create the graph (defined in pose2SLAM.h, derived from
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# NonlinearFactorGraph)
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graph = NonlinearFactorGraph()
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# Add two odometry factors
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# create a measurement for both factors (the same in this case)
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odometry = Pose2(2.0, 0.0, 0.0)
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odometryNoise = noiseModel_Diagonal.Sigmas(
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np.array([0.2, 0.2, 0.1])) # 20cm std on x,y, 0.1 rad on theta
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graph.add(BetweenFactorPose2(0, 1, odometry, odometryNoise))
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graph.add(BetweenFactorPose2(1, 2, odometry, odometryNoise))
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# Add three "GPS" measurements
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# We use Pose2 Priors here with high variance on theta
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groundTruth = Values()
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groundTruth.insertPose2(0, Pose2(0.0, 0.0, 0.0))
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groundTruth.insertPose2(1, Pose2(2.0, 0.0, 0.0))
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groundTruth.insertPose2(2, Pose2(4.0, 0.0, 0.0))
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model = noiseModel_Diagonal.Sigmas(np.array([0.1, 0.1, 10.]))
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for i in range(3):
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graph.add(PriorFactorPose2(i, groundTruth.atPose2(i), model))
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# Initialize to noisy points
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initialEstimate = Values()
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initialEstimate.insertPose2(0, Pose2(0.5, 0.0, 0.2))
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initialEstimate.insertPose2(1, Pose2(2.3, 0.1, -0.2))
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initialEstimate.insertPose2(2, Pose2(4.1, 0.1, 0.1))
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# Optimize using Levenberg-Marquardt optimization with an ordering from
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# colamd
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optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate)
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result = optimizer.optimizeSafely()
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# Plot Covariance Ellipses
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marginals = Marginals(graph, result)
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P = [None] * result.size()
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for i in range(0, result.size()):
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pose_i = result.atPose2(i)
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self.assertTrue(pose_i.equals(groundTruth.atPose2(i), 1e-4))
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P[i] = marginals.marginalCovariance(i)
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if __name__ == "__main__":
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unittest.main()
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