46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
import unittest
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import gtsam
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import numpy as np
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class TestOdometryExample(unittest.TestCase):
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def test_OdometryExample(self):
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# Create the graph (defined in pose2SLAM.h, derived from
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# NonlinearFactorGraph)
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graph = gtsam.NonlinearFactorGraph()
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# Add a Gaussian prior on pose x_1
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priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior mean is at origin
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priorNoise = gtsam.noiseModel_Diagonal.Sigmas(
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np.array([0.3, 0.3, 0.1])) # 30cm std on x,y, 0.1 rad on theta
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# add directly to graph
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graph.add(gtsam.PriorFactorPose2(1, priorMean, priorNoise))
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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 = gtsam.Pose2(2.0, 0.0, 0.0)
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odometryNoise = gtsam.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(gtsam.BetweenFactorPose2(1, 2, odometry, odometryNoise))
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graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, odometryNoise))
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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, 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 = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate)
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result = optimizer.optimizeSafely()
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marginals = gtsam.Marginals(graph, result)
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marginals.marginalCovariance(1)
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# Check first pose equality
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pose_1 = result.atPose2(1)
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self.assertTrue(pose_1.equals(gtsam.Pose2(), 1e-4))
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if __name__ == "__main__":
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unittest.main()
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