69 lines
2.8 KiB
Python
69 lines
2.8 KiB
Python
"""
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A simple 2D pose slam example with "GPS" measurements
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- The robot moves forward 2 meter each iteration
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- The robot initially faces along the X axis (horizontal, to the right in 2D)
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- We have full odometry between pose
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- We have "GPS-like" measurements implemented with a custom factor
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"""
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import numpy as np
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import gtsam
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from gtsam import BetweenFactorPose2, Pose2, noiseModel
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from gtsam_unstable import PartialPriorFactorPose2
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def main():
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# 1. Create a factor graph container and add factors to it.
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graph = gtsam.NonlinearFactorGraph()
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# 2a. Add odometry factors
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# For simplicity, we will use the same noise model for each odometry factor
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odometryNoise = noiseModel.Diagonal.Sigmas(np.asarray([0.2, 0.2, 0.1]))
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# Create odometry (Between) factors between consecutive poses
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graph.push_back(
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BetweenFactorPose2(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise))
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graph.push_back(
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BetweenFactorPose2(2, 3, Pose2(2.0, 0.0, 0.0), odometryNoise))
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# 2b. Add "GPS-like" measurements
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# We will use PartialPrior factor for this.
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unaryNoise = noiseModel.Diagonal.Sigmas(np.array([0.1,
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0.1])) # 10cm std on x,y
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graph.push_back(
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PartialPriorFactorPose2(1, [0, 1], np.asarray([0.0, 0.0]), unaryNoise))
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graph.push_back(
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PartialPriorFactorPose2(2, [0, 1], np.asarray([2.0, 0.0]), unaryNoise))
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graph.push_back(
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PartialPriorFactorPose2(3, [0, 1], np.asarray([4.0, 0.0]), unaryNoise))
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graph.print("\nFactor Graph:\n")
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# 3. Create the data structure to hold the initialEstimate estimate to the solution
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# For illustrative purposes, these have been deliberately set to incorrect values
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initialEstimate = gtsam.Values()
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initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2))
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initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2))
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initialEstimate.insert(3, Pose2(4.1, 0.1, 0.1))
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initialEstimate.print("\nInitial Estimate:\n")
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# 4. Optimize using Levenberg-Marquardt optimization. The optimizer
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# accepts an optional set of configuration parameters, controlling
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# things like convergence criteria, the type of linear system solver
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# to use, and the amount of information displayed during optimization.
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# Here we will use the default set of parameters. See the
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# documentation for the full set of parameters.
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate)
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result = optimizer.optimize()
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result.print("Final Result:\n")
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# 5. Calculate and print marginal covariances for all variables
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marginals = gtsam.Marginals(graph, result)
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print("x1 covariance:\n", marginals.marginalCovariance(1))
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print("x2 covariance:\n", marginals.marginalCovariance(2))
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print("x3 covariance:\n", marginals.marginalCovariance(3))
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if __name__ == "__main__":
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main()
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