PlanarSLAMExample.py

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"""
GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
Authors: Frank Dellaert, et al. (see THANKS for the full author list)

See LICENSE for the license information

Simple robotics example using odometry measurements and bearing-range (laser) measurements
Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
"""
# pylint: disable=invalid-name, E1101

from __future__ import print_function

import numpy as np

import gtsam
from gtsam.symbol_shorthand import X, L

# Create noise models
PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
MEASUREMENT_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.1, 0.2]))

# Create an empty nonlinear factor graph
graph = gtsam.NonlinearFactorGraph()

# Create the keys corresponding to unknown variables in the factor graph
X1 = X(1)
X2 = X(2)
X3 = X(3)
L1 = L(4)
L2 = L(5)

# Add a prior on pose X1 at the origin. A prior factor consists of a mean and a noise model
graph.add(gtsam.PriorFactorPose2(X1, gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE))

# Add odometry factors between X1,X2 and X2,X3, respectively
graph.add(gtsam.BetweenFactorPose2(
    X1, X2, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE))
graph.add(gtsam.BetweenFactorPose2(
    X2, X3, gtsam.Pose2(2.0, 0.0, 0.0), ODOMETRY_NOISE))

# Add Range-Bearing measurements to two different landmarks L1 and L2
graph.add(gtsam.BearingRangeFactor2D(
    X1, L1, gtsam.Rot2.fromDegrees(45), np.sqrt(4.0+4.0), MEASUREMENT_NOISE))
graph.add(gtsam.BearingRangeFactor2D(
    X2, L1, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE))
graph.add(gtsam.BearingRangeFactor2D(
    X3, L2, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE))

# Print graph
print("Factor Graph:\n{}".format(graph))

# Create (deliberately inaccurate) initial estimate
initial_estimate = gtsam.Values()
initial_estimate.insert(X1, gtsam.Pose2(-0.25, 0.20, 0.15))
initial_estimate.insert(X2, gtsam.Pose2(2.30, 0.10, -0.20))
initial_estimate.insert(X3, gtsam.Pose2(4.10, 0.10, 0.10))
initial_estimate.insert(L1, gtsam.Point2(1.80, 2.10))
initial_estimate.insert(L2, gtsam.Point2(4.10, 1.80))

# Print
print("Initial Estimate:\n{}".format(initial_estimate))

# Optimize using Levenberg-Marquardt optimization. The optimizer
# accepts an optional set of configuration parameters, controlling
# things like convergence criteria, the type of linear system solver
# to use, and the amount of information displayed during optimization.
# Here we will use the default set of parameters.  See the
# documentation for the full set of parameters.
params = gtsam.LevenbergMarquardtParams()
optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)
result = optimizer.optimize()
print("\nFinal Result:\n{}".format(result))

# Calculate and print marginal covariances for all variables
marginals = gtsam.Marginals(graph, result)
for (key, str) in [(X1, "X1"), (X2, "X2"), (X3, "X3"), (L1, "L1"), (L2, "L2")]:
    print("{} covariance:\n{}\n".format(str, marginals.marginalCovariance(key)))