Pose2SLAMExample.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 math
 
import gtsam
import gtsam.utils.plot as gtsam_plot
import matplotlib.pyplot as plt
 
 
def main():
    """Main runner."""
    # Create noise models
    PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(gtsam.Point3(0.3, 0.3, 0.1))
    ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(
        gtsam.Point3(0.2, 0.2, 0.1))
 
    # 1. Create a factor graph container and add factors to it
    graph = gtsam.NonlinearFactorGraph()
 
    # 2a. Add a prior on the first pose, setting it to the origin
    # A prior factor consists of a mean and a noise ODOMETRY_NOISE (covariance matrix)
    graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE))
 
    # 2b. Add odometry factors
    # Create odometry (Between) factors between consecutive poses
    graph.add(
        gtsam.BetweenFactorPose2(1, 2, gtsam.Pose2(2, 0, 0), ODOMETRY_NOISE))
    graph.add(
        gtsam.BetweenFactorPose2(2, 3, gtsam.Pose2(2, 0, math.pi / 2),
                                 ODOMETRY_NOISE))
    graph.add(
        gtsam.BetweenFactorPose2(3, 4, gtsam.Pose2(2, 0, math.pi / 2),
                                 ODOMETRY_NOISE))
    graph.add(
        gtsam.BetweenFactorPose2(4, 5, gtsam.Pose2(2, 0, math.pi / 2),
                                 ODOMETRY_NOISE))
 
    # 2c. Add the loop closure constraint
    # This factor encodes the fact that we have returned to the same pose. In real
    # systems, these constraints may be identified in many ways, such as appearance-based
    # techniques with camera images. We will use another Between Factor to enforce this constraint:
    graph.add(
        gtsam.BetweenFactorPose2(5, 2, gtsam.Pose2(2, 0, math.pi / 2),
                                 ODOMETRY_NOISE))
    print("\nFactor Graph:\n{}".format(graph))  # print
 
    # 3. Create the data structure to hold the initial_estimate estimate to the
    # solution. For illustrative purposes, these have been deliberately set to incorrect values
    initial_estimate = gtsam.Values()
    initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
    initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
    initial_estimate.insert(3, gtsam.Pose2(4.1, 0.1, math.pi / 2))
    initial_estimate.insert(4, gtsam.Pose2(4.0, 2.0, math.pi))
    initial_estimate.insert(5, gtsam.Pose2(2.1, 2.1, -math.pi / 2))
    print("\nInitial Estimate:\n{}".format(initial_estimate))  # print
 
    # 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
    # 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. We will set a few parameters as a demonstration.
    parameters = gtsam.GaussNewtonParams()
 
    # Stop iterating once the change in error between steps is less than this value
    parameters.setRelativeErrorTol(1e-5)
    # Do not perform more than N iteration steps
    parameters.setMaxIterations(100)
    # Create the optimizer ...
    optimizer = gtsam.GaussNewtonOptimizer(graph, initial_estimate, parameters)
    # ... and optimize
    result = optimizer.optimize()
    print("Final Result:\n{}".format(result))
 
    # 5. Calculate and print marginal covariances for all variables
    marginals = gtsam.Marginals(graph, result)
    for i in range(1, 6):
        print("X{} covariance:\n{}\n".format(i,
                                             marginals.marginalCovariance(i)))
 
    for i in range(1, 6):
        gtsam_plot.plot_pose2(0, result.atPose2(i), 0.5,
                              marginals.marginalCovariance(i))
 
    plt.axis('equal')
    plt.show()
 
 
if __name__ == "__main__":
    main()