73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
"""
|
|
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 robot motion example, with prior and two odometry measurements
|
|
Author: Frank Dellaert
|
|
"""
|
|
# pylint: disable=invalid-name, E1101
|
|
|
|
from __future__ import print_function
|
|
|
|
import gtsam
|
|
import gtsam.utils.plot as gtsam_plot
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
# Create noise models
|
|
ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.2, 0.2, 0.1]))
|
|
PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
|
|
|
|
|
|
def main():
|
|
"""Main runner"""
|
|
# Create an empty nonlinear factor graph
|
|
graph = gtsam.NonlinearFactorGraph()
|
|
|
|
# Add a prior on the first pose, setting it to the origin
|
|
# A prior factor consists of a mean and a noise model (covariance matrix)
|
|
priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior at origin
|
|
graph.add(gtsam.PriorFactorPose2(1, priorMean, PRIOR_NOISE))
|
|
|
|
# Add odometry factors
|
|
odometry = gtsam.Pose2(2.0, 0.0, 0.0)
|
|
# For simplicity, we will use the same noise model for each odometry factor
|
|
# Create odometry (Between) factors between consecutive poses
|
|
graph.add(gtsam.BetweenFactorPose2(1, 2, odometry, ODOMETRY_NOISE))
|
|
graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, ODOMETRY_NOISE))
|
|
print("\nFactor Graph:\n{}".format(graph))
|
|
|
|
# Create the data structure to hold the initialEstimate estimate to the solution
|
|
# For illustrative purposes, these have been deliberately set to incorrect values
|
|
initial = gtsam.Values()
|
|
initial.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
|
|
initial.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
|
|
initial.insert(3, gtsam.Pose2(4.1, 0.1, 0.1))
|
|
print("\nInitial Estimate:\n{}".format(initial))
|
|
|
|
# optimize using Levenberg-Marquardt optimization
|
|
params = gtsam.LevenbergMarquardtParams()
|
|
optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
|
|
result = optimizer.optimize()
|
|
print("\nFinal Result:\n{}".format(result))
|
|
|
|
# 5. Calculate and print marginal covariances for all variables
|
|
marginals = gtsam.Marginals(graph, result)
|
|
for i in range(1, 4):
|
|
print("X{} covariance:\n{}\n".format(i,
|
|
marginals.marginalCovariance(i)))
|
|
|
|
for i in range(1, 4):
|
|
gtsam_plot.plot_pose2(0, result.atPose2(i), 0.5,
|
|
marginals.marginalCovariance(i))
|
|
plt.axis('equal')
|
|
plt.show()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|