gtsam/python/gtsam/tests/test_OdometryExample.py

60 lines
2.0 KiB
Python

"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Odometry unit tests.
Author: Frank Dellaert & Duy Nguyen Ta (Python)
"""
import unittest
import numpy as np
import gtsam
from gtsam.utils.test_case import GtsamTestCase
class TestOdometryExample(GtsamTestCase):
def test_OdometryExample(self):
# Create the graph (defined in pose2SLAM.h, derived from
# NonlinearFactorGraph)
graph = gtsam.NonlinearFactorGraph()
# Add a Gaussian prior on pose x_1
priorMean = gtsam.Pose2(0.0, 0.0, 0.0) # prior mean is at origin
priorNoise = gtsam.noiseModel.Diagonal.Sigmas(
np.array([0.3, 0.3, 0.1])) # 30cm std on x,y, 0.1 rad on theta
# add directly to graph
graph.add(gtsam.PriorFactorPose2(1, priorMean, priorNoise))
# Add two odometry factors
# create a measurement for both factors (the same in this case)
odometry = gtsam.Pose2(2.0, 0.0, 0.0)
odometryNoise = gtsam.noiseModel.Diagonal.Sigmas(
np.array([0.2, 0.2, 0.1])) # 20cm std on x,y, 0.1 rad on theta
graph.add(gtsam.BetweenFactorPose2(1, 2, odometry, odometryNoise))
graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, odometryNoise))
# Initialize to noisy points
initialEstimate = gtsam.Values()
initialEstimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
initialEstimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
initialEstimate.insert(3, gtsam.Pose2(4.1, 0.1, 0.1))
# Optimize using Levenberg-Marquardt optimization with an ordering from
# colamd
optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate)
result = optimizer.optimizeSafely()
marginals = gtsam.Marginals(graph, result)
marginals.marginalCovariance(1)
# Check first pose equality
pose_1 = result.atPose2(1)
self.gtsamAssertEquals(pose_1, gtsam.Pose2(), 1e-4)
if __name__ == "__main__":
unittest.main()