""" 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()