%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % GTSAM Copyright 2010, 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 % % @brief Example of a simple 2D localization example % @author Frank Dellaert %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% import gtsam.* %% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph) graph = NonlinearFactorGraph; %% Add a Gaussian prior on pose x_1 priorMean = Pose2(0.0, 0.0, 0.0); % prior mean is at origin priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta graph.add(PriorFactorPose2(1, priorMean, priorNoise)); % add directly to graph %% Add two odometry factors odometry = Pose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case) odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta graph.add(BetweenFactorPose2(1, 2, odometry, odometryNoise)); graph.add(BetweenFactorPose2(2, 3, odometry, odometryNoise)); %% Initialize to noisy points initialEstimate = Values; initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2)); initialEstimate.insert(2, Pose2(2.3, 0.1,-0.2)); initialEstimate.insert(3, Pose2(4.1, 0.1, 0.1)); %% Optimize using Levenberg-Marquardt optimization with an ordering from colamd optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate); result = optimizer.optimizeSafely(); marginals = Marginals(graph, result); marginals.marginalCovariance(1); %% Check first pose equality pose_1 = result.atPose2(1); CHECK('pose_1.equals(Pose2,1e-4)',pose_1.equals(Pose2,1e-4));