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examples/matlab/LocalizationExample.m

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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 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
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Assumptions
+%  - Robot poses are facing along the X axis (horizontal, to the right in 2D)
+%  - The robot moves 2 meters each step
+%  - The robot is on a grid, moving 2 meters each step
+
+%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
+graph = pose2SLAMGraph;
+
+%% Add a Gaussian prior on pose x_1
+priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior mean is at origin
+priorNoise = gtsamSharedNoiseModel_Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
+graph.addPrior(1, priorMean, priorNoise); % add directly to graph
+
+%% Add two odometry factors
+odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
+odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
+graph.addOdometry(1, 2, odometry, odometryNoise);
+graph.addOdometry(2, 3, odometry, odometryNoise);
+
+%% print
+graph.print(sprintf('\nFactor graph:\n'));
+
+%% Initialize to noisy points
+initialEstimate = pose2SLAMValues;
+initialEstimate.insertPose(1, gtsamPose2(0.5, 0.0, 0.2));
+initialEstimate.insertPose(2, gtsamPose2(2.3, 0.1,-0.2));
+initialEstimate.insertPose(3, gtsamPose2(4.1, 0.1, 0.1));
+initialEstimate.print(sprintf('\nInitial estimate:\n  '));
+
+%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
+result = graph.optimize(initialEstimate);
+result.print(sprintf('\nFinal result:\n  '));
+
+%% Use an explicit Optimizer object so we can query the marginals
+% marginals = gtsamMarginals(graph, result);
+% marginals.marginalCovariance(pose2SLAMPoseKey(1))
+% marginals.marginalCovariance(pose2SLAMPoseKey(2))
+% marginals.marginalCovariance(pose2SLAMPoseKey(3))