diff --git a/examples/matlab/Pose2SLAMExample_easy.m b/examples/matlab/Pose2SLAMExample_easy.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 Simple robotics example using the pre-built planar SLAM domain
+% @author Alex Cunningham
+% @author Frank Dellaert
+% @author Chris Beall
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Assumptions
+%  - All values are axis aligned
+%  - Robot poses are facing along the X axis (horizontal, to the right in images)
+%  - We have full odometry for measurements
+%  - The robot is on a grid, moving 2 meters each step
+
+%% Create keys for variables
+x1 = 1; x2 = 2; x3 = 3;
+
+%% Create graph container and add factors to it
+graph = pose2SLAMGraph;
+
+%% Add prior
+% gaussian for prior
+prior_model = SharedNoiseModel_sharedSigmas([0.3; 0.3; 0.1]);
+prior_measurement = Pose2(0.0, 0.0, 0.0); % prior at origin
+graph.addPrior(x1, prior_measurement, prior_model); % add directly to graph
+
+%% Add odometry
+% general noisemodel for odometry
+odom_model = SharedNoiseModel_sharedSigmas([0.2; 0.2; 0.1]);
+odom_measurement = Pose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
+graph.addOdometry(x1, x2, odom_measurement, odom_model);
+graph.addOdometry(x2, x3, odom_measurement, odom_model);
+
+%% Add measurements
+% general noisemodel for measurements
+meas_model = SharedNoiseModel_sharedSigmas([0.1; 0.2]);
+
+% print
+graph.print('full graph');
+
+%% Initialize to noisy points
+initialEstimate = pose2SLAMValues;
+initialEstimate.insertPose(x1, Pose2(0.5, 0.0, 0.2));
+initialEstimate.insertPose(x2, Pose2(2.3, 0.1,-0.2));
+initialEstimate.insertPose(x3, Pose2(4.1, 0.1, 0.1));
+
+initialEstimate.print('initial estimate');
+
+%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
+result = graph.optimize(initialEstimate);
+result.print('final result');
+
+%% Get the corresponding dense matrix
+ord = graph.orderingCOLAMD(result);
+gfg = graph.linearize(result,ord);
+denseAb = gfg.denseJacobian;
+
+%% Get sparse matrix A and RHS b
+IJS = gfg.sparseJacobian_();
+Ab=sparse(IJS(1,:),IJS(2,:),IJS(3,:));
+A = Ab(:,1:end-1);
+b = full(Ab(:,end));
+spy(A);