diff --git a/matlab/examples/Pose2SLAMExample_graph1.m b/matlab/examples/Pose2SLAMExample_graph1.m
deleted file mode 100644
index ef8002209..000000000
--- a/matlab/examples/Pose2SLAMExample_graph1.m
+++ /dev/null
@@ -1,36 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% 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 Read graph from file and perform GraphSLAM
-% @author Frank Dellaert
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-%% Initialize graph, initial estimate, and odometry noise
-import gtsam.*
-model = noiseModel.Diagonal.Sigmas([0.05; 0.05; 1*pi/180]);
-maxID=0;
-addNoise=false;
-smart=true;
-[graph,initial]=load2D('Data/w10000-odom.graph',model,maxID,addNoise,smart);
-
-%% 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([0.01; 0.01; 0.01]);
-graph.addPosePrior(0, priorMean, priorNoise); % add directly to graph
-
-%% Plot Initial Estimate
-figure(1);clf
-P=initial.poses;
-plot(P(:,1),P(:,2),'r-'); axis equal
-
-%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
-tic
-result = graph.optimize(initial,1);
-toc
-P=result.poses;
-hold on; plot(P(:,1),P(:,2),'b-')
diff --git a/matlab/examples/Pose2SLAMExample_graph2.m b/matlab/examples/Pose2SLAMExample_graph2.m
deleted file mode 100644
index 0eb0dc31d..000000000
--- a/matlab/examples/Pose2SLAMExample_graph2.m
+++ /dev/null
@@ -1,52 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% 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 Read graph from file and perform GraphSLAM
-% @author Frank Dellaert
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-import gtsam.*
-maxID=0;
-addNoise=false;
-smart=true;
-priorMean = Pose2(0.0, 0.0, 0.0); % prior mean is at origin
-priorNoise = noiseModel.Diagonal.Sigmas([0.01; 0.01; 0.01]);
-
-%% Create graph, disregarding translation measurements by setting sigma high
-model = noiseModel.Diagonal.Sigmas([100000; 100000; 1*pi/180]);
-[graph,initial]=load2D('Data/w10000-odom.graph',model,maxID,addNoise,smart);
-
-%% Add a Gaussian prior on pose x_1
-graph.addPosePrior(0, priorMean, priorNoise); % add directly to graph
-
-%% Plot Initial Estimate
-figure(1);clf
-P=initial.poses;
-plot(P(:,1),P(:,2),'r-'); axis equal
-
-%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
-tic
-result1 = graph.optimize(initial,1);
-toc
-P=result1.poses;
-hold on; plot(P(:,1),P(:,2),'g-')
-
-%% Read again, with correct noise now...
-model = noiseModel.Diagonal.Sigmas([0.05; 0.05; 1*pi/180]);
-graph=load2D('Data/w10000-odom.graph',model,maxID,addNoise,smart);
-
-%% Add a Gaussian prior on pose x_1
-graph.addPosePrior(0, priorMean, priorNoise); % add directly to graph
-
-%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
-tic
-result2 = graph.optimize(result1,1);
-toc
-P=result2.poses;
-hold on; plot(P(:,1),P(:,2),'b-')
-