diff --git a/matlab/gtsam_examples/PlanarSLAMExample.m b/matlab/gtsam_examples/PlanarSLAMExample.m
index 1dc6ba41c..ef1516017 100644
--- a/matlab/gtsam_examples/PlanarSLAMExample.m
+++ b/matlab/gtsam_examples/PlanarSLAMExample.m
@@ -31,7 +31,7 @@ graph = NonlinearFactorGraph;
 %% Add prior
 priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
 priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
-graph.add(PriorFactorPose2(i1, priorMean, priorNoise)); % add directly to graph
+graph.add(PriorFactorPose2(i1, priorMean, priorNoise));
 
 %% Add odometry
 odometry = Pose2(2.0, 0.0, 0.0);
@@ -42,7 +42,7 @@ graph.add(BetweenFactorPose2(i2, i3, odometry, odometryNoise));
 %% Add bearing/range measurement factors
 degrees = pi/180;
 brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
-graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(4+4), brNoise));
+graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(8), brNoise));
 graph.add(BearingRangeFactor2D(i2, j1, Rot2(90*degrees), 2, brNoise));
 graph.add(BearingRangeFactor2D(i3, j2, Rot2(90*degrees), 2, brNoise));
 
@@ -69,7 +69,7 @@ cla;hold on
 
 marginals = Marginals(graph, result);
 plot2DTrajectory(result, [], marginals);
-plot2DPoints(result, [], marginals);
+plot2DPoints(result, 'b', marginals);
 
 plot([result.at(i1).x; result.at(j1).x],[result.at(i1).y; result.at(j1).y], 'c-');
 plot([result.at(i2).x; result.at(j1).x],[result.at(i2).y; result.at(j1).y], 'c-');
diff --git a/matlab/gtsam_examples/Pose2SLAMExample_graph.m b/matlab/gtsam_examples/Pose2SLAMExample_graph.m
index b48485573..eb1b03950 100644
--- a/matlab/gtsam_examples/Pose2SLAMExample_graph.m
+++ b/matlab/gtsam_examples/Pose2SLAMExample_graph.m
@@ -17,11 +17,7 @@ datafile = findExampleDataFile('w100-odom.graph');
 
 %% Initialize graph, initial estimate, and odometry noise
 model = noiseModel.Diagonal.Sigmas([0.05; 0.05; 5*pi/180]);
-maxID = 0;
-addNoise = false;
-smart = true;
-[graph,initial] = load2D(datafile, model, maxID, addNoise, smart);
-initial.print(sprintf('Initial estimate:\n'));
+[graph,initial] = load2D(datafile, model);
 
 %% Add a Gaussian prior on pose x_1
 priorMean = Pose2(0, 0, 0); % prior mean is at origin
@@ -34,9 +30,10 @@ plot2DTrajectory(initial, 'g-*'); axis equal
 
 %% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
 optimizer = LevenbergMarquardtOptimizer(graph, initial);
+tic
 result = optimizer.optimizeSafely;
+toc
 hold on; plot2DTrajectory(result, 'b-*');
-result.print(sprintf('\nFinal result:\n'));
 
 %% Plot Covariance Ellipses
 marginals = Marginals(graph, result);
@@ -48,4 +45,4 @@ for i=1:result.size()-1
 end
 view(2)
 axis tight; axis equal;
-fprintf(1,'%.5f %.5f %.5f\n',P{99})
\ No newline at end of file
+% fprintf(1,'%.5f %.5f %.5f\n',P{99})
\ No newline at end of file