diff --git a/examples/matlab/VisualISAMExample_Duy.m b/examples/matlab/VisualISAMExample_Duy.m
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% GTSAM Copyright 3510, 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 A simple visual SLAM example for structure from motion
+% @author Duy-Nguyen Ta
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Create a triangle target, just 3 points on a plane
+nPoints = 3;
+r = 10;
+points = {};
+for j=1:nPoints
+    theta = (j-1)*2*pi/nPoints;
+    points{j} = gtsamPoint3([r*cos(theta), r*sin(theta), 0]');
+end
+
+%% Create camera cameras on a circle around the triangle
+nCameras = 30;
+height = 10;
+r = 30;
+cameras = {};
+K = gtsamCal3_S2(500,500,0,640/2,480/2);
+for i=1:nCameras
+    theta = (i-1)*2*pi/nCameras;
+    t = gtsamPoint3([r*cos(theta), r*sin(theta), height]');
+    cameras{i} = gtsamSimpleCamera_lookat(t, gtsamPoint3, gtsamPoint3([0,0,1]'), K);
+end
+odometry = cameras{1}.pose.between(cameras{2}.pose);
+
+poseNoise = gtsamSharedNoiseModel_Sigmas([0.001 0.001 0.001 0.5 0.5 0.5]');
+pointNoise = gtsamSharedNoiseModel_Sigma(3, 0.1);
+measurementNoise = gtsamSharedNoiseModel_Sigma(2, 1.0);
+
+%% Create an ISAM object for inference
+isam = visualSLAMISAM(5);
+
+%% Update ISAM
+newFactors = visualSLAMGraph;
+initialEstimates = visualSLAMValues;
+figure(1); clf;
+for i=1:nCameras
+    
+    % Prior for the first pose or odometry for subsequent cameras
+    if (i==1)
+        newFactors.addPosePrior(symbol('x',1), cameras{1}.pose, poseNoise);
+        for j=1:nPoints
+            newFactors.addPointPrior(symbol('l',j), points{j}, pointNoise);
+        end
+    else
+        newFactors.addOdometry(symbol('x',i-1), symbol('x',i), odometry, poseNoise);
+    end
+
+    % Visual measurement factors
+    for j=1:nPoints
+        zij = cameras{i}.project(points{j});
+        newFactors.addMeasurement(zij, measurementNoise, symbol('x',i), symbol('l',j), K);
+    end
+    
+    % Initial estimates for the new pose. Also initialize points while in 
+    % the first frame.
+    if (i==1)
+        initialEstimates.insertPose(symbol('x',i), cameras{i}.pose);
+        for j=1:size(points,2)
+            initialEstimates.insertPoint(symbol('l',j), points{j});
+        end
+    else
+        %TODO: this might be suboptimal since "result" is not the fully
+        %optimized result
+        if (i==2), prevPose = cameras{1}.pose;
+        else, prevPose = result.pose(symbol('x',i-1)); end
+        initialEstimates.insertPose(symbol('x',i), prevPose.compose(odometry));
+    end
+
+    % Update ISAM, only update for the second frame onward
+    % Update the first frame will cause error, since it's under constrained
+    if (i>=2)
+        isam.update(newFactors, initialEstimates);
+        result = isam.estimate();
+
+        % Plot results
+        h=figure(1);
+        hold on;
+        for j=1:size(points,2)
+            P = isam.marginalCovariance(symbol('l',j));
+            point_j = result.point(symbol('l',j));
+            plot3(point_j.x, point_j.y, point_j.z,'marker','o');
+            covarianceEllipse3D([point_j.x;point_j.y;point_j.z],P);
+        end
+
+        for ii=i-1:i
+            P = isam.marginalCovariance(symbol('x',ii));
+            pose_ii = result.pose(symbol('x',ii));
+            plotPose3(pose_ii,P,10);
+        end
+        axis([-35 35 -35 35 -35 35])
+        view([36 34])
+        colormap('hot')
+%         print(h,'-dpng',sprintf('vISAM_%03d.png',i));
+        
+        % Reset newFactors and initialEstimates to prepare for the next 
+        % update
+        newFactors = visualSLAMGraph;
+        initialEstimates = visualSLAMValues;
+    end
+end
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