diff --git a/examples/VisualSLAMExampleData.h b/examples/VisualSLAMExampleData.h
new file mode 100644
index 000000000..f0ee4d2b5
--- /dev/null
+++ b/examples/VisualSLAMExampleData.h
@@ -0,0 +1,91 @@
+/* ----------------------------------------------------------------------------
+
+ * 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
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    VisualSLAMSimulatedData.cpp
+ * @brief   Generate ground-truth simulated data for VisualSLAM examples (SFM and ISAM2)
+ * @author  Duy-Nguyen Ta
+ */
+
+#pragma once
+
+#include <vector>
+#include <map>
+#include <gtsam/geometry/SimpleCamera.h>
+
+/* ************************************************************************* */
+/**
+ * Simulated data for the example:
+ * - 8 Landmarks:	(10,10,10) (-10,10,10) (-10,-10,10) (10,-10,10)
+ *      					(10,10,-10) (-10,10,-10) (-10,-10,-10) (10,-10,-10)
+ * - n 90-deg-FoV cameras with the same calibration parameters:
+ * 				 f = 50.0, Image: 100x100, center: 50.0, 50.0
+ * 		and ground-truth poses on a circle around the landmarks looking at the world's origin:
+ * 					Rot3(-sin(theta),  0, -cos(theta),
+ * 							  cos(theta),  0, -sin(theta),
+ * 							 					 0, -1,					 0 ),
+ * 					Point3(r*cos(theta), r*sin(theta), 0.0)
+ * 		(theta += 2*pi/N)
+ * - Measurement noise: 1 pix sigma
+ */
+struct VisualSLAMExampleData {
+	gtsam::shared_ptrK sK;								// camera calibration parameters
+	std::vector<gtsam::Pose3> poses;          // ground-truth camera poses
+	gtsam::Pose3 odometry;								// ground-truth odometry between 2 consecutive poses (simulated data for iSAM)
+	std::vector<gtsam::Point3> landmarks;     // ground-truth landmarks
+	std::map<int, vector<gtsam::Point2> > z;	// 2D measurements of landmarks in each camera frame
+	gtsam::SharedDiagonal noiseZ; 			// measurement noise (noiseModel::Isotropic::Sigma(2, 5.0f));
+	gtsam::SharedDiagonal noiseX; 			// noise for camera poses
+	gtsam::SharedDiagonal noiseL; 			// noise for landmarks
+
+	static const VisualSLAMExampleData generate() {
+		VisualSLAMExampleData data;
+		// Landmarks (ground truth)
+		data.landmarks.push_back(gtsam::Point3(10.0,10.0,10.0));
+		data.landmarks.push_back(gtsam::Point3(-10.0,10.0,10.0));
+		data.landmarks.push_back(gtsam::Point3(-10.0,-10.0,10.0));
+		data.landmarks.push_back(gtsam::Point3(10.0,-10.0,10.0));
+		data.landmarks.push_back(gtsam::Point3(10.0,10.0,-10.0));
+		data.landmarks.push_back(gtsam::Point3(-10.0,10.0,-10.0));
+		data.landmarks.push_back(gtsam::Point3(-10.0,-10.0,-10.0));
+		data.landmarks.push_back(gtsam::Point3(10.0,-10.0,-10.0));
+
+		// Camera calibration parameters
+		data.sK = gtsam::shared_ptrK(new gtsam::Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0));
+
+		// n camera poses
+		int n = 8;
+		double theta = 0.0;
+		double r = 30.0;
+		for (int i=0; i<n; ++i) {
+			theta += 2*M_PI/n;
+			data.poses.push_back(gtsam::Pose3(
+					gtsam::Rot3(-sin(theta), 0.0, -cos(theta),
+											cos(theta), 0.0, -sin(theta),
+											0.0, -1.0, 0.0),
+					gtsam::Point3(r*cos(theta), r*sin(theta), 0.0)));
+		}
+		data.odometry = data.poses[0].between(data.poses[1]);
+
+		// Simulated measurements with Gaussian noise
+		data.noiseZ = gtsam::sharedSigma(2, 1.0);
+		for (size_t i=0; i<data.poses.size(); ++i) {
+			for (size_t j=0; j<data.landmarks.size(); ++j) {
+				gtsam::SimpleCamera camera(data.poses[i], *data.sK);
+				data.z[i].push_back(camera.project(data.landmarks[j]) + gtsam::Point2(data.noiseZ->sample()));
+			}
+		}
+		data.noiseX = gtsam::sharedSigmas(gtsam::Vector_(6, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1));
+		data.noiseL = gtsam::sharedSigma(3, 0.1);
+
+		return data;
+	}
+};
diff --git a/examples/VisualSLAMforSFMExample.cpp b/examples/VisualSLAMforSFMExample.cpp
new file mode 100644
index 000000000..17f457908
--- /dev/null
+++ b/examples/VisualSLAMforSFMExample.cpp
@@ -0,0 +1,64 @@
+/* ----------------------------------------------------------------------------
+
+ * 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
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    VisualSLAMforSFMExample.cpp
+ * @brief   A visualSLAM example for the structure-from-motion problem on a simulated dataset
+ * @author  Duy-Nguyen Ta
+ */
+
+#include <gtsam/slam/visualSLAM.h>
+#include <gtsam/nonlinear/Symbol.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
+#include "VisualSLAMExampleData.h"
+
+using namespace std;
+using namespace gtsam;
+
+// Convenience for named keys
+using symbol_shorthand::X;
+using symbol_shorthand::L;
+
+/* ************************************************************************* */
+int main(int argc, char* argv[]) {
+
+	VisualSLAMExampleData data = VisualSLAMExampleData::generate();
+
+  /* 1. Create graph *///using the 2D measurements (features) and the calibration data
+  visualSLAM::Graph graph;
+
+  /* 2. Add factors to the graph */
+  // 2a. Measurement factors
+  for (size_t i=0; i<data.poses.size(); ++i) {
+  	for (size_t j=0; j<data.landmarks.size(); ++j)
+  		graph.addMeasurement(data.z[i][j], data.noiseZ, X(i), L(j), data.sK);
+  }
+  // 2b. Prior factor for the first pose to resolve ambiguity (not needed for LevenbergMarquardtOptimizer)
+  graph.addPosePrior(X(0), data.poses[0], data.noiseX);
+
+  /* 3. Initial estimates for variable nodes, simulated by Gaussian noises */
+  Values initial;
+  for (size_t i=0; i<data.poses.size(); ++i)
+  	initial.insert(X(i), data.poses[i]*Pose3::Expmap(data.noiseX->sample()));
+  for (size_t j=0; j<data.landmarks.size(); ++j)
+  	initial.insert(L(j), data.landmarks[j] + Point3(data.noiseL->sample()));
+  initial.print("Intial Estimates: ");
+
+  /* 4. Optimize the graph and print results */
+  visualSLAM::Values result = GaussNewtonOptimizer(graph, initial).optimize();
+//  visualSLAM::Values result = LevenbergMarquardtOptimizer(graph, initial).optimize();
+  result.print("Final results: ");
+
+  return 0;
+}
+/* ************************************************************************* */
+
diff --git a/examples/VisualSLAMwISAM2Example.cpp b/examples/VisualSLAMwISAM2Example.cpp
new file mode 100644
index 000000000..07fe0433c
--- /dev/null
+++ b/examples/VisualSLAMwISAM2Example.cpp
@@ -0,0 +1,113 @@
+/* ----------------------------------------------------------------------------
+
+ * 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
+
+ * -------------------------------------------------------------------------- */
+
+/**
+ * @file    VisualSLAMwISAM2Example.cpp
+ * @brief   An ISAM example for synthesis sequence, single camera
+ * @author  Duy-Nguyen Ta
+ */
+
+#include <gtsam/nonlinear/Symbol.h>
+#include <gtsam/nonlinear/NonlinearISAM.h>
+#include <gtsam/slam/visualSLAM.h>
+#include <gtsam/slam/BetweenFactor.h>
+#include "VisualSLAMExampleData.h"
+
+using namespace std;
+using namespace gtsam;
+
+// Convenience for named keys
+using symbol_shorthand::X;
+using symbol_shorthand::L;
+
+/* ************************************************************************* */
+int main(int argc, char* argv[]) {
+
+	VisualSLAMExampleData data = VisualSLAMExampleData::generate();
+
+  /* 1. Create a NonlinearISAM which will be relinearized and reordered after every "relinearizeInterval" updates */
+  int relinearizeInterval = 3;
+  NonlinearISAM<> isam(relinearizeInterval);
+
+  /* 2. At each frame (poseId) with new camera pose and set of associated measurements,
+   * create a graph of new factors and update ISAM */
+
+  // Store the current best estimate from ISAM
+	Values currentEstimate;
+
+  // First two frames:
+	// Add factors and initial values for the first two poses and landmarks then update ISAM.
+	// Note: measurements from the first pose only are not enough to update ISAM:
+	//       the system is underconstrained.
+  {
+  	visualSLAM::Graph newFactors;
+
+  	// First pose with prior factor
+  	newFactors.addPosePrior(X(0), data.poses[0], data.noiseX);
+
+  	// Second pose with odometry measurement, simulated by adding Gaussian noise to the ground-truth.
+  	Pose3 odoMeasurement =  data.odometry*Pose3::Expmap(data.noiseX->sample());
+  	newFactors.push_back( boost::shared_ptr<BetweenFactor<Pose3> >(
+  	  			new BetweenFactor<Pose3>(X(0), X(1), odoMeasurement, data.noiseX)));
+
+  	// Visual measurements at both poses
+  	for (size_t i=0; i<2; ++i) {
+			for (size_t j=0; j<data.z[i].size(); ++j) {
+				newFactors.addMeasurement(data.z[i][j], data.noiseZ, X(i), L(j), data.sK);
+			}
+  	}
+
+  	// Initial values for the first two poses, simulated with Gaussian noise
+  	Values initials;
+  	Pose3 pose0Init = data.poses[0]*Pose3::Expmap(data.noiseX->sample());
+  	initials.insert(X(0), pose0Init);
+  	initials.insert(X(1), pose0Init*odoMeasurement);
+
+  	// Initial values for the landmarks, simulated with Gaussian noise
+  	for (size_t j=0; j<data.landmarks.size(); ++j)
+  		initials.insert(L(j), data.landmarks[j] + Point3(data.noiseL->sample()));
+
+  	// Update ISAM the first time and obtain the current estimate
+  	isam.update(newFactors, initials);
+  	currentEstimate = isam.estimate();
+  	cout << "Pose 0 and 1: " << endl;
+  	currentEstimate.print("Current estimate: ");
+  }
+
+  // Subsequent frames: Add new odometry and measurement factors and initial values,
+  // then update ISAM at each frame
+  for (size_t i=2; i<data.poses.size(); ++i) {
+  	visualSLAM::Graph newFactors;
+  	// Factor for odometry measurements, simulated by adding Gaussian noise to the ground-truth.
+  	Pose3 odoMeasurement =  data.odometry*Pose3::Expmap(data.noiseX->sample());
+  	newFactors.push_back( boost::shared_ptr<BetweenFactor<Pose3> >(
+  			new BetweenFactor<Pose3>(X(i-1), X(i), odoMeasurement, data.noiseX)));
+  	// Factors for visual measurements
+  	for (size_t j=0; j<data.z[i].size(); ++j) {
+  		newFactors.addMeasurement(data.z[i][j], data.noiseZ, X(i), L(j), data.sK);
+  	}
+
+    // Initial estimates for the new node Xi, simulated by Gaussian noises
+  	Values initials;
+  	initials.insert(X(i), currentEstimate.at<Pose3>(X(i-1))*odoMeasurement);
+
+  	// update ISAM
+  	isam.update(newFactors, initials);
+  	currentEstimate = isam.estimate();
+  	cout << "****************************************************" << endl;
+  	cout << "Pose " << i << ": " << endl;
+  	currentEstimate.print("Current estimate: ");
+  }
+
+  return 0;
+}
+/* ************************************************************************* */
+