diff --git a/examples/ISAM2_SmartFactorStereo_IMU.cpp b/examples/ISAM2_SmartFactorStereo_IMU.cpp
index 968f1edc7..f39e9f4eb 100644
--- a/examples/ISAM2_SmartFactorStereo_IMU.cpp
+++ b/examples/ISAM2_SmartFactorStereo_IMU.cpp
@@ -15,6 +15,7 @@
 #include <fstream>
 #include <iostream>
 #include <sstream>
+#include <string>
 #include <vector>
 
 using namespace std;
@@ -224,7 +225,7 @@ int main(int argc, char* argv[]) {
 
       smartFactors[landmark]->add(StereoPoint2(xl, xr, y), X(frame), K);
     } else {
-      throw runtime_error("unexpected data type: " + type);
+      throw runtime_error("unexpected data type: " + string(1, type));
     }
 
     lastFrame = frame;
diff --git a/examples/Pose2SLAMStressTest.cpp b/examples/Pose2SLAMStressTest.cpp
new file mode 100644
index 000000000..0f306b7f4
--- /dev/null
+++ b/examples/Pose2SLAMStressTest.cpp
@@ -0,0 +1,84 @@
+/**
+ * @file Pose2SLAMStressTest.cpp
+ * @brief Test GTSAM on large open-loop chains
+ * @date May 23, 2018
+ * @author Wenqiang Zhou
+ */
+
+// Create N 3D poses, add relative motion between each consecutive poses. (The
+// relative motion is simply a unit translation(1, 0, 0), no rotation). For each
+// each pose, add some random noise to the x value of the translation part.
+// Use gtsam to create a prior factor for the first pose and N-1 between factors
+// and run optimization.
+
+#include <gtsam/geometry/Cal3_S2Stereo.h>
+#include <gtsam/geometry/Pose3.h>
+#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/nonlinear/NonlinearEquality.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/nonlinear/Values.h>
+#include <gtsam/slam/BetweenFactor.h>
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/slam/StereoFactor.h>
+
+#include <random>
+
+using namespace std;
+using namespace gtsam;
+
+void testGtsam(int numberNodes) {
+  std::random_device rd;
+  std::mt19937 e2(rd());
+  std::uniform_real_distribution<> dist(0, 1);
+
+  vector<Pose3> poses;
+  for (int i = 0; i < numberNodes; ++i) {
+    Matrix4 M;
+    double r = dist(e2);
+    r = (r - 0.5) / 10 + i;
+    M << 1, 0, 0, r, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1;
+    poses.push_back(Pose3(M));
+  }
+
+  // prior factor for the first pose
+  auto priorModel = noiseModel::Isotropic::Variance(6, 1e-4);
+  Matrix4 first_M;
+  first_M << 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1;
+  Pose3 first = Pose3(first_M);
+
+  NonlinearFactorGraph graph;
+  graph.add(PriorFactor<Pose3>(0, first, priorModel));
+
+  // vo noise model
+  auto VOCovarianceModel = noiseModel::Isotropic::Variance(6, 1e-3);
+
+  // relative VO motion
+  Matrix4 vo_M;
+  vo_M << 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1;
+  Pose3 relativeMotion(vo_M);
+  for (int i = 0; i < numberNodes - 1; ++i) {
+    graph.add(
+        BetweenFactor<Pose3>(i, i + 1, relativeMotion, VOCovarianceModel));
+  }
+
+  // inital values
+  Values initial;
+  for (int i = 0; i < numberNodes; ++i) {
+    initial.insert(i, poses[i]);
+  }
+
+  LevenbergMarquardtParams params;
+  params.setVerbosity("ERROR");
+  params.setOrderingType("METIS");
+  params.setLinearSolverType("MULTIFRONTAL_CHOLESKY");
+  LevenbergMarquardtOptimizer optimizer(graph, initial, params);
+  auto result = optimizer.optimize();
+}
+
+int main(int args, char* argv[]) {
+  int numberNodes = stoi(argv[1]);
+  cout << "number of_nodes: " << numberNodes << endl;
+  testGtsam(numberNodes);
+  return 0;
+}