diff --git a/examples/Pose3Localization.cpp b/examples/Pose3Localization.cpp
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+/* ----------------------------------------------------------------------------
+
+ * 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 Pose3SLAMExample_initializePose3.cpp
+ * @brief A 3D Pose SLAM example that reads input from g2o, and initializes the Pose3 using InitializePose3
+ * Syntax for the script is ./Pose3SLAMExample_initializePose3 input.g2o output.g2o
+ * @date Aug 25, 2014
+ * @author Luca Carlone
+ */
+
+#include <gtsam/slam/dataset.h>
+#include <gtsam/slam/BetweenFactor.h>
+#include <gtsam/slam/PriorFactor.h>
+#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
+#include <fstream>
+#include <gtsam/nonlinear/Marginals.h>
+
+using namespace std;
+using namespace gtsam;
+
+int main(const int argc, const char *argv[]) {
+
+  // Read graph from file
+  string g2oFile;
+  if (argc < 2)
+    g2oFile = findExampleDataFile("pose3Localizationexample.txt");
+  else
+    g2oFile = argv[1];
+
+  NonlinearFactorGraph::shared_ptr graph;
+  Values::shared_ptr initial;
+  bool is3D = true;
+  boost::tie(graph, initial) = readG2o(g2oFile, is3D);
+
+  // Add prior on the first key
+  NonlinearFactorGraph graphWithPrior = *graph;
+  noiseModel::Diagonal::shared_ptr priorModel = //
+      noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
+  Key firstKey = 0;
+  for(const Values::ConstKeyValuePair& key_value: *initial) {
+    std::cout << "Adding prior to g2o file " << std::endl;
+    firstKey = key_value.key;
+    graphWithPrior.add(PriorFactor<Pose3>(firstKey, Pose3(), priorModel));
+    break;
+  }
+
+  std::cout << "Optimizing the factor graph" << std::endl;
+  GaussNewtonParams params;
+  params.setVerbosity("TERMINATION"); // this will show info about stopping conditions
+  GaussNewtonOptimizer optimizer(graphWithPrior, *initial, params);
+  Values result = optimizer.optimize();
+  std::cout << "Optimization complete" << std::endl;
+
+  std::cout << "initial error=" <<graph->error(*initial)<< std::endl;
+  std::cout << "final error=" <<graph->error(result)<< std::endl;
+
+  if (argc < 3) {
+    result.print("result");
+  } else {
+    const string outputFile = argv[2];
+    std::cout << "Writing results to file: " << outputFile << std::endl;
+    writeG2o(*graph, result, outputFile);
+    std::cout << "done! " << std::endl;
+  }
+
+  // Calculate and print marginal covariances for all variables
+  Marginals marginals(*graph, result);
+  for(const auto& key_value: result) {
+    auto p = dynamic_cast<const GenericValue<Pose3>*>(&key_value.value);
+    if (!p) continue;
+    std::cout << marginals.marginalCovariance(key_value.key) << endl;
+  }
+  return 0;
+}