diff --git a/examples/OdometryExample.cpp b/examples/OdometryExample.cpp
index f0874bedb..e420a0be9 100644
--- a/examples/OdometryExample.cpp
+++ b/examples/OdometryExample.cpp
@@ -27,11 +27,6 @@
 #include <gtsam/geometry/Pose2.h>
 #include <gtsam/geometry/Point2.h>
 
-// Each variable in the system (poses) must be identified with a unique key.
-// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
-// Here we will use symbols
-#include <gtsam/nonlinear/Symbol.h>
-
 // In GTSAM, measurement functions are represented as 'factors'. Several common factors
 // have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
 // Here we will use Between factors for the relative motion described by odometry measurements.
@@ -60,35 +55,35 @@
 // for each variable, held in a Values container.
 #include <gtsam/nonlinear/Values.h>
 
-
 using namespace std;
 using namespace gtsam;
 
 int main(int argc, char** argv) {
 
-  // Create a factor graph container
+  // Create an empty nonlinear factor graph
   NonlinearFactorGraph graph;
 
   // Add a prior on the first pose, setting it to the origin
   // A prior factor consists of a mean and a noise model (covariance matrix)
-  Pose2 prior(0.0, 0.0, 0.0); // prior at origin
+  Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
   noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
-  graph.add(PriorFactor<Pose2>(Symbol('x', 1), prior, priorNoise));
+  graph.add(PriorFactor<Pose2>(1, priorMean, priorNoise));
 
   // Add odometry factors
+  Pose2 odometry(2.0, 0.0, 0.0);
   // For simplicity, we will use the same noise model for each odometry factor
   noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
   // Create odometry (Between) factors between consecutive poses
-  graph.add(BetweenFactor<Pose2>(Symbol('x', 1), Symbol('x', 2), Pose2(2.0, 0.0, 0.0), odometryNoise));
-  graph.add(BetweenFactor<Pose2>(Symbol('x', 2), Symbol('x', 3), Pose2(2.0, 0.0, 0.0), odometryNoise));
+  graph.add(BetweenFactor<Pose2>(1, 2, odometry, odometryNoise));
+  graph.add(BetweenFactor<Pose2>(2, 3, odometry, odometryNoise));
   graph.print("\nFactor Graph:\n"); // print
 
   // Create the data structure to hold the initialEstimate estimate to the solution
   // For illustrative purposes, these have been deliberately set to incorrect values
   Values initialEstimate;
-  initialEstimate.insert(Symbol('x', 1), Pose2(0.5, 0.0, 0.2));
-  initialEstimate.insert(Symbol('x', 2), Pose2(2.3, 0.1, -0.2));
-  initialEstimate.insert(Symbol('x', 3), Pose2(4.1, 0.1, 0.1));
+  initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
+  initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2));
+  initialEstimate.insert(3, Pose2(4.1, 0.1, 0.1));
   initialEstimate.print("\nInitial Estimate:\n"); // print
 
   // optimize using Levenberg-Marquardt optimization
@@ -97,9 +92,9 @@ int main(int argc, char** argv) {
 
   // Calculate and print marginal covariances for all variables
   Marginals marginals(graph, result);
-  cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(Symbol('x', 1)) << endl;
-  cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(Symbol('x', 2)) << endl;
-  cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(Symbol('x', 3)) << endl;
+  cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(1) << endl;
+  cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(2) << endl;
+  cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(3) << endl;
 
   return 0;
 }