diff --git a/python/gtsam_unstable/examples/LocalizationExample.py b/python/gtsam_unstable/examples/LocalizationExample.py
new file mode 100644
index 000000000..ad8266d6d
--- /dev/null
+++ b/python/gtsam_unstable/examples/LocalizationExample.py
@@ -0,0 +1,68 @@
+"""
+A simple 2D pose slam example with "GPS" measurements
+  - The robot moves forward 2 meter each iteration
+  - The robot initially faces along the X axis (horizontal, to the right in 2D)
+  - We have full odometry between pose
+  - We have "GPS-like" measurements implemented with a custom factor
+"""
+import numpy as np
+
+import gtsam
+from gtsam import BetweenFactorPose2, Pose2, noiseModel
+from gtsam_unstable import PartialPriorFactorPose2
+
+
+def main():
+    # 1. Create a factor graph container and add factors to it.
+    graph = gtsam.NonlinearFactorGraph()
+
+    # 2a. Add odometry factors
+    # For simplicity, we will use the same noise model for each odometry factor
+    odometryNoise = noiseModel.Diagonal.Sigmas(np.asarray([0.2, 0.2, 0.1]))
+
+    # Create odometry (Between) factors between consecutive poses
+    graph.push_back(
+        BetweenFactorPose2(1, 2, Pose2(2.0, 0.0, 0.0), odometryNoise))
+    graph.push_back(
+        BetweenFactorPose2(2, 3, Pose2(2.0, 0.0, 0.0), odometryNoise))
+
+    # 2b. Add "GPS-like" measurements
+    # We will use PartialPrior factor for this.
+    unaryNoise = noiseModel.Diagonal.Sigmas(np.array([0.1,
+                                                      0.1]))  # 10cm std on x,y
+
+    graph.push_back(
+        PartialPriorFactorPose2(1, [0, 1], np.asarray([0.0, 0.0]), unaryNoise))
+    graph.push_back(
+        PartialPriorFactorPose2(2, [0, 1], np.asarray([2.0, 0.0]), unaryNoise))
+    graph.push_back(
+        PartialPriorFactorPose2(3, [0, 1], np.asarray([4.0, 0.0]), unaryNoise))
+    graph.print("\nFactor Graph:\n")
+
+    # 3. Create the data structure to hold the initialEstimate estimate to the solution
+    # For illustrative purposes, these have been deliberately set to incorrect values
+    initialEstimate = gtsam.Values()
+    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")
+
+    # 4. Optimize using Levenberg-Marquardt optimization. The optimizer
+    # accepts an optional set of configuration parameters, controlling
+    # things like convergence criteria, the type of linear system solver
+    # to use, and the amount of information displayed during optimization.
+    # Here we will use the default set of parameters.  See the
+    # documentation for the full set of parameters.
+    optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate)
+    result = optimizer.optimize()
+    result.print("Final Result:\n")
+
+    # 5. Calculate and print marginal covariances for all variables
+    marginals = gtsam.Marginals(graph, result)
+    print("x1 covariance:\n", marginals.marginalCovariance(1))
+    print("x2 covariance:\n", marginals.marginalCovariance(2))
+    print("x3 covariance:\n", marginals.marginalCovariance(3))
+
+
+if __name__ == "__main__":
+    main()