diff --git a/cpp/testPose2Graph.cpp b/cpp/testPose2Graph.cpp
index 86ae8bb6b..50a3c499c 100644
--- a/cpp/testPose2Graph.cpp
+++ b/cpp/testPose2Graph.cpp
@@ -4,6 +4,8 @@
 
 #include <CppUnitLite/TestHarness.h>
 #include "Pose2Graph.h"
+#include "NonlinearFactorGraph-inl.h"
+#include "GaussianFactorGraph.h"
 
 using namespace std;
 using namespace gtsam;
@@ -18,15 +20,59 @@ TEST( Pose2Graph, constructor )
 			0.0, 0.25, 0.0,
 			0.0, 0.0, 0.01
 			);
-	Pose2Factor constraint("p1","p2",measured, measurement_covariance);
+	Pose2Factor constraint("x1","x2",measured, measurement_covariance);
 	Pose2Graph graph;
-	graph.push_back(Pose2Factor::shared_ptr(new Pose2Factor("p1","p2",measured, measurement_covariance)));
+	graph.push_back(Pose2Factor::shared_ptr(new Pose2Factor("x1","x2",measured, measurement_covariance)));
 	// get the size of the graph
 	size_t actual = graph.size();
 	// verify
 	size_t expected = 1;
 	CHECK(actual == expected);
 
+}
+TEST( Pose2Graph, linerization )
+{
+	// create a factor between unknown poses p1 and p2
+	Pose2 measured(2,2,M_PI_2);
+	Matrix measurement_covariance = Matrix_(3,3,
+			0.25, 0.0, 0.0,
+			0.0, 0.25, 0.0,
+			0.0, 0.0, 0.01
+	);
+	Pose2Factor constraint("x1","x2",measured, measurement_covariance);
+	Pose2Graph graph;
+	graph.push_back(Pose2Factor::shared_ptr(new Pose2Factor("x1","x2",measured, measurement_covariance)));
+
+	// Choose a linearization point
+	Pose2 p1(1.1,2,M_PI_2); // robot at (1.1,2) looking towards y (ground truth is at 1,2, see testPose2)
+	Pose2 p2(-1,4.1,M_PI);  // robot at (-1,4) looking at negative (ground truth is at 4.1,2)
+	Pose2Config config;
+	config.insert("x1",p1);
+	config.insert("x2",p2);
+	// Linearize
+	GaussianFactorGraph lfg_linearized = graph.linearize(config);
+	lfg_linearized.print("lfg_actual");
+
+	// the expected linear factor
+	GaussianFactorGraph lfg_expected;
+	Matrix A1 = Matrix_(3,3,
+			0.0,	2.0, -4.2,
+			-2.0,	0.0,  4.2,
+			0.0,	0.0,  10.0);
+
+	Matrix A2 = Matrix_(3,3,
+			0.0,	-2.0,  0.0,
+			2.0,	0.0,   0.0,
+			0.0,	0.0,  -10.0);
+
+
+	double sigma = 1;
+	Vector b = Vector_(3,-0.1,-0.1,0.0);
+	lfg_expected.add("x1", A1, "x2", A2, b, sigma);
+
+
+	CHECK(lfg_expected.equals(lfg_linearized));
+
 }
 /* ************************************************************************* */
 int main() {