diff --git a/gtsam/navigation/tests/testImuFactor.cpp b/gtsam/navigation/tests/testImuFactor.cpp
index 4b55e0ee3..0b672608d 100644
--- a/gtsam/navigation/tests/testImuFactor.cpp
+++ b/gtsam/navigation/tests/testImuFactor.cpp
@@ -922,79 +922,6 @@ TEST(ImuFactor, PredictRotation) {
     EXPECT(assert_equal(Vector(expectedVelocity), Vector(poseVelocity.velocity)));
 }
 
-/* ************************************************************************* */
-#include <gtsam/nonlinear/NonlinearFactorGraph.h>
-#include <gtsam/slam/BetweenFactor.h>
-#include <gtsam/slam/PriorFactor.h>
-#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
-
-TEST(ImuFactor, CheckBiasCorrection) {
-  int numFactors = 2;
-  int numSamplesPreint = 1;
-  double g = 9.81;
-  // Measurements. Body frame and nav frame are both z-up
-  Vector3 measuredOmega; measuredOmega << 0, 0.3, 0.0;
-  Vector3 measuredAcc; measuredAcc << 0, 0, g;
-  Vector3 gravity; gravity << 0, 0, -g;
-
-  // Set up noise and other test params
-  imuBias::ConstantBias zeroBias(Vector3(0, 0, 0), Vector3(0.0, 0, 0)); // Biases (acc, rot)
-  Vector6 noiseBetweenBiasSigma; noiseBetweenBiasSigma << Vector3(2.0e-5,  2.0e-5,  2.0e-5), Vector3(3.0e-6, 3.0e-6, 3.0e-6);
-  SharedDiagonal biasNoiseModel = noiseModel::Diagonal::Sigmas(noiseBetweenBiasSigma);
-  Matrix3 accCov = 1e-4 * I_3x3;
-  Matrix3 gyroCov = 1e-6 * I_3x3;
-  Matrix3 integrationCov = 1e-8 * I_3x3;
-  double deltaT = 0.005;
-  Vector3 omegaCoriolis; omegaCoriolis << 0, 0, 0;
-
-  //   Specify noise values on priors
-  Vector6 priorNoisePoseSigmas((Vector(6) << 0.001, 0.001, 0.001, 0.01, 0.01, 0.01).finished());
-  Vector3 priorNoiseVelSigmas((Vector(3) <<  0.5, 0.5, 0.5).finished());
-  Vector6 priorNoiseBiasSigmas((Vector(6) << 1, 1, 1, 1, 1, 1).finished());
-  SharedDiagonal priorNoisePose = noiseModel::Diagonal::Sigmas(priorNoisePoseSigmas);
-  SharedDiagonal priorNoiseVel = noiseModel::Diagonal::Sigmas(priorNoiseVelSigmas);
-  SharedDiagonal priorNoiseBias = noiseModel::Diagonal::Sigmas(priorNoiseBiasSigmas);
-  Vector3 zeroVel(0, 0.0, 0.0);
-
-  // Instantiate graph and values
-  Values values;
-  NonlinearFactorGraph graph;
-
-  // Add prior factor and values
-  graph.add(PriorFactor<Pose3> (X(0), Pose3(), priorNoisePose));
-  graph.add(PriorFactor<Vector3>(V(0), zeroVel, priorNoiseVel));
-  graph.add(PriorFactor<imuBias::ConstantBias>(B(0), zeroBias, priorNoiseBias));
-  values.insert(X(0), Pose3());
-  values.insert(V(0), zeroVel);
-  values.insert(B(0), zeroBias);
-
-  for (int i = 1; i < numFactors; i++) {
-	  // Preintegrate measurements
-    ImuFactor::PreintegratedMeasurements pre_int_data = ImuFactor::PreintegratedMeasurements(imuBias::ConstantBias(Vector3(0, 0.0, 0.0),
-        Vector3(0.0, 0.0, 0.0)), accCov, gyroCov, integrationCov, true);
-    for (int j = 0; j< numSamplesPreint; ++j)   pre_int_data.integrateMeasurement(measuredAcc, measuredOmega, deltaT);
-
-    // Create and add factor
-    ImuFactor factor(X(i-1), V(i-1), X(i), V(i), B(i-1), pre_int_data, gravity, omegaCoriolis);
-    graph.add(factor);
-    graph.add(BetweenFactor<imuBias::ConstantBias>(B(i-1), B(i), zeroBias, biasNoiseModel));
-
-    if (i == 30) graph.add(PriorFactor<Pose3>(X(i), Pose3(), priorNoisePose));
-
-    // Add values
-    values.insert(X(i), Pose3());
-    values.insert(V(i), zeroVel);
-    values.insert(B(i), zeroBias);
-  }
-  // Solve graph and find estimated bias
-  Values results = LevenbergMarquardtOptimizer(graph, values).optimize();
-  imuBias::ConstantBias biasActual = results.at<imuBias::ConstantBias>(B(numFactors-1));
-//
-//  // set expected bias
-//  imuBias::ConstantBias biasExpected(Vector3(0,0,0), Vector3(0, 0.3, 0.0));
-//  EXPECT(assert_equal(biasExpected, biasActual, 1e-2));
-}
-
 /* ************************************************************************* */
   int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
 /* ************************************************************************* */