diff --git a/tests/testTranslationRecovery.cpp b/tests/testTranslationRecovery.cpp
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+++ b/tests/testTranslationRecovery.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 testTranslationRecovery.cpp
+ * @author Frank Dellaert
+ * @date March 2020
+ * @brief test recovering translations when rotations are given.
+ */
+
+#include <gtsam/slam/dataset.h>
+// #include <gtsam/sam/TranslationFactor.h>
+#include <gtsam/geometry/Point3.h>
+#include <gtsam/geometry/unit3.h>
+#include <gtsam/inference/Symbol.h>
+#include <gtsam/linear/NoiseModel.h>
+#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
+#include <gtsam/nonlinear/NonlinearFactorGraph.h>
+#include <gtsam/nonlinear/Values.h>
+
+#include <CppUnitLite/TestHarness.h>
+
+using namespace std;
+using namespace gtsam;
+
+class TranslationFactor {};
+
+// Set up an optimization problem for the unknown translations Ti in the world
+// coordinate frame, given the known camera attitudes wRi with respect to the
+// world frame, and a set of (noisy) translation directions of type Unit3,
+// aZb. The measurement equation is
+//    aZb = Unit3(aRw * (Tb - Ta))   (1)
+// i.e., aZb is the normalized translation of B's origin in the camera frame A.
+// It is clear that we cannot recover the scale, nor the absolute position, so
+// the gauge freedom in this case is 3 + 1 = 4. We fix these by taking fixing
+// the translations Ta and Tb associated with the first measurement aZb,
+// clamping them to their initial values as given to this method. If no initial
+// values are given, we use the origin for Tb and set Tb to make (1) come
+// through, i.e.,
+//    Tb = s * wRa * Point3(aZb)     (2)
+// where s is an arbitrary scale that can be supplied, default 1.0. Hence, two
+// versions are supplied below corresponding to whether we have initial values
+// or not. Because the latter one is called from the first one, this is prime.
+
+void recoverTranslations(const double scale = 1.0) {
+  //   graph.emplace_shared<TranslationFactor>(m.second, unit2, m.first,
+  //   P(j));
+}
+
+using KeyPair = pair<Key, Key>;
+
+/**
+ * @brief Simulate translation direction measurements
+ *
+ * @param poses SE(3) ground truth poses stored as Values
+ * @param edges pairs (a,b) for which a measurement aZb will be generated.
+ */
+vector<Unit3> simulateMeasurements(const Values& poses,
+                                   const vector<KeyPair>& edges) {
+  vector<Unit3> measurements;
+  for (auto edge : edges) {
+    Key a, b;
+    std::tie(a, b) = edge;
+    Pose3 wTa = poses.at<Pose3>(a), wTb = poses.at<Pose3>(b);
+    Point3 Ta = wTa.translation(), Tb = wTb.translation();
+    auto aRw = wTa.rotation().inverse();
+    Unit3 aZb = Unit3(aRw * (Tb - Ta));
+    measurements.push_back(aZb);
+  }
+  return measurements;
+}
+
+/* ************************************************************************* */
+// We read the BAL file, which has 3 cameras in it, with poses. We then assume
+// the rotations are correct, but translations have to be estimated from
+// translation directions only. Since we have 3 cameras, A, B, and C, we can at
+// most create three relative measurements, let's call them aZb, aZc, and bZc.
+// These will be of type Unit3. We then call `recoverTranslations` which sets up
+// an optimization problem for the three unknown translations.
+TEST(TranslationRecovery, BAL) {
+  string filename = findExampleDataFile("dubrovnik-3-7-pre");
+  SfM_data db;
+  bool success = readBAL(filename, db);
+  if (!success) throw runtime_error("Could not access file!");
+
+  SharedNoiseModel unit2 = noiseModel::Unit::Create(2);
+  NonlinearFactorGraph graph;
+
+  size_t i = 0;
+  Values poses;
+  for (auto camera : db.cameras) {
+    GTSAM_PRINT(camera);
+    poses.insert(i++, camera.pose());
+  }
+  Pose3 wTa = poses.at<Pose3>(0), wTb = poses.at<Pose3>(1),
+        wTc = poses.at<Pose3>(2);
+  Point3 Ta = wTa.translation(), Tb = wTb.translation(), Tc = wTc.translation();
+  auto measurements = simulateMeasurements(poses, {{0, 1}, {0, 2}, {1, 2}});
+  auto aRw = wTa.rotation().inverse();
+  Unit3 aZb = measurements[0];
+  EXPECT(assert_equal(Unit3(aRw * (Tb - Ta)), aZb));
+  Unit3 aZc = measurements[1];
+  EXPECT(assert_equal(Unit3(aRw * (Tc - Ta)), aZc));
+  //   Values initial = initialCamerasAndPointsEstimate(db);
+
+  //   LevenbergMarquardtOptimizer lm(graph, initial);
+
+  //   Values actual = lm.optimize();
+  //   double actualError = graph.error(actual);
+  //   EXPECT_DOUBLES_EQUAL(0.0199833, actualError, 1e-5);
+}
+
+/* ************************************************************************* */
+int main() {
+  TestResult tr;
+  return TestRegistry::runAllTests(tr);
+}
+/* ************************************************************************* */