diff --git a/gtsam/navigation/ImuFactor.h b/gtsam/navigation/ImuFactor.h
index 40bc15e11..7254838fd 100644
--- a/gtsam/navigation/ImuFactor.h
+++ b/gtsam/navigation/ImuFactor.h
@@ -37,13 +37,9 @@ typedef ManifoldPreintegration PreintegrationType;
 
 /*
  * If you are using the factor, please cite:
- * L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, "Eliminating
- * conditionally independent sets in factor graphs: a unifying perspective based
- * on smart factors", Int. Conf. on Robotics and Automation (ICRA), 2014.
- *
- * C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration on
- * Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
- * Robotics: Science and Systems (RSS), 2015.
+ * Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza,
+ * "On-Manifold Preintegration for Real-Time Visual-Inertial Odometry", IEEE
+ * Transactions on Robotics, 2017.
  *
  * REFERENCES:
  * [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
@@ -54,8 +50,8 @@ typedef ManifoldPreintegration PreintegrationType;
  * [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
  *     Computation of the Jacobian Matrices", Tech. Report, 2013.
  *     Available in this repo as "PreintegratedIMUJacobians.pdf".
- * [4] C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration on
- *     Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
+ * [4] C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration
+ * on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
  *     Robotics: Science and Systems (RSS), 2015.
  */
 
diff --git a/gtsam/navigation/README.md b/gtsam/navigation/README.md
new file mode 100644
index 000000000..fe99d8f73
--- /dev/null
+++ b/gtsam/navigation/README.md
@@ -0,0 +1,42 @@
+# Navigation Factors
+
+This directory contains factors related to navigation, including various IMU factors.
+
+## IMU Factor:
+
+![IMU Factor Diagram](https://www.mathworks.com/help/examples/shared_positioning/win64/FactorGraphPedestrianIMUGPSLocalizationExample_02.png)
+
+The `ImuFactor` is a 5-ways factor involving previous state (pose and velocity of
+the vehicle at previous time step), current state (pose and velocity at
+current time step), and the bias estimate.
+Following the preintegration
+scheme proposed in [2], the `ImuFactor` includes many IMU measurements, which
+are "summarized" using the PreintegratedIMUMeasurements class.
+The figure above, courtesy of [Mathworks' navigation toolbox](https://www.mathworks.com/help/nav/index.html), which are also using our work, shows the factor graph fragment for two time slices.
+
+Note that this factor does not model "temporal consistency" of the biases
+(which are usually slowly varying quantities), which is up to the caller.
+See also `CombinedImuFactor` for a class that does this for you.
+
+If you are using the factor, please cite:
+> Christian Forster, Luca Carlone, Frank Dellaert, and Davide Scaramuzza, "On-Manifold Preintegration for Real-Time Visual-Inertial Odometry", IEEE Transactions on Robotics, 2017.
+
+## REFERENCES:
+1. G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
+    Volume 2, 2008.
+2. T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for
+    High-Dynamic Motion in Built Environments Without Initial Conditions",
+    TRO, 28(1):61-76, 2012.
+3. L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
+    Computation of the Jacobian Matrices", Tech. Report, 2013.
+    Available in this repo as "PreintegratedIMUJacobians.pdf".
+4. C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, "IMU Preintegration on
+    Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation",
+    Robotics: Science and Systems (RSS), 2015.
+
+## The Attitude Factor
+
+The `AttitudeFactor` in GTSAM is a factor that constrains the orientation (attitude) of a robot or sensor platform based on directional measurements. Both `Rot3` and `Pose3` versions are available. 
+
+Written up in detail with the help of ChatGPT [here](AttitudeFactor.md).
+