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diff --git a/docs/developer-guide/rosplane/controller/controller-general-overview.md b/docs/developer-guide/rosplane/controller/controller-general-overview.md
index 6d8f607..d8c2210 100644
--- a/docs/developer-guide/rosplane/controller/controller-general-overview.md
+++ b/docs/developer-guide/rosplane/controller/controller-general-overview.md
@@ -83,7 +83,7 @@ A table of arguments and parameter files that work out of the box is given in th
 !!! note 
     Filepaths will need to be altered to work.
 !!! note
-    The most common way of running the controller is in through a launch file with the rest of the ROSplane pipeline running as well.
+    The most common way of running the controller is through a launch file with the rest of the ROSplane pipeline running as well.
     See the ROSplane Overview in the Developer and User Guides for more details.
 
 
diff --git a/docs/developer-guide/rosplane/estimator/estimator-base.md b/docs/developer-guide/rosplane/estimator/estimator-base.md
new file mode 100644
index 0000000..b7a88df
--- /dev/null
+++ b/docs/developer-guide/rosplane/estimator/estimator-base.md
@@ -0,0 +1,49 @@
+# Estimator Base
+
+## Overview
+
+The estimator base implements the basic ROS interfaces for the estimator.
+This includes setting up subscribers, publishers and initializing parameter management.
+The idea of the base class, is that all interfacing with ROS and shared resources across all inheritance levels happens or are contained in this class.
+
+## ROS Interfaces
+
+The estimator has the following ROS interfaces.
+
+| ![Diagram of Estimator ROS Sensors](../../../assets/estimator_assets/estimator_ros_input_output.png "Estimator ROS Sensors") |
+|:--:|
+|*Figure 1: Estimator's ROS interactions.*|
+
+The estimator has several ROS interfaces that are tracked as member variables of the `estimator_base` class.
+They are summarized in the table below:
+
+| ROS Interface | Topic | Explanation | Message Type |
+|:------:|:-------:| :---: | :---: |
+| <div style="white-space: nowrap;">`vehicle_state_pub_`<div> | `/estimated_state` | Publishes the estimated state of the vehicle. | State.msg |
+| <div style="white-space: nowrap;">`gnss_fix_sub_`<div> | `/navsat_compat/fix` | Subcribes to the GNSS position information. | NavSatFix.msg |
+| <div style="white-space: nowrap;">`gnss_vel_sub_`<div> | `/navsat_compat/vel` | Subcribes to the GNSS velocity information. | TwistStamped.msg |
+| <div style="white-space: nowrap;">`imu_sub_`<div> | `/imu/data` | Subcribes to the IMU data (both Gyro and Accel). | Imu.msg |
+| <div style="white-space: nowrap;">`baro_sub_`<div> | `/baro` | Subcribes to the barometer pressure information. | Barometer.msg |
+| <div style="white-space: nowrap;">`airspeed_sub_`<div> | `/airspeed` | Subcribes to the differential pressure information. | Airspeed.msg |
+| <div style="white-space: nowrap;">`status_sub_`<div> | `/status` | Subcribes to the aircraft status information. | Status.msg |
+
+!!! note 
+    The NavSatFix, TwistStamped and Imu messages are from standard ROS message packages. Barometer, Airspeed and Status messages are from `rosflight_msgs`.
+
+## Parameters
+
+| **Parameter** | **Explanation** | **Type** | **Range** |
+| :---: | :---: | :---: | :---: |
+| `rho` | The density of the air. | double | ~1.225 $\frac{kg}{m^3}$ |
+| `gravity` | The acceleration due to gravity. | double | ~9.81 $\frac{m}{s^2}$ |
+| `estimator_update_frequency` | The frequency that the estimator will run estimations. | double | $\geq 100$ Hz |
+| `gps_ground_speed_threshold` | This determines when the aircraft is moving fast enough to get a course estimate. | double | $\geq$ 0.3 $\frac{m}{s}$ |
+| `baro_measurement_gate` | The maximum allowable instantaneous change in barometer pressure measurement. | double | ~1 meter |
+| `airspeed_measurement_gate` | The maximum allowable instantaneous change in differential pressure measurement. | double | ~5 $\frac{m}{s}$ |
+| `baro_calibration_count` | The number of barometer measurements used to do calibration. | int | $\geq 100$ |
+
+## Modifying the Estimator
+
+Update when full state estimator and continuous-discrete are both implemented and a swap between them works.
+
+
diff --git a/docs/developer-guide/rosplane/estimator/estimator-example.md b/docs/developer-guide/rosplane/estimator/estimator-example.md
new file mode 100644
index 0000000..5ba15c2
--- /dev/null
+++ b/docs/developer-guide/rosplane/estimator/estimator-example.md
@@ -0,0 +1,387 @@
+# Estimator Example
+
+<!-- TODO: rename this to continuous discrete once full state is integrated. -->
+
+## Overview
+
+The `estimator_example` class implements a continuous-discrete Kalman filter as described in section 8.5 of the [UAV book](https://github.com/randybeard/mavsim_public) or 8.6 and 8.7 of volume one of the same book.
+Specifically, this estimator uses algorithm 2 of chapter 8 in Volume 1.
+It utilizes a two stage estimation process along with low pass filtering the inversion of a few sensor models and direct measurements.
+The roll and pitch of the aircraft are estimated first.
+This is called the attitude estimation step though not all of the attitude is estimated here.
+The other states are then estimated as a all at once.
+This is called the position estimation step, though more than just position is estimated during this step.
+The estimator runs on a set timer with a configurable frequency (see Parameters section for details).
+
+The estimator makes large use of something called a pseudo-measurement.
+These measures assume that the side slip angle is zero and the corresponding wind triangle solution.
+This allows us to take these measures and then find the wind in the north and east directions along with the yaw of the aircraft.
+They are more fully explored in section 8.11.4 of the UAV book.
+
+## Nomenclature
+
+| Symbol | Meaning | Range |
+|:------:|:-------:| :---: |
+|$\large{\chi}$| Course | $[-\pi,\pi)$ |
+|$\large{\phi}$| Roll | $[-\pi,\pi)$ |
+|$\large{\theta}$| Theta | $[-\pi,\pi)$ |
+|$\large{\psi}$| Yaw | $[-\pi,\pi)$ |
+|$\large{h}$| Altitude | - |
+|$\large{p}$| Roll Rate | - |
+|$\large{q}$| Pitch Rate | - |
+|$\large{r}$| Yaw Rate | - |
+|$\large{V_a}$| Airspeed | $\geq 0$ |
+|$\large{w_n}$| Wind North | - |
+|$\large{w_e}$| Wind East | - |
+
+## Sensor Model Inversion
+
+The roll, pitch and yaw rates are directly measured by the rate gyro and then low pass filtered.
+The low pass filter is a simple alpha filter described by:
+
+$$
+    a_{n} = a_{n-1} \alpha + (1 - \alpha) a_{measured}
+$$
+
+Where $a$ is the state.
+This filter technique is used on the measurements before they are used in an estimate throughout the estimator.
+
+The following are calculated directly from a model of the sensor:
+
+<center>
+
+| State | Measured Value | Model Equation | Inversion Equation |
+|:--:|:--:|:--:|:--:|
+| $h$ - altitude | $P$ - absolute pressure (Pa) | $P = \rho_{air} gh$ | $h = \frac{\rho_{air} g}{P}$|
+| $V_a$ - airspeed | $\Delta P$ - differential pressure (Pa) | $\Delta P = \frac{1}{2} \rho_{air} V_a^2$ | $V_a = \sqrt{\frac{2}{\rho_{air} g} \Delta P}$|
+
+</center>
+
+These values are then used in the first step of the estimator.
+
+## Attitude Estimation
+
+The first step in estimating the attitude is understanding how we propagate our guess between measurements.
+Our estimates are on roll angle, $\phi$, and on pitch angle, $\theta$.
+These estimates are part of the state $\hat{x}_a$, where the hat over the variable indicates an estimate.
+
+### Propagation
+
+At each call of the estimation algorithm, the estimate from the previous time step is propagated to the next time step.
+The propagation step is broken up into $N$ smaller steps to yield an estimate for the current time step.
+$N$ is typically 10, meaning that the previous estimate that was updated by a measurement is updated in 10 steps instead of a single step to calculate the estimate at the current time step.
+The length of each of the $N$ steps is 1/$N$ the original time step.
+
+The attitude estimation is propagated according to the model $f$:
+
+\begin{equation}
+    f =
+    \begin{bmatrix}
+        \dot{\phi} = p + (q \sin{\phi} + r \cos{\phi}) \tan{\theta} \\
+        \dot{\theta} = q \cos{\phi} + r \sin{\phi})
+    \end{bmatrix}
+\end{equation}
+
+This propagated estimate is then used in the calculation of the Jacobian $A$.
+
+\begin{equation}
+    A =
+    \begin{bmatrix}
+        (q \cos{\phi} - r \sin{\phi})\tan{\theta} & \frac{q\sin{\phi} + r\cos{phi}}{\cos^2{\theta}} \\
+        0 & -q \sin{\phi} - r \cos{\phi} \\
+    \end{bmatrix}
+\end{equation}
+
+This Jacobian is then used to find a second-order approximation of the matrix exponential, $A_d$.
+
+\begin{equation}
+    A_d = I + \frac{T_s}{N} A + \frac{1}{2} \frac{T_s^2}{N^2} A^2
+\end{equation}
+
+Where $T_s$ is the length of a time step.
+$A_d$ is then used to propagate the actual covariance of the estimate.
+
+The process noise due to model uncertainty is defined in the matrix $Q$, but the process uncertainty due to the use of the rate gyro measurements $Q_g$, has not been adjusted for yet.
+This is done with the use of the matrix $G$ which takes into account the Coriolis effects of the gyro measurements.
+The measurement variance is transformed into process noise due to gyro measurements.
+All of these contribute into finding the current covariance of our estimate, $P$.
+This is done by the following equation:
+
+$$P_a = A_d P A_d^\top + (Q + G Q_g G^\top) \frac{T_s^2}{N^2}$$
+
+With this propagated estimate and covariance we are now ready for a measurement update.
+
+### Measurement Update
+
+A measurement update provides a check on our propagated estimate and we take this new information and fuse it into our estimate.
+The Kalman filter allows us to optimally adjust our estimate, our tuned process noises, and the noise characteristics of our sensor given the measurement.
+These noise characteristics are captured in a diagonal matrix, $R_{sensor}$.
+The entries are the variances and for the attitude step this is defined as:
+
+\begin{equation}
+    R_{accel} = 
+    \begin{bmatrix}
+        \sigma_{accel, x}^2 & 0 & 0 \\
+        0 & \sigma_{accel, y}^2 & 0 \\
+        0 & 0 & \sigma_{accel, z}^2 \\
+    \end{bmatrix}
+\end{equation}
+
+Using our estimate and a model set of equations $h$, we predict the measurements the accelerometer will produce.
+We will then compare the actual and predicted measurements and optimally adjust our estimate with the new information.
+Since measurements come in much faster than the model propagates the measurement step is run every time the propagated estimate is calculated.
+!!! note
+    The estimator asssumes that measurements come in faster than/as fast the estimation timer is calls for estimation updates.
+    If this is not the case performance can actually go down for the estimation if you increase the frequency of the estimation updates.
+The set of equations, $h$, that predict the 3 measurements of the accelerometer, $y$, is given by:
+
+\begin{equation}
+    h = 
+    \begin{bmatrix}
+        q V_a \sin{\theta} + g \sin{\theta} \\
+        r V_a \cos{\theta} - p V_a \sin{\theta} - g \cos{\theta} \sin{\phi} \\
+        -q V_a \cos{\theta} - g \cos{\theta} \cos{\phi} \\
+    \end{bmatrix}
+\end{equation}
+
+This yields a Jacobian $C$:
+
+\begin{equation}
+    C = 
+    \begin{bmatrix}
+        0 & q V_a \cos{\theta} + g \cos{\theta} \\
+        -g \cos{\phi}\cos{\theta} & -r V_a \sin{\theta} - p V_a \cos{\theta} + g \sin{\phi} \sin{theta} \\
+        g \sin{\phi}\cos{\theta} & (q V_a + g \cos{\phi}) \sin{\theta}
+    \end{bmatrix}
+\end{equation}
+
+Which is used in finding the Kalman gain $L$.
+An intermediate value is calculated called $S^{-1}$.
+This value is:
+
+\begin{equation}
+    S^{-1} = (R_{accel} + CPC^\top)^{-1}
+\end{equation}
+
+This intermediate value is then used to find $L$:
+
+\begin{equation}
+    L = PC^\top S^{-1}
+\end{equation}
+
+The optimal estimate is then found:
+
+\begin{equation}
+    \hat{x}_a^+ =  \hat{x}_a^- + L (y - h)
+\end{equation}
+
+Finally, we update the covariance from our new estimate:
+
+\begin{equation}
+    P^+ = (I - LC) P^- (I - LC)^\top + LR_{accel}L^\top
+\end{equation}
+
+We repeat this cycle until termination of the program.
+This estimation scheme split into two parts allows for a clear set of equations and how the estimates affect one another.
+We will now move on to the next portion of the estimator that estimates the rest of the states.
+
+## Position Estimation
+
+The position estimation step follows the same algorithm as previous.
+Only new values are used for each of the matrices.
+Those new entries are shown here, but reference the previous section for details on implementation.
+
+### Propagation
+
+The attitude estimation is propagated according to the model $f$:
+
+\begin{equation}
+    f =
+    \begin{bmatrix}
+        V_g \cos{\chi} \\
+        V_g \sin{\chi} \\
+        \dot{V_g} \\
+        0 \\
+        0 \\
+        \dot{\psi} \\
+    \end{bmatrix}
+\end{equation}
+
+Where,
+
+\begin{equation}
+    \dot{\psi} = q \sin{\phi} + r \frac{\cos{\phi}}{\cos{\theta}} \\
+    \dot{V_g} = \frac{V_a}{V_g} \dot{\psi} (w_e \cos{\psi} - w_n \sin{\psi})
+\end{equation}
+
+This propagated estimate is then used in the calculation of the Jacobian $A$.
+
+\begin{equation}
+    A =
+    \begin{bmatrix}
+        0 & 0 & \cos{\chi} & -V_g \sin{\chi} & 0 & 0 & 0 \\
+        0 & 0 & \sin{\chi} & V_g \cos{\chi} & 0 & 0 & 0 \\
+        0 & 0 & -\frac{\dot{V_g}}{V_g} & 0 & -\dot{\psi} V_a \frac{\sin{\psi}}{V_g} & \dot{\psi} V_a \frac{\cos{\psi}}{V_g} & -\dot{\psi} V_a (w_n \cos{\psi} + w_e \sin{\psi}) \\
+        0 & 0 & -\frac{g}{V_g^2} \tan{\phi} & 0 & 0 & 0 & 0 \\
+        0 & 0 & 0 & 0 & 0 & 0 & 0 \\
+        0 & 0 & 0 & 0 & 0 & 0 & 0 \\
+        0 & 0 & 0 & 0 & 0 & 0 & 0 \\
+    \end{bmatrix}
+\end{equation}
+
+This Jacobian is then used to find a second-order approximation of the matrix exponential, $A_d$.
+
+\begin{equation}
+    A_d = I + \frac{T_s}{N} A + \frac{1}{2} \frac{T_s^2}{N^2} A^2
+\end{equation}
+
+Where $T_s$ is the length of a time step.
+$A_d$ is then used to propagate the actual covariance of the estimate.
+
+The process noise due to model uncertainty is defined in the matrix $Q$ and is fused into the covariance.
+This is done by the following equation:
+
+$$P_a = A_d P A_d^\top + Q \frac{T_s^2}{N^2}$$
+
+With this propagated estimate and covariance we are now ready for a measurement update.
+
+### Measurement Update
+
+<!-- TODO: update the documentation when the position step is refactored to happen all at once. -->
+
+Because the GPS measures come in slower than the model propagates, the measurement step is only run when there is new GPS information.
+This process is identical to the measurement update in the attitude step.
+This will likely change before release, but the only difference is that it is done one measurement at a time.
+This has advantages for querying the values while debugging, but is on the whole less clear and is more error prone.
+The math is the same but it is carried through, and expressed as the final sum and each row calculated separately.
+
+The measurement model, $h$ has only 6 entries instead of 7 since heading, $\psi$ is not measured.
+A digital compass could be added and $h$ and $C$ expanded if desired, but control is operated on course, $\chi$, so this proves largely unnecessary.
+
+\begin{equation}
+    h = 
+    \begin{bmatrix}
+        p_n \\
+        p_e \\
+        V_g \\
+        \chi \\
+        V_a \cos{\psi} + w_n - V_g \cos{\chi} \\
+        V_a \sin{\psi} + w_e - V_g \sin{\chi} \\
+    \end{bmatrix}
+\end{equation}
+
+This yields a Jacobian $C$:
+
+\begin{equation}
+    C = 
+    \begin{bmatrix}
+        1 & 0 & 0 & 0 & 0 & 0 & 0 \\
+        0 & 1 & 0 & 0 & 0 & 0 & 0 \\
+        0 & 0 & 1 & 0 & 0 & 0 & 0 \\
+        0 & 0 & 0 & 1 & 0 & 0 & 0 \\
+        0 & 0 & -\cos{\chi} & V_g\sin{\chi} & 1 & 0 & -V_a\sin{\psi} \\
+        0 & 0 & -\sin{\chi} & -V_g\cos{\chi} & 0 & 1 & V_a\cos{\psi} \\
+    \end{bmatrix}
+\end{equation}
+
+Which is used in finding the Kalman gain $L$.
+
+The measurement noise matrix, $R_{position}$, is defined as:
+
+\begin{equation}
+    R_{position} = 
+    \begin{bmatrix}
+        \sigma_{gps, n}^2 & 0 & 0 & 0 & 0 & 0 & 0 \\
+        0 & \sigma_{gps, e}^2 & 0 & 0 & 0 & 0 & 0 \\
+        0 & 0 & \sigma_{gps, V_g}^2 & 0 & 0 & 0 & 0 \\
+        0 & 0 & 0 & \sigma_{gps, \chi}^2 & 0 & 0 & 0 \\
+        0 & 0 & 0 & 0 & \sigma_{pseudo, w_n}^2 & 0 & 0 \\
+        0 & 0 & 0 & 0 & 0 & \sigma_{pseudo, w_n}^2 & 0 \\
+        0 & 0 & 0 & 0 & 0 & 0 & \sigma_{pseudo, \psi}^2 \\
+    \end{bmatrix}
+\end{equation}
+
+An intermediate value is calculated called $S^{-1}$.
+This value is:
+
+\begin{equation}
+    S^{-1} = (R_{position} + CPC^\top)^{-1}
+\end{equation}
+
+This intermediate value is then used to find $L$:
+
+\begin{equation}
+    L = PC^\top S^{-1}
+\end{equation}
+
+The optimal estimate is then found:
+
+\begin{equation}
+    \hat{x}_a^+ =  \hat{x}_a^- + L (y - h)
+\end{equation}
+
+Finally, we update the covariance from our new estimate:
+
+\begin{equation}
+    P^+ = (I - LC) P^- (I - LC)^\top + LR_{accel}L^\top
+\end{equation}
+
+We repeat this cycle until termination of the program.
+
+## Software Architecture
+
+### Estimator implementation Specifics
+
+The estimator calls the update of the estimate on a timer.
+Between estimation updates, the measurements come in as they are published and are saved in the estimator.
+This allows asynchronous measurements and for only the most recent measurements to be used.
+It does mean however, that the propagation is reliant on the measurements being faster or as fast as the estimation updates.
+It also means that turning up the estimation frequency will not always have the anticipated effect.
+To increase estimator performance a combination of process noise tuning, sensor uncertainty characterization, sensor update speed increase estimate update speed increase will be needed.
+
+### Replacing the Estimator
+
+To replace the estimator all that needs to be done is inherit from the `estimator_base` class and override the `estimate` method, as described in the .hpp file.
+
+| ![Estimator Override Function](../../../assets/estimator_assets/estimator_override_function.png "Estimator Override Function") |
+|:--:|
+|*Figure 1: The member method that must be overriden to use a new estimator.*|
+
+Next, replace `estimator_example.cpp` in the `CMakeLists.txt` with the newly created estimator file.
+
+| ![Estimator CMake Change Location](../../../assets/estimator_assets/estimator_cmake.png "Estimator CMake Change Location") |
+|:--:|
+|*Figure 2: The location to replace the file for the new estimator file.*|
+
+## Parameters
+
+| **Parameter** | **Explanation** | **Type** | **Range** |
+| :---: | :---: | :---: | :---: |
+| `sigma_n_gps` | The standard deviation of gps measurements in the north axis. | double | ~0.01 meters |
+| `sigma_e_gps` | The standard deviation of gps measurements in the east axis. | double | ~0.01 meters |
+| `sigma_Vg_gps` | The standard deviation of gps ground speed measurements. | double | ~0.005 $\frac{m}{s}$ |
+| `sigma_course_gps` | The standard deviation of gps ground course measurements. | double | ~0.00025 radians |
+| `sigma_accel` | The standard deviation of accelerometer measurements. | double | ~0.025 $\frac{m}{s^2}$ |
+| `sigma_pseudo_wind_n` | The standard deviation of pseudo-measurement along the north axis. | double | ~0.01 |
+| `sigma_pseudo_wind_e` | The standard deviation of pseudo-measurement along the east axis. | double | ~0.01 |
+| `sigma_heading` | The standard deviation of heading measurement. | double | ~0.01 radians |
+| `lpf_a` | The lowpass filter cutoff frequency for the gyro and accelerometers. | double | ~50.0 Hz |
+| `lpf_a1` | The lowpass filter cutoff frequency for the pressure sensors. | double | ~8.0 Hz |
+| `gps_n_lim` | The limit of GPS measurements in the north direction. | double | $\geq 10000$ meters |
+| `gps_e_lim` | The limit of GPS measurements in the east direction. | double | $\geq 10000$ meters |
+| `roll_process_noise` | The process noise on the roll propagation. | double | ~0.0001 |
+| `pitch_process_noise` | The process noise on the pitch propagation. | double | ~0.0000001 |
+| `gyro_process_noise` | The process noise on the gyro inversion. | double |  ~0.13 |
+| `pos_process_noise` | The process noise on the position propagation. | double | 0.1 |
+| `attitude_initial_cov` | Initial covariance entry for attitude. | double | 5.0 |
+| `pos_n_initial_cov` | Initial covariance entry for position north. | double | 0.03 |
+| `pos_e_initial_cov` | Initial covariance entry for position east. | double | 0.03 |
+| `vg_initial_cov` | Initial covariance entry for groundspeed. | double | 0.01 |
+| `chi_initial_cov` | Initial covariance entry for course. | double | 5.0 Degrees |
+| `wind_n_initial_cov` | Initial covariance entry for wind north. | double | 0.04 |
+| `wind_e_initial_cov` | Initial covariance entry for wind east. | double | 0.04 |
+| `psi_initial_cov` | Initial covariance entry for heading. | double | 5.0 |
+| `num_propagation_steps` | Number of propagation steps to run between measurements. | int | 10 |
+| `max_estimated_phi` | The maximum roll estimate before estimator saturation. | double | ~90.0 degrees |
+| `max_estimated_theta` | The maximum pitch estimate before estimator saturation. | double | ~80.0 degrees |
+| `estimator_max_buffer` | The buffer that the esimator returns the estimate to after saturation. | double | ~3.0 degrees |
diff --git a/docs/developer-guide/rosplane/estimator/estimator-overview.md b/docs/developer-guide/rosplane/estimator/estimator-overview.md
new file mode 100644
index 0000000..ce25e43
--- /dev/null
+++ b/docs/developer-guide/rosplane/estimator/estimator-overview.md
@@ -0,0 +1,95 @@
+# Estimator Overview
+
+## Overview
+
+The estimator is a continuous-discrete Kalman Filter, a full treatment of the filter is found in section 8.5 of the [UAV book](https://github.com/randybeard/mavsim_public).
+This filter essentially works by inverting a few sensor models and then using a two stage estimation of first the attitude and then the position state values.
+This page will outline the states, their meaning and any general notes on the states.
+For a more in depth look at which states are estimated in which way visit the [Estimator Base](./estimator-base.md) and [Estimator Example](./estimator-example.md) pages.
+
+## ROS Interactions
+
+The estimator takes in sensor information from `rosflight_io` computes an estimate and publishes it to the rest of ROSplane.
+
+
+| ![Diagram of Estimator ROS Interactions](../../../assets/estimator_assets/estimator_full_ros_interactions.png "Diagram of Estimator ROS Interactions") |
+|:--:|
+|*Figure 1: ROS network interactions for the estimator.*|
+
+### Input
+
+The inputs to the estimator are, accelerometer, rate gyro, barometer, differential pressure, GPS position, and GPS velocity estimates.
+The table with the topic for each of the measures is below.
+
+| Measure | Explanation | Topic |
+|:------:|:-------:| :---: |
+| Accelerometer | This measures the specfic force applied to the aircraft in the body frame axes (see section 7.1 in the UAV book for more details). | `/imu/data/linear_acceleration` |
+| Rate Gyro | This measures the angular velocity of the aircraft around the body frame axes. | `/imu/data/angular_velocity` |
+| Barometer | The barometer measures the ambient air pressure. It is calibrated on arm to establish a "zero" altitude measurement. | `/baro/pressure` |
+| Differential Pressure | The differential pressure sensor, measures the difference in pressure using a pitot tube due to forward velocity. | `/airspeed/differential_pressure` |
+| GNSS Position | GNSS postion gives the position of the aircraft in latitude, longitude and altitude. | `/navsat_compat/fix` |
+| GNSS Velocity | GNSS velocity gives the velocity of the aircraft in meters per second in the global NED frame. | `/navsat_compat/vel` |
+| Status | Indicates whether the aircraft is armed (indicating a need to initialize position and altitude estimates). | `/status` |
+
+These topics provide the measures that are fused to create a state estimate.
+
+| ![Diagram of Estimator ROS IO](../../../assets/estimator_assets/estimator_ros_input_output.png "Diagram of Estimator ROS IO") |
+|:--:|
+|*Figure 1: ROS topic subscriptions and publications for the estimator.*|
+
+## Output
+
+There are 20 states estimated by the estimator that are published to the rest of ROSplane.
+These states cover the position, orientation and aerodynamic information for the aircraft.
+Below is a table of the `/estimated_state` message and what each of the fields represents.
+Note that there are more than 20 states listed in the following table, but this is because either Euler angles or quaternions can be used to express orientation.
+See the Frames and Derivation page for more information, or chapter 2 of the [UAV book](https://github.com/randybeard/mavsim_public).
+
+| State | Explanation | Range/Type/Units |
+|:--:|:--:|:--:|
+| postion[3] | A 3 vector of the NED position of the aircraft. | $(-\infty , \infty)$ (float)(meters) |
+| va | The airspeed of the aircraft. Always positive because it is the magnitude. | $(0 , \infty)$ (float)(meters/second) |
+| alpha | The angle of attack of the aircraft wing. | $(-\pi, \pi)$ (float)(radians) |
+| beta | The side-slip angle of the aircraft. | $(-\pi, \pi)$ (float)(radians) |
+| phi | The roll angle of the aircraft. | $(-\pi, \pi)$ (float)(radians) |
+| theta | The pitch angle of the aircraft. | $(-\pi, \pi)$ (float)(radians) |
+| psi | The yaw angle of the aircraft. | $(-\pi, \pi)$ (float)(radians) |
+| chi | The course angle of the aircraft. | $(-\pi, \pi)$ (float)(radians) |
+| u | The velocity in the body x axis. | $(-\infty, \infty)$ (float)(meters/second) |
+| v | The velocity in the body y axis. | $(-\infty, \infty)$ (float)(meters/second) |
+| w | The velocity in the body z axis. | $(-\infty, \infty)$ (float)(meters/second) |
+| p | The angular velocity about the body x axis (rollrate). | $(-\infty, \infty)$ (float)(radians/second) |
+| q | The angular velocity about the body y axis (pitchrate). | $(-\infty, \infty)$ (float)(radians/second) |
+| r | The angular velocity about the body z axis (yawrate). | $(-\infty, \infty)$ (float)(radians/second) |
+| vg | The groundspeed of the aircraft. Always positive because it is the magnitude. | $(0 , \infty)$ (float)(meters/second) |
+| wn | The global north velocity of the wind. | $(-\infty , \infty)$ (float)(meters/second) |
+| we | The global east velocity of the wind. | $(-\infty , \infty)$ (float)(meters/second) |
+| _ | **The following entries are optional.** | _ |
+| quat[4] | A 4 vector of the quaternion describing the orientation. | $(-1 , 1)$ (float) |
+| quat_valid | A flag indicating whether the data in the quat entry is valid.| True/False (bool) |
+| psi_deg | The yaw angle of the aircraft in degrees. | $(-180, 180)$ (float)(degrees) |
+| chi_deg | The course angle of the aircraft in degrees. | $(-180, 180)$ (float)(degrees) |
+| init_lat | The latitude of the aircraft when first armed. | $(-90, 90)$ (float)(DDS) |
+| init_long | The longitude of the aircraft when first armed. | $(-180, 180)$ (float)(DDS) |
+| init_alt | The altitude of the aircraft when first armed. | $(0, \infty)$ (float)(meters) |
+
+!!! note 
+    More states may be estimated in the estimator than listed, these states are just those useful to other parts of ROSplane.
+
+## Running the Estimator
+
+The estimator is in the main `rosplane` ROS package.
+The ROS executable is `rosplane_estimator_node`, yielding the run command:
+
+`ros2 run rosplane rosplane_estimator_node`
+
+To pass a set of parameters for the controller from a yaml file using the `--ros-args` option.
+
+`ros2 run rosplane rosplane_estimator_node --ros-args --params-file path/to/params.yaml`
+
+
+!!! note 
+    Filepaths will need to be altered to work.
+!!! note
+    The most common way of running the estimator is through a launch file with the rest of the ROSplane pipeline running as well.
+    See the ROSplane Overview in the Developer and User Guides for more details.
diff --git a/mkdocs.yml b/mkdocs.yml
index 798ae44..484053b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -77,6 +77,10 @@ nav:
         - Controller State Machine: developer-guide/rosplane/controller/controller-state-machine.md
         - Successive Loop Closure Controller Outline: developer-guide/rosplane/controller/controller-outline.md
         - Total Energy Controller: developer-guide/rosplane/controller/controller-total-energy.md
+      - Estimator:
+        - Estimator Overview: developer-guide/rosplane/estimator/estimator-overview.md
+        - Estimator Base: developer-guide/rosplane/estimator/estimator-base.md
+        - Estimator Example: developer-guide/rosplane/estimator/estimator-example.md
       - Parameter Management: developer-guide/rosplane/parameter-management.md
       - Path Planning: 
         - Path Planning Overview: developer-guide/rosplane/path-planning/path-planning-overview.md