Add assets and update README.md

README.md

@@ -11,9 +11,30 @@ Harness the power of GPU acceleration for fusing visual odometry and IMU data wi
 2) Move `cuUKF` into `ROS2_WORKSPACE`
 3) `cd ROS2_WORKSPACE` build workspace using `colcon build --symlink-install --cmake-args=-DCMAKE_BUILD_TYPE=Release --parallel-workers $(nproc)`
 
-## 🖼️ Running cuUKF
+## 📈 Running cuUKF
 Launch the node using `ros2 launch cuUKF gpu_filter.launch.py odom_topic:=/odom imu_topic:=/imu`.
 The `/odom` should be replaced with your `nav_msgs/Odometry` and the `/imu` should be replaced with your `sensor_msgs/Imu`.
 
+## 💬 ROS2 Message
+The filter odometry gets published as `nav_msgs/Odometry` in the `/cuUKF/filtered_odom` topic.
+
 ## 🖼️ RVIZ2 GUI
 Launch RVIZ2 using `ros2 run rviz2 rviz2` and subcribe to the `/cuUKF/filered_odom` topic.
+
+<div align="center">
+    <img src="assets/Odometry.png" alt="Odometry" width="800"/>
+    <p>Odometry</p>
+</div>
+
+### Visualize the covaraince of the state
+
+<div align="center">
+    <img src="assets/Covarince.png" alt="Covarince" width="800"/>
+    <p>Odometry</p>
+</div>
+
+## ⚠️ Note
+1) The fusion does not consider the IMU's orientation only the visual odometry, as raw IMU dont produce orientation without addiontal filters such as complementary filter and the Madgwick filter.
+2) Feel free to change the alpha, beta and kappa values along with the convariace to improve state estimates.
+3) The dynamics of the system use simple equations, for the best fusion you may need to change the dynamics.
+4) Consider adding augmented simga points to further increase the robustness.