An in-depth step-by-step tutorial for implementing sensor fusion with extended Kalman filter nodes from robot_localization! Basic concepts like covariance and Kalman filters are explained here!

This tutorial is especially useful because there hasn't been a full end-to-end implementation tutorial for sensor fusion with the robot_localization package yet.

You can find the implementation in the Example Implementation folder!

A lot of times, the individual navigation stack components in a robot application can fail more often than not, but together, they form a more robust whole than not.

One way to do this is with the extended Kalman filter from the robot_localization package. The package features a relatively simple ROS interface to help you fuse and configure your sensors, so that's what we'll be using!

1. Make sure you're caught up on ROS
2. It'll be good to read the Marvelmind Indoor 'GPS' beacon tutorial alongside this if you want to understand the example implementation
3. Likewise for the Linorobot stack
4. And AMCL
5. Then go ahead and follow the tutorial in order!