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