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An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰
| Date | Stars |
|---|---|
| 2026-07-24 | 717 |
| 2026-07-25 | 717 |
| 2026-07-28 | 717 |
| 2026-07-30 | 717 |
| 2026-08-06 | 717 |
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# Sensor Fusion in ROS [](https://youtu.be/5vZOvISwT94) 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! ### Why fuse sensor data 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](http://wiki.ros.org/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! ### How to use this tutorial 1. Make sure you're caught up on [ROS](https://github.com/methylDragon/coding-notes/tree/master/Robot%20Operating%20System%20(ROS)/ROS) 2. It'll be good to read the [Marvelmind Indoor 'GPS' beacon tutorial](https://github.com/methylDragon/marvelmind-indoor-gps-tutorial) alongside this if you want to understand the example implementation 3. Likewise for the [Linorobot stack](https://linorobot.org) 4. And [AMCL](http://wiki.ros.org/amcl) 5. Then go ahead and follow the tutorial in order! ------ [.png)](https://www.buymeacoffee.com/methylDragon)
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methylDragon · Intrinsic @intrinsic-ai
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matched fp:edab5a2486045c73, topic:robotics, topic:ros
matched fp:edab5a2486045c73, topic:tutorial, name:tutorial, desc:tutorial