https://github.com/methylDragon/ros-sensor-fusion-tutorial

An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰
https://github.com/methylDragon/ros-sensor-fusion-tutorial

ekf-localization kalman-filter robot-localization robotics ros sensor-fusion tutorial

Last synced: 27 days ago
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An in-depth step-by-step tutorial for implementing sensor fusion with robot_localization! 🛰

• Host: GitHub
• URL: https://github.com/methylDragon/ros-sensor-fusion-tutorial
• Owner: methylDragon
• License: mit
• Created: 2018-08-03T03:58:04.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2019-03-15T19:08:11.000Z (over 5 years ago)
• Last Synced: 2024-02-10T05:42:23.061Z (4 months ago)
• Topics: ekf-localization, kalman-filter, robot-localization, robotics, ros, sensor-fusion, tutorial
• Homepage:
• Size: 7.27 MB
• Stars: 622
• Watchers: 24
• Forks: 167
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-SLAM - Sensor Fusion in ROS - depth step-by-step tutorial for implementing sensor fusion with robot_localization (8. Tutorials / 8.2 Robotics)

README

        
# Sensor Fusion in ROS

[![Click to watch video!](assets/youtube_thumbnail.png)](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!

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