Skip to content

We will study how to fuse different sensor modalities to get better robot localization. We will go over Multi-Variable Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter Approaches for Global Localization.

Notifications You must be signed in to change notification settings

Muhammad540/Guide-to-Sensor-Fusion

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

🎉 Welcome to the Sensor Fusion & Filtering Guide!

This repo is your go-to resource for understanding and practically implementing different filtering techniques in ROS2! 🚀 Whether you're a beginner or looking to refine your skills, you’ll find everything you need to confidently tune and test sensor fusion algorithms. 💡

📝 How to Get Started

  1. Read Before You Run
    Head over to ws/docs 📚 and explore the filter you're interested in. For example, if you're excited about AMCL/ParticleFiltering 🧠, read the Jupyter notebook provided. This will help you understand the theory before jumping into ROS2.

  2. Test in ROS2
    Once you've got the theory down, close the notebook and dive into the ROS2 workspace. By testing the algorithm yourself, you’ll feel more confident when it's time to tweak and tune! 🛠️

🐳 Everything is Dockerized!

No need to worry about installing ROS2 or any other packages. We've made it super easy for you! Follow these steps:

  1. Clone the Repo
    Create a folder on your PC called ros2_ws, then clone this repository into it. 📂

  2. Use VS Code
    Open the Sensor-Fusion folder using Visual Studio Code. Make sure you have Docker installed on your PC and the Docker extension for VS Code installed.

  3. Let the Magic Happen
    VS Code will automatically build and run the Docker container for you. 🎉 You'll find yourself inside a ready-to-go workspace with everything set up!

🐢 TurtleBot3 Simulation Included

We’ve integrated the TurtleBot3 simulation for quick testing of the algorithms! 🐢 Head to the README inside the ws folder to see how to run each algorithm. Feel free to play around, tweak settings, and explore. Have fun experimenting! 🧪

🤝 Contributions & Feedback

Got a cool idea or found a bug? Feel free to open an issue or submit a PR. This is an open community, and your contributions are always welcome!

Happy Learning! 🎓

About

We will study how to fuse different sensor modalities to get better robot localization. We will go over Multi-Variable Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter Approaches for Global Localization.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published