BUILD STATUS

Contact Information

If you have any questions or need support, feel free to reach out to our team members:

• Şevval Belkıs Dikkaya: Head of the team, working on software, electronics, and mechanics. LinkedIn
• Muhammed Sezer: Responsible for electronics and software, and the maintainer of this package. LinkedIn
• Metehan İçöz: Responsible for mechanics. LinkedIn
• Zeynep Keleş: Working on software, electronics, and mechanics. LinkedIn

Feel free to connect with us on LinkedIn and mention any specific questions or issues related to the project. We’ll be happy to help!

Object Detection and Tracking ROS2 Package

Table of Contents

1. Introduction
2. Dependencies
3. Installation
4. Usage
5. Node Descriptions
   • object_detector
     • Published Topics
     • Subscribed Topics
   • object_tracker
     • Published Topics
     • Subscribed Topics
   • best_object_selector
     • Published Topics
     • Subscribed Topics
   • vehicle_status
     • Published Topics
     • Subscribed Topics
6. Parameters
7. Messages
8. Code Examples
9. Troubleshooting
10. Contributing
11. License
12. Hardware Used
13. Resources

Introduction

The introduction section should provide a brief overview of the main features, capabilities, and use cases of the package. Explain the purpose of the package and what it aims to achieve.

Eflatun is a package that contains a comprehensive set of tools and algorithms for implementing and testing machine learning models. This project is aimed at providing developers with a modular and extensible software framework for developing autonomous systems, such as robots, drones, and other intelligent machines. The package includes nodes that track and detect an object and select the best out of them. It also includes instructions for how to install and use the framework, as well as examples of how to develop various autonomous systems using the eflatun_src framework..

The package is capable of data preprocessing, feature engineering, and model evaluation. It also supports parallel computing, which enables users to take advantage of multi-core CPUs for faster processing of large datasets.

The package can be used for a variety of domains such as finance, healthcare, marketing, and research purposes. Its features and capabilities make it a valuable tool for anyone interested in machine learning and data science. The use cases of Eflatun include predictive modeling, anomaly detection, image and speech recognition, natural language processing (NLP), and recommender systems.

Dependencies

In the Dependencies section, list and explain all external dependencies required for the package to work, including specific versions if necessary. Add instructions for installing these dependencies.

Installation

The Installation section should provide step-by-step installation instructions for the package. Include information on installing the package from source or using a package manager, if applicable.

Usage

The Usage section should include detailed examples demonstrating how to use the package. Provide code snippets, terminal commands, or configuration files to help users understand the package’s functionality better.

Node Descriptions

🔎 Object Detector Node

The object_detector node is responsible for detecting objects in a video stream. It processes the video frames and identifies objects based on the provided model. The node does not subscribe to any topics.

Published Topics

Subscribed Topics

The object_detector node does not subscribe to any topics.

📌 Object Tracker Node

The object_tracker node is responsible for tracking detected objects in a video stream. It subscribes to the /webcam/detections topic, receives detected objects, and updates their tracking information. The tracking information is then published to the /tracker/tracked_objects topic.

Published Topics

Subscribed Topics

🎯 Best Object Selector Node

The best_object_selector is a ROS node responsible for selecting the best object from a list of tracked objects. It subscribes to the /tracker/tracked_objects topic, calculates a score for each object based on its properties (width, height, age, and distance to center), and publishes the best object to the /tracker/best_object topic. It also takes into account the minimum size of an object and if it lies within the specified range.

Published Topics

Subscribed Topics

🚗 Vehicle Status Node

This is a Python script for a simple GUI that subscribes to multiple MAVROS topics using ROS2 and displays the received data in a table.

The MavrosSubscriber class is a ROS2 node that subscribes to the provided topics using the create_subscription method from the Node class. When a message is received on a subscribed topic, the corresponding callback function is called to update the GUI.

The MavrosGUI class is a PyQt5 widget that displays a table with the subscribed topics and their values. The update_table method updates the table with the received message.

Published Topics

The vehicle_status node doesn’t publish to any topics.

Subscribed Topics

Parameters

object_detector

object_tracker

vehicle_status_gui

best_object_selector

Custom Messages

TrackedObject.msg

TrackedObjectArray.msg

Code Examples

The Code Examples section should provide examples demonstrating how to use the package in various scenarios. Include code snippets or configuration files to give users a clear understanding of how to implement the package in their projects.

Contributing

We welcome and appreciate contributions from the community! If you’re interested in contributing to this project, please follow these guidelines to ensure a smooth collaboration process:

1. Reporting Issues: If you encounter any issues or have suggestions for improvements, please create an issue on the project’s GitHub repository. When reporting an issue, provide a clear and concise description of the problem, steps to reproduce it, and any additional information (e.g., screenshots, logs) that might be helpful in diagnosing and resolving the issue.

2. Submitting Pull Requests: If you want to contribute code, start by forking the repository and creating a new branch for your changes. Make sure your code follows the project’s coding style and conventions. Before submitting a pull request, ensure that your changes do not introduce any new errors or warnings, and that all tests pass. In your pull request, provide a brief summary of the changes, as well as any relevant issue numbers.

3. Participating in Development Discussions: If you have ideas for new features or would like to provide feedback on the project’s direction, join the development discussions by reaching out to the team members via LinkedIn or other communication channels mentioned in the Contact Information section.

4. Documentation: If you notice any errors or omissions in the project’s documentation, please submit a pull request with the necessary changes, or create an issue to bring it to our attention.

5. Testing: Help us improve the quality and stability of the project by testing it on various platforms, configurations, and scenarios. Report any issues you encounter, along with detailed information on how to reproduce them.

By following these guidelines, you will help maintain a high level of quality and collaboration in the project, ensuring its continued success and growth.

License

This project is free to use, but you must give proper reference to the original authors when utilizing any part of it in your work. By using this project, you acknowledge and agree to this requirement. Please make sure to cite our work appropriately to give credit to our team.

Eflatun (2023). Object Detection and Tracking on Fixed Wing Airplanes. Version 0.0.1. [Source Code]. Available at: https://github.com/sezer-muhammed/eflatun_src

Hardware Used

In this project, we utilized the following hardware components to achieve object detection and tracking on fixed wing airplanes:

• Airframe: Our custom-built airframe has a wingspan of X meters and a length of Y meters. The airframe is made of lightweight, durable materials such as carbon fiber and foam, ensuring optimal flight performance and endurance. The hardware produced at NURUS Company.

• Flight Controller: TODO

• Camera: TODO

• Onboard Computer: TODO

Make sure you have compatible hardware components when using this project in your own fixed wing airplane setup. Detailed specifications and connection diagrams can be found in the documentation.