AI.SLAM - SLAM for Outdoor Arable Fields

This repository contains a SLAM (Simultaneous Localization and Mapping) implementation designed for outdoor arable fields. It features a stationary robot with a fixed-axis turning motion, creating circular paths while mapping the environment. The primary focus is on harvesting vegetables in a dynamic, ever-changing environment to ensure variety and freshness.

Table of Contents

• AI.SLAM - SLAM for Outdoor Arable Fields
  • Table of Contents
  • Introduction
  • Setup and Installation
    • Prerequisites
  • Usage
  • Testing
  • Contributing
  • Data

Introduction

AI.SLAM is designed to enable autonomous navigation and mapping of agricultural fields using SLAM techniques. It integrates well with various sensors and tools necessary for outdoor environments, ensuring high-quality and real-time mapping of arable fields.

Setup and Installation

Prerequisites

Before setting up, ensure you have the following tools and dependencies:

• ROS 2 (Recommended version: Foxy or later)
• C++ compiler (GCC 8 or later)
• Python 3.8 or higher
• Additional libraries like OpenCV, PCL, etc. (Check requirements.txt for specifics)

Usage

To launch the SLAM system, use the following command:

ros2 launch ai_slam <launch_file>.launch.py

Replace <launch_file> with the appropriate launch file for your configuration. Structure

The repository is organized into the following directories:

src/: Contains the core SLAM implementation and sensor integration.
data/: Stores sample datasets and configurations.
scripts/: Includes utility scripts for preprocessing data and launching tests.
tests/: Unit and integration tests for the system's components.
third_party/: Third-party libraries and dependencies used in the project.

Testing

Make sure the system is properly set up and dependencies are installed before running tests.

Contributing

We welcome contributions! To contribute:

Fork the repository.
Create a feature branch (git checkout -b feature-name).
Commit your changes (git commit -am 'Add new feature').
Push to the branch (git push origin feature-name).
Submit a pull request.

Please ensure all contributions adhere to the coding standards and include necessary documentation. License

This project is licensed under the MIT License - see the LICENSE file for details.

Data

Datasets used for testing and validation. Datasets for training. Datasets for ground truth evaluation.