VisionNav

VisionNav is a cutting-edge navigation system designed for visually impaired individuals, providing real-time guidance through voice commands. The system leverages advanced technologies such as OpenCV, SLAM, YOLO for object detection, and Text-to-Speech (TTS) to recognize indoor landmarks and obstacles, ensuring precise and safe navigation.

Features

• Real-Time Object Detection and Recognition: Utilizes YOLO to detect and recognize objects in the environment.
• Indoor Localization and Mapping: Employs SLAM (Simultaneous Localization and Mapping) to create and update maps in real-time.
• Voice Guidance: Provides audio feedback and navigation commands using Text-to-Speech.
• Multi-Floor Navigation: Supports navigation across multiple floors with elevator recognition.
• Bluetooth Beacon Integration: Enhances indoor positioning accuracy using Bluetooth beacons.

Technologies Used

• OpenCV: For capturing and processing video feed.
• SLAM: For real-time localization and mapping.
• YOLO: For object detection.
• Text-to-Speech (pyttsx3): For voice guidance.
• Bleak: For Bluetooth beacon integration.
• Python: The primary programming language used.

Setup

Prerequisites

• Python 3.6 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone https://github.com/debjit-mandal/VisionNav.git
   cd VisionNav

2. Install the required dependencies:

   ./setup.sh

3. Download YOLO configuration and weights:

   mkdir -p data/yolo
   cd data/yolo
   curl -O https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg
   curl -O https://pjreddie.com/media/files/yolov3.weights
   curl -O https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names
   cd ../../

4. Collect Bluetooth beacon data (if applicable):

   python collect_beacon_data.py

Running the Application

1. Run the main script:

   python -m src.main

2. Usage:

   • The system will start capturing video from the webcam.
   • Detected objects will be displayed with bounding boxes and labels.
   • Voice commands will guide the user based on detected objects and navigation logic.
   • Bluetooth beacon data will be printed after the video feed is stopped.

Directory Structure

• data/: Contains configuration files and data.
  • beacons/: Stores Bluetooth beacon data.
  • yolo/: Contains YOLO configuration, weights, and class names.
• src/: Contains source code for object detection, SLAM, navigation, TTS, and Bluetooth integration.
  • main.py: Main script to run the application.
  • object_detection.py: Module for object detection using YOLO.
  • tts.py: Module for Text-to-Speech functionality.
  • slam.py: Module placeholder for SLAM functionality.
  • navigation.py: Module for navigation logic.
  • bluetooth.py: Module for Bluetooth beacon integration.
• requirements.txt: List of dependencies.
• setup.sh: Setup script to install dependencies.
• README.md: Project documentation.
• LICENSE: License for the project.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements, bug fixes, or new features.

License

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

Acknowledgments

• Thanks to the open-source community for providing the tools and libraries used in this project.
• Special thanks to the creators of OpenCV, YOLO, pyttsx3, and Bleak.

---

Happy navigating!