International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Indoor navigation remains a major barrier for visually impaired individuals, especially in unfamiliar environments such as public buildings, offices, and hospitals. Most existing systems depend on Bluetooth beacons or RFID tags, which either lack accuracy or require regular maintenance. This work presents a low-cost, room-accurate indoor navigation and assistance system built using Li-Fi based room identification, sensor-based directional estimation, YOLO-powered object recognition, and emergency SOS support. A 3W LED driven through MOSFET circuitry transmits room IDs using Li-Fi at 2000 baud, while a BPW34 photodiode-based receiver decodes the signal and forwards location and orientation data to a Raspberry Pi 5 over HTTP. The Pi processes navigation commands, captures user speech, and generates voice-based guidance. Additional features include real-time obstacle alerting, object identification using YOLOv8s, and a safety button that sends an emergency telegram message with an image and a 5-second audio clip. Experimental evaluation in a four-room demo environment shows reliable Li-Fi detection up to 30 cm in low-light conditions, 90% object recognition accuracy, and an average navigation response delay of 5 seconds. The system demonstrates a practical and scalable solution for autonomous indoor mobility for visually impaired users.

Keywords: Li-Fi, Indoor Navigation, Visually Impaired, Raspberry Pi, YOLOv8, Object Detection, Assistive Technology

Downloads: | DOI: 10.17148/IJARCCE.2025.141235