coming soon …
This paper describes the design and implementation of an indoor navigation system intended to support visually impaired students in university buildings. The system integrates computer vision, LiDAR-based distance measurement, QR code–based localization, route planning using the A* algorithm, and a voice interface for user interaction. The hardware platform is based on a Raspberry Pi and includes a camera, LiDAR sensor, OLED display, microphone, and audio output module. The software components implement object detection using the MobileNetV3 SSD model, QR code decoding for position initialization, and distance estimation based on image geometry. The prototype was tested in an academic building environment. The results demonstrate that the system is capable of determining the user’s position, generating indoor routes, and providing obstacle-related audio cues during movement. The proposed approach supports hands-free navigation in indoor environments where satellite positioning is unavailable and can serve as a basis for further development of assistive navigation tools for educational settings. The system is implemented as a functional prototype, and the results demonstrate its feasibility for indoor navigation in controlled environments. The presented results should be interpreted as preliminary, and further validation with end users and large-scale experiments remains part of future work.
