Hybrid Navigation Framework with Computer Vision and GPS for Visually Impaired Users

Abstract

A hybrid navigation framework is proposed to improve mobility assistance for visually impaired users by incorporating GPS-based turn-by-turn guidance with real-time vision-based obstacle detection. Current navigation systems offer directional guidance but are not aware of dynamic environmental obstacles, making them less suitable for real-world pedestrian navigation. The limitation is addressed by a multimodal solution, which uses voice-assisted GPS navigation and computer vision. The navigation component allows users to input locations via voice inputs and receive turn-by-turn instructions in real-time using GPS. The vision component uses real-time object detection with YOLOv8, while depth estimation is achieved with Depth Pro. A semantic risk model is also introduced to evaluate obstacles based on the type of objects, distance, and location in the path. The environment is segmented into navigational regions, and a least-risk direction is dynamically determined. The system also ensures safety by pausing navigation guidance when obstacles with high risk are detected and giving immediate audio feedback. Experimental results demonstrate effective obstacle detection with over 90% accuracy and real-time system response below 1.5 seconds, validating the system’s suitability for practical deployment. The framework can help improve safe navigation and situational awareness for the visually impaired.