Outdoor Navigation Assistive System Based on Robust and Real-Time Visual-Auditory Substitution Approach

Area of Science:

• Computer Science
• Robotics
• Human-Computer Interaction

Background:

• Millions worldwide experience blindness, significantly impacting daily travel and independence.
• Existing navigation aids often lack the real-time spatial information crucial for safe and efficient mobility.
• The need for advanced assistive technologies to empower visually impaired individuals is critical.

Purpose of the Study:

• To develop and evaluate a novel navigation aid system for individuals with severe blindness.
• To enhance the autonomy and quality of life for blind people by providing reliable environmental navigation.
• To integrate multi-sensory information for intuitive and effective spatial guidance.

Main Methods:

• A deep learning approach utilizing neural networks for trajectory definition and obstacle detection.
• Integration of inertial sensors, GPS data, and cartographic pedestrian path knowledge.
• Real-time processing using a low-power embedded GPU and multithreaded architecture, incorporating camera-based visual cues.

Main Results:

• The system achieves low latency and real-time processing capabilities for navigation assistance.
• Effective integration of auditory (3D spatialized sound) and visual cues for spatial awareness.
• Demonstrated potential for reliable and efficient navigation in complex environments.

Conclusions:

• The proposed navigation aid offers a significant advancement in assistive technology for the visually impaired.
• This system can substantially improve the independence and mobility of blind individuals.
• The technology's real-time, embedded processing ensures practical application without remote dependencies.