NaVIP: A Computer Vision-Based Indoor Navigation Solution for Visually Impaired Persons

Indoor navigation poses significant challenges due to the ineffectiveness of satellite signals, which fail to penetrate building structures. This challenge is manifold greater for visually impaired persons (VIPs) who cannot obtain information from wayfinding signage. Other sensor signals (e.g., Bluetooth and WiFi) come into play and can be used to estimate users’ positions. However, these solutions typically require the addition of tags or hardware throughout the environment to calibrate Points of Interest (PoIs), which limits scalability in providing precise navigational guidance to VIPs. Thus, we propose a computer vision (CV)-based approach called NaVIP (Navigation for VIPs) that offers a cost-effective, infrastructure-free, and scalable solution to elaborate indoor navigation for VIPs. In addition to turn-by-turn navigation, NaVIP provides continuous and accurate distance and clockwise orientation cues, guiding users through any deviations or missteps from the starting point to their final destination. To confirm our conclusion, we extensively evaluated the localization accuracy of NaVIP within a large and complex indoor space. Furthermore, we demonstrate the potential effectiveness and usability of NaVIP by creating an iOS application and testing it with VIP users. NaVIP, as a CV-based framework, is designed to be task-scalable, enabling future enhancements that utilize the advancements in CV tasks, such as automated object detection and depth estimation, to aid VIPs further.