Abstract. Indoor wayfinding is a major challenge for people with visual impairments, who are often unable to see visual cues such as informational signs, landmarks and structural features that people with normal vision rely on for wayfinding. We describe a novel indoor localization approach to facilitate wayfinding that uses a smartphone to combine computer vision and a dead reckoning technique known as visual-inertial odometry (VIO). The approach uses sign recognition to estimate the user's location on the map whenever a known sign is recognized, and VIO to track the user's movements when no sign is visible. The advantages of our approach are (a) that it runs on a standard smartphone and requires no new physical infrastructure, just a digital 2D map of the indoor environment that includes the locations of signs in it; and (b) it allows the user to walk freely without having to actively search for signs with the smartphone (which is challenging for people with severe visual impairments). We report a formative study with four blind users demonstrating the feasibility of the approach and suggesting areas for future improvement.
State of the Art and Related TechnologyThe key to any wayfinding aid is localization -a means of estimating and tracking a person's location as they travel in an environment. The most widespread localization approach is GPS, which enables a variety of wayfinding tools such as Google Maps, Seeing Eye GPS 1 and BlindSquare, but it is only accurate outdoors. There are a range of indoor localization approaches, including Bluetooth beacons [1], Wi-Fi triangulation 2 , infrared light beacons [2] and RFIDs [3]. However, all of these approaches incur the cost of installing and maintaining physical infrastructure, or of updating the system as the existing infrastructure changes (e.g., whenever Wi-Fi access points change). Dead reckoning approaches such as step counting using inertial navigation [4] can estimate relative movements without any physical infrastructure, but this tracking estimate drifts over time unless it is augmented by absolute location estimates.