Indoor Localization using Computer Vision and Visual-Inertial Odometry

Abstract:

Indoor wayfinding is a major challenge for people with visual impairments. They 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) 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 (this is challenging for people with severe visual impairments). In this talk, we'll report a formative study with four blind users demonstrating the feasibility of the approach and suggesting areas for future improvement.