| Abstrakt: |
In this demo paper, we introduce RASSAR, a mobile AR application for semi-automatically identifying, localizing, and visualizing indoor accessibility and safety issues using LiDAR and real-time computer vision. Our prototype supports four classes of detection problems: inaccessible object dimensions (e.g., table height), inaccessible object positions (e.g., a light switch out of reach), the presence of unsafe items (e.g., scissors), and the lack of proper assistive devices (e.g., grab bars). RASSAR's design was informed by a formative interview study with 18 participants from five key stakeholder groups, including wheelchair users, blind and low vision participants, families with young children, and caregivers. Our envisioned use cases include vacation rental hosts, new caregivers, or people with disabilities themselves documenting issues in their homes or rental spaces and planning renovations. We present key findings from our formative interviews, the design of RASSAR, and results from an initial performance evaluation. [ABSTRACT FROM AUTHOR] |
