  The safety and accessibility of our homes is critical to quality of life and
evolves as we age, become ill, host guests, or experience life events such as
having children. Researchers and health professionals have created assessment
instruments such as checklists that enable homeowners and trained experts to
identify and mitigate safety and access issues. With advances in computer
vision, augmented reality (AR), and mobile sensors, new approaches are now
possible. We introduce RASSAR, a mobile AR application for semi-automatically
identifying, localizing, and visualizing indoor accessibility and safety issues
such as an inaccessible table height or unsafe loose rugs using LiDAR and
real-time computer vision. We present findings from three studies: a formative
study with 18 participants across five stakeholder groups to inform the design
of RASSAR, a technical performance evaluation across ten homes demonstrating
state-of-the-art performance, and a user study with six stakeholders. We close
with a discussion of future AI-based indoor accessibility assessment tools,
RASSAR's extensibility, and key application scenarios.
