  To help improve the safety and accessibility of indoor spaces, researchers
and health professionals have created assessment instruments that enable
homeowners and trained experts to audit and improve homes. With advances in
computer vision, augmented reality (AR), and mobile sensors, new approaches are
now possible. We introduce RASSAR (Room Accessibility and Safety Scanning in
Augmented Reality), a new proof-of-concept prototype for semi-automatically
identifying, categorizing, and localizing indoor accessibility and safety
issues using LiDAR + camera data, machine learning, and AR. We present an
overview of the current RASSAR prototype and a preliminary evaluation in a
single home.
