Project Description

2022–Present
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.

Publications

RASSAR: Room Accessibility and Safety Scanning in Augmented Reality

Xia Su, Han Zhang, Kaiming Cheng, Jaewook Lee, Qiaochu (Steve) Liu, Wyatt Olson, Jon E. Froehlich

CHI 2024 To Appear | Acceptance Rate: 26.3% (1060 / 4028)

A Demonstration of RASSAR: Room Accessibility and Safety Scanning in Augmented Reality

Xia Su, Kaiming Cheng, Han Zhang, Jaewook Lee, Wyatt Olson, Jon E. Froehlich

Extended Abstract Proceedings of ASSETS 2023 | Acceptance Rate: 51.9% (40 / 77)

Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision

Xia Su, Kaiming Cheng, Han Zhang, Jaewook Lee, Yueqian Zhang, Jon E. Froehlich

UrbanAccess 2022