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.

中文翻译：

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RASSAR：增强现实中的房间无障碍和安全扫描

我们家的安全性和可达性对于生活质量至关重要，并且随着我们年龄的增长、生病、接待客人或经历生孩子等生活事件而不断变化。研究人员和卫生专业人员创建了检查表等评估工具，使房主和经过培训的专家能够识别和缓解安全和访问问题。随着计算机视觉、增强现实 (AR) 和移动传感器的进步，新方法现已成为可能。我们推出 RASSAR，这是一款移动 AR 应用程序，用于使用激光雷达和实时计算机视觉半自动识别、定位和可视化室内无障碍和安全问题，例如难以接近的桌子高度或不安全的松散地毯。我们展示了三项研究的结果：一项由 5 个利益相关者群体的 18 名参与者参与的形成性研究，旨在为 RASSAR 的设计提供信息；对 10 个家庭进行的技术性能评估，展示了最先进的性能；以及由 6 个利益相关者参与的用户研究。最后我们讨论了未来基于人工智能的室内可达性评估工具、RASSAR 的可扩展性和关键应用场景。