Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision
Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision
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DOI：
10.48550/arxiv.2210.02533
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Xia Su;Kaiming Cheng;Han Zhang;Jaewook Lee;Jon Froehlich
中科院分区：
文献类型：
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作者：
Xia Su;Kaiming Cheng;Han Zhang;Jaewook Lee;Jon Froehlich
为了帮助改善室内空间的安全性和可达性，研究人员和卫生专业人员创建了评估工具，使房主和受过培训的专家能够审计和改善住房。随着计算机视觉、增强现实（AR）和移动传感器的进步，新的方法现在成为可能。我们介绍了RASSAR（增强现实中的房间可达性和安全扫描），这是一种新的概念验证原型，用于使用LiDAR +相机数据、机器学习和AR半自动识别、分类和定位室内可达性和安全问题。我们介绍了当前RASSAR原型的概述和在单个家庭中的初步评估。
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.