论文标题
使用移动深度扫描和计算机视觉来朝着半自动检测和本地化室内可及性问题
Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision
论文作者
论文摘要
为了帮助提高室内空间的安全性和可及性，研究人员和卫生专业人员创建了评估工具，使房主和训练有素的专家能够审核和改善房屋。随着计算机视觉，增强现实（AR）和移动传感器的进步，现在可以使用新的方法。我们介绍了Rassar（在增强现实中的房间可访问性和安全扫描），这是一种新的概念验证原型，用于半自动化的原型，使用LIDAR +相机数据，机器学习和AR来识别，分类和本地化室内可访问性和安全性问题。我们概述了当前的耙子原型和单个房屋中的初步评估。
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.
