根据用户隐私偏好，设计可调节分辨率的机器人视觉导航系统。
Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences
- 基于用户研究设计可配置的隐私保护视觉策略
- 用户更倾向低分辨率图像和抽象视觉信息
- 支持根据距离与隐私等级动态调整图像清晰度
视觉导航是移动服务机器人的基础能力，但其搭载的摄像头可能采集敏感隐私信息，引发用户担忧。现有隐私保护导航视觉感知方法多基于技术考量，缺乏对用户隐私偏好的支撑。本文提出一种以用户为中心的设计方法，通过两次用户研究发现：用户更偏好隐私保护型视觉抽象及捕获时的低分辨率保留机制；其偏好的RGB分辨率同时受隐私等级和机器人距离影响。基于此，我们进一步提出一种可配置的距离-分辨率隐私策略，用于实现隐私保护的机器人视觉导航。
原文摘要 · Abstract (English)
Visual navigation is a fundamental capability of mobile service robots, yet the onboard cameras required for such navigation can capture privacy-sensitive information and raise user privacy concerns. Existing approaches to privacy-preserving navigation-oriented visual perception have largely been driven by technical considerations, with limited grounding in user privacy preferences. In this work, we propose a user-centered approach to designing privacy-preserving visual perception for robot navigation. To investigate how user privacy preferences can inform such design, we conducted two user studies. The results show that users prefer privacy-preserving visual abstractions and capture-time low-resolution preservation mechanisms: their preferred RGB resolution depends both on the desired privacy level and robot proximity during navigation. Based on these findings, we further derive a user-configurable distance-to-resolution privacy policy for privacy-preserving robot visual navigation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。