Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences

TL;DR

This paper introduces a user-centered approach to privacy-preserving visual perception in robot navigation, emphasizing user preferences for privacy abstractions and resolution control.

Contribution

It presents a novel privacy policy framework based on user studies that guides robot visual perception to align with user privacy preferences.

Findings

Users prefer privacy-preserving visual abstractions.

Capture-time low-resolution mechanisms are favored.

Preferred RGB resolution varies with privacy level and robot proximity.

Abstract

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.