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

arXiv: https://arxiv.org/abs/2604.06382 · HTML: https://mlsanity.com/paper/2604.06382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications](https://mlsanity.com/paper/2607.00978/) (2026)
- [Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB](https://mlsanity.com/paper/2609.28360/) (2026)

## Abstract (arXiv)

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.