Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, gender, identity, activity and even emotional state of humans within that scene. This raises important questions about the reach, lifespan, and potential misuse of this information. This paper is a call to action to consider privacy in the context of robotic vision. We propose a specific form privacy preservation in which no images are captured or could be reconstructed by an attacker even with full remote access. We present a set of principles by which such systems can be designed, and through a case study in localisation demonstrate in simulation a specific implementation that delivers an important robotic capability in an inherently privacy-preserving manner. This is a first step, and we hope to inspire future works that expand the range of applications open to sighted robotic systems.

翻译：视觉是机器人学中一种常用且有效的传感器，能够从中提取丰富的环境信息：场景的几何与语义特征，以及场景中人类的年龄、性别、身份、活动甚至情感状态。这引发了关于这些信息的覆盖范围、生命周期及潜在滥用的重要问题。本文旨在呼吁在机器人视觉的背景下考虑隐私保护问题。我们提出了一种特定的隐私保护形式，其中即使攻击者拥有完全远程访问权限，也无法捕获或重建任何图像。我们提出了一套可设计此类系统的原则，并通过一个定位案例研究，在仿真中展示了一种具体实现，该实现以固有的隐私保护方式提供了一项重要的机器人能力。这是迈出的第一步，我们期望能启发未来的工作，从而扩展有视觉能力的机器人系统的应用范围。