The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
This addresses security and safety issues for robots in indoor environments, though it appears incremental as it builds on existing audio sensing techniques.
The paper tackles the problem of detecting and localizing people who are trying to sneak up on a robot by using only incidental sounds they produce, even when quiet, and demonstrates a method that enables a robot to track a single moving person with passive audio sensing.
How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. We collect a robotic dataset of high-quality 4-channel audio paired with 360 degree RGB data of people moving in different indoor settings. We train models that predict if there is a moving person nearby and their location using only audio. We implement our method on a robot, allowing it to track a single person moving quietly with only passive audio sensing. For demonstration videos, see our project page: https://sites.google.com/view/unkidnappable-robot