RF-Based Indoor Localization Around Corners

Abstract

Unmanned robots are increasingly used around humans in factories, malls, and hotels. As they navigate our space, it is important to ensure that such robots do not collide with people who suddenly appear as they turn a corner. Today, however, there is no practical solution for localizing people around corners. Optical solutions try to track hidden people through their visible shadows on the floor or a sidewall, but they can easily fail depending on the ambient light and the environment. More recent work has considered the use of radio frequency (RF) signals to track people and vehicles around street corners. However, past RF-based proposals rely on a simplistic ray-tracing model that fails in practical indoor scenarios. This thesis introduces CornerRadar, an RF-based method that provides accurate around-corner indoor localization. CornerRadar addresses the limitations of the ray-tracing model used in past work. It does so through a novel encoding of how RF signals bounce off walls and occlusions. The encoding, which we call the hint map, is then fed to a neural network along with the radio signals to localize people around corners. Empirical evaluation with people moving around corners in 56 indoor environments shows that CornerRadar achieves a median error that is 3x to 12x smaller than past RF-based solutions for localizing people around corners.