FEEL

Abstract

Autonomous vehicles have created a sensation in both indoor and outdoor applications. The famous indoor use-case is process automation inside a warehouse using Autonomous Indoor Vehicles (AIV). These vehicles need to locate themselves not only with an accuracy of a few centimeters but also within a few milliseconds in an energy-efficient manner. Due to these challenges, localization is a holy grail. In this paper, we propose FEEL – an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar. We provide detailed software and hardware architecture of FEEL. Further, we propose Adaptive Sensing Algorithm (ASA) for optimizing for localization accuracy and energy consumption of FEEL by adjusting the sensing rate to the dynamics of the physical environment in real-time. Our extensive performance evaluation over diverse test settings reveals that FEEL provides a localization accuracy of sub-7 cm with an ultra-low latency of around 3 ms. Additionally, ASA yields up to 20% energy savings with only a marginal trade off in accuracy. Furthermore, we show that FEEL outperforms state of the art in indoor localization.