  Autonomous vehicles have created a sensation in both outdoor and indoor
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 centimetres 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 opportunistically
minimizing energy consumption of FEEL by adjusting the sensing frequency to the
dynamics of the physical environment. Our extensive performance evaluation over
diverse test settings reveal that FEEL provides a localization accuracy of <7cm
with ultra-low latency of around 3ms. Further, ASA yields up to 20% energy
saving with only a marginal trade-off in accuracy.
