FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles
This addresses the critical need for precise and efficient localization in warehouse automation, though it appears incremental as it builds on existing sensor fusion techniques.
The paper tackles the problem of fast, energy-efficient localization for autonomous indoor vehicles by proposing FEEL, a system that fuses IMU, UWB, and radar sensors with an adaptive algorithm, achieving <7cm accuracy, 3ms latency, and up to 20% energy savings.
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.