Autonomous vehicles have created a sensation in both outdoor and indoor applications. The famous indoor usecase 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 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 <7 cm with ultra-low latency of ≈3 ms. Further, ASA yields up to 20% energy saving with only a marginal trade off in accuracy.
Autonomous vehicles have created a sensation in both indoor and outdoor applications. The famous indoor usecase 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.
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