FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles

Vineet Gokhale, Gerardo Moyers Barrera, R. Venkatesha Prasad

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
43 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationICC 2021 - IEEE International Conference on Communications, Proceedings
Subtitle of host publicationProceedings
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)978-1-7281-7122-7
ISBN (Print)978-1-7281-7123-4
Publication statusPublished - 2021
EventICC 2021 - IEEE International Conference on Communications - Virtual at Montreal, Canada
Duration: 14 Jun 202123 Jun 2021

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


ConferenceICC 2021 - IEEE International Conference on Communications
CityVirtual at Montreal

Bibliographical note

Accepted author manuscript


  • AIV
  • FEEL
  • Localization
  • accuracy
  • energy-efficiency
  • latency

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