Edge-Cloud Based Vehicle SLAM for Autonomous Indoor Map Updating

Zepeng Zhu, Jiajia Liu, Jiadai Wang, Nei Kato

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Map information is of crucial importance to ensure the safety and reliability of vehicle, no matter indoor or outdoor, it should reflect the real-time changes of environment. Existing indoor map update mechanisms have several common limitations such as small update range, long cycle, large amount of update data, high cost and poor currency. Therefore, we present a multi-vehicle collaborative indoor map update scheme based on edge-cloud architecture to realize real-time autonomous map updating. This scheme can be achieved through continuous monitoring, tagging, identification, and layering of the environment during driving process. Compared with traditional map update schemes, experimental results show that our scheme can effectively realize the collaborative map update in indoor environment, enhance the map update efficiency, reduce the update delay, and improve the adaptability of vehicles.

Original languageEnglish
Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194844
Publication statusPublished - 2020 Nov
Event92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
Duration: 2020 Nov 18 → …

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
CityVirtual, Victoria
Period20/11/18 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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