Recent advances in vision-based indoor navigation: A systematic literature review

Dawar Khan*, Zhanglin Cheng, Hideaki Uchiyama, Sikandar Ali, Muhammad Asshad, Kiyoshi Kiyokawa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Indoor navigation has remained an active research area for the last decade. Unlike outdoor environments, indoor environments have additional challenges, such as weak signals, low light, and complex scenarios. Different technologies are used for indoor navigation, including WiFi, Bluetooth, inertial sensors, and computer cameras. Vision-based methods have great potentials for indoor navigation as they fulfill most of the general requirements such as minimal cost, ease of use, ease of implementation, and realism. Therefore, researchers have successively proposed different novel vision-based approaches for indoor navigation. Unfortunately, there is no standard review article (except a few general reviews) that covers the current trends and draws a pipeline for future research. In this paper, we reviewed the current state-of-the-art vision-based indoor navigation methods. We followed the systematic literature review (SLR) methodology for article searching, selection, and quality assessments. In total, we selected 68 articles after final selection using SLR. We classified these articles into different categories. Each article is briefly studied for information extraction, including key idea, category of the article, evaluation criterion, and its strengths and weaknesses. We also highlighted several interesting future directions. This study will help new researchers to grasp the research challenge as well as present the results of their research in the field. It will also help the community to find a suitable indoor navigation system according to users’ requirements.

Original languageEnglish (US)
Pages (from-to)24-45
Number of pages22
JournalComputers and Graphics (Pergamon)
StatePublished - May 2022

Bibliographical note

Funding Information:
This work was supported in part by NSFC (No. 62150410433 , 61972388 ), Shenzhen Basic Research Program ( JCYJ20180507182222355 ), CAS-PIFI (No. 2020PT0013 ) and JSPS KAKENHI (No. JP20K11891 ).

Publisher Copyright:
© 2022 Elsevier Ltd


  • Computer vision
  • Indoor navigation
  • Location tracking
  • Pattern recognition
  • Visual positioning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • General Engineering
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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