Recent advances in vision-based indoor navigation: A systematic literature review. (May 2022)

• Record Type: Journal Article
• Authors: Khan, Dawar; Cheng, Zhanglin; Uchiyama, Hideaki; Ali, Sikandar; Asshad, Muhammad; Kiyokawa, Kiyoshi
• Is Part Of: Computers & graphics. Volume 104(2022)
• Page Start: 24
• Page End: 45
• Publication Date: 2022-05
• Subjects: Indoor navigation -- Computer vision -- Visual positioning -- Location tracking -- Pattern recognition; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2022.03.005
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: 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. Graphical abstract: Highlights: Executed the first SLR study on vision-based methods for indoor navigation. As a result, 68 studies were found, analyzed, and presented in six categories. Each article is analyzed for data extraction and its quality assessment. We extracted key idea, pros and con, and analysis metrics from each article. A number of interested future direction have been drawn.