Multimodal Image-Based Indoor Localization with Machine Learning-A Systematic Review

被引：3

作者：

Lukasik, Szymon ^{[1
]}

Szott, Szymon ^{[2
]}

Leszczuk, Mikolaj ^{[2
]}

机构：

[1] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland

[2] AGH Univ Krakow, PL-30059 Krakow, Poland

来源：

SENSORS | 2024年 / 24卷 / 18期

关键词：

computer vision; data fusion; indoor localization; machine learning; positioning; FUSION; NAVIGATION;

D O I：

10.3390/s24186051

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Outdoor positioning has become a ubiquitous technology, leading to the proliferation of many location-based services such as automotive navigation and asset tracking. Meanwhile, indoor positioning is an emerging technology with many potential applications. Researchers are continuously working towards improving its accuracy, and one general approach to achieve this goal includes using machine learning to combine input data from multiple available sources, such as camera imagery. For this active research area, we conduct a systematic literature review and identify around 40 relevant research papers. We analyze contributions describing indoor positioning methods based on multimodal data, which involves combinations of images with motion sensors, radio interfaces, and LiDARs. The conducted survey allows us to draw conclusions regarding the open research areas and outline the potential future evolution of multimodal indoor positioning.

引用

页数：20

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