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

Szymon Łukasik1, Szymon Szott2, Mikołaj Leszczuk2

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This study reviews multimodal indoor positioning systems, combining sensors like cameras and LiDAR. Machine learning enhances accuracy by fusing data from multiple sources for better indoor location services.

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Area of Science:

  • Computer Science
  • Robotics
  • Geomatics Engineering

Background:

  • Outdoor positioning is widespread, enabling services like navigation.
  • Indoor positioning is an emerging field with significant application potential.
  • Improving indoor positioning accuracy is a key research challenge.

Purpose of the Study:

  • To systematically review existing research on multimodal indoor positioning.
  • To analyze methods combining various data sources for indoor localization.
  • To identify future research directions in this domain.

Main Methods:

  • Conducted a systematic literature review of approximately 40 research papers.
  • Analyzed indoor positioning techniques utilizing multimodal data.
  • Focused on combinations of camera imagery, motion sensors, radio interfaces, and LiDAR.

Main Results:

  • Identified various multimodal approaches for indoor positioning.
  • Highlighted the integration of diverse sensors like cameras, IMUs, Wi-Fi, and LiDAR.
  • Demonstrated the effectiveness of machine learning in fusing sensor data.

Conclusions:

  • Multimodal data fusion is crucial for accurate indoor positioning.
  • Machine learning plays a vital role in enhancing localization accuracy.
  • Further research is needed to explore novel sensor combinations and advanced algorithms.