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Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion

Research output: Contribution to journalConference articleResearchpeer review

Authors

  • Faezeh Mortazavi
  • Alexander Kuzminykh
  • Volker Ahlers
  • Claus Brenner
  • Monika Sester

External Research Organisations

  • University of Applied Sciences and Arts Hannover (HsH)

Details

Original languageEnglish
Pages (from-to)81-87
Number of pages7
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume48
Publication statusPublished - 13 Dec 2024
EventOptical 3D Metrology, O3DM 2024 - Brescia, Italy
Duration: 12 Dec 202413 Dec 2024

Abstract

This paper presents a novel multi-stereo camera system for robust indoor localization, leveraging point cloud data and temporal fusion techniques. The system integrates three synchronized stereo cameras to capture point clouds from multiple angles, enhancing coverage and improving point cloud density in complex indoor environments. By combining data from different perspectives and accumulating point clouds over time, the method mitigates the limitations in the short range of point clouds derived from stereo cameras, ensuring broader coverage for effective localization. To manage the computational complexity of large-scale point clouds and reduce noise in accumulated data, voxelization is applied to downsample the point clouds while preserving key geometric features. The localization process is driven by a predictive point cloud odometry method, refined through the Iterative Closest Point (ICP) algorithm. Experimental results demonstrate the system’s ability to achieve accurate localization within a pre-built LiDAR map. This study highlights the feasibility of using low-cost stereo camera systems as an alternative to LiDAR-based solutions for indoor localization.

Keywords

    Indoor Localization, LiDAR Sensor, Point Cloud, Stereo Camera, Voxelization

ASJC Scopus subject areas

Cite this

Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion. / Mortazavi, Faezeh; Kuzminykh, Alexander; Ahlers, Volker et al.
In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 48, 13.12.2024, p. 81-87.

Research output: Contribution to journalConference articleResearchpeer review

Mortazavi, F, Kuzminykh, A, Ahlers, V, Brenner, C & Sester, M 2024, 'Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion', International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, vol. 48, pp. 81-87. https://doi.org/10.5194/isprs-archives-XLVIII-2-W7-2024-81-2024
Mortazavi, F., Kuzminykh, A., Ahlers, V., Brenner, C., & Sester, M. (2024). Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 48, 81-87. https://doi.org/10.5194/isprs-archives-XLVIII-2-W7-2024-81-2024
Mortazavi F, Kuzminykh A, Ahlers V, Brenner C, Sester M. Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2024 Dec 13;48:81-87. doi: 10.5194/isprs-archives-XLVIII-2-W7-2024-81-2024
Mortazavi, Faezeh ; Kuzminykh, Alexander ; Ahlers, Volker et al. / Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. 2024 ; Vol. 48. pp. 81-87.
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abstract = "This paper presents a novel multi-stereo camera system for robust indoor localization, leveraging point cloud data and temporal fusion techniques. The system integrates three synchronized stereo cameras to capture point clouds from multiple angles, enhancing coverage and improving point cloud density in complex indoor environments. By combining data from different perspectives and accumulating point clouds over time, the method mitigates the limitations in the short range of point clouds derived from stereo cameras, ensuring broader coverage for effective localization. To manage the computational complexity of large-scale point clouds and reduce noise in accumulated data, voxelization is applied to downsample the point clouds while preserving key geometric features. The localization process is driven by a predictive point cloud odometry method, refined through the Iterative Closest Point (ICP) algorithm. Experimental results demonstrate the system{\textquoteright}s ability to achieve accurate localization within a pre-built LiDAR map. This study highlights the feasibility of using low-cost stereo camera systems as an alternative to LiDAR-based solutions for indoor localization.",
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AU - Mortazavi, Faezeh

AU - Kuzminykh, Alexander

AU - Ahlers, Volker

AU - Brenner, Claus

AU - Sester, Monika

N1 - Publisher Copyright: © Author(s) 2024.

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