Details
| Original language | English |
|---|---|
| Pages (from-to) | 81-87 |
| Number of pages | 7 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Publication status | Published - 13 Dec 2024 |
| Event | Optical 3D Metrology, O3DM 2024 - Brescia, Italy Duration: 12 Dec 2024 → 13 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
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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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 journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Integrated Multi-Stereo Camera System for Robust Indoor Localization with Temporal Fusion
AU - Mortazavi, Faezeh
AU - Kuzminykh, Alexander
AU - Ahlers, Volker
AU - Brenner, Claus
AU - Sester, Monika
N1 - Publisher Copyright: © Author(s) 2024.
PY - 2024/12/13
Y1 - 2024/12/13
N2 - 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.
AB - 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.
KW - Indoor Localization
KW - LiDAR Sensor
KW - Point Cloud
KW - Stereo Camera
KW - Voxelization
UR - http://www.scopus.com/inward/record.url?scp=105001562924&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLVIII-2-W7-2024-81-2024
DO - 10.5194/isprs-archives-XLVIII-2-W7-2024-81-2024
M3 - Conference article
AN - SCOPUS:105001562924
VL - 48
SP - 81
EP - 87
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
T2 - Optical 3D Metrology, O3DM 2024
Y2 - 12 December 2024 through 13 December 2024
ER -