ArticleSN computer science2025
Outdoor Warehouse Management: UAS-Driven Precision Tracking of Stacked Steel Bars.
Article in SN computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately identifying the positions of products in outdoor environments-such as warehouses or industrial yards-presents unique challenges due to variable lighting, weather conditions, and the lack of fixed infrastructure. This work presents a vision-based drone system for product localization using QR code detection and relative positioning. The proposed system enables a UAV to autonomously scan an area, extract QR codes from captured video frames, and compute the spatial relationships between products using a trust-ability graph. Unlike traditional GPS- or RFID-based methods, our approach does not rely on external infrastructure, making it scalable and adaptable for outdoor and semi-structured environments. We demonstrate that the proposed algorithm achieves over 94% positioning accuracy in indoor settings and 80% in outdoor environments, even under occlusion and varying illumination. The key contributions of this work include: (1) a novel infrastructure-free method for product positioning based on relative spatial relationships, (2) the integration of trust-ability scoring to improve the reliability of detected positions, and (3) an extensive evaluation in real-world indoor and outdoor industrial scenarios. These results validate the potential of UAV-assisted inventory systems to enhance automation in logistics and warehouse management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.