Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Outdoor Warehouse Management: UAS-Driven Precision Tracking of Stacked Steel Bars

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月28日

查看全文
  • Assia Belbachir,
  • Antonio M. Ortiz,
  • Erik T. Hauge,
  • Ahmed Nabil Belbachir,
  • Giusy Bonanno,
  • Emanuele Ciccia,
  • Giorgio Felline

摘要

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.