综述无人零售中的传感技术挑战与解决方案
A Survey of Challenges and Sensing Technologies in Autonomous Retail Systems
- 融合视觉、RFID、称重等多模态传感提升精度
- 解决遮挡、实时处理和防窃等核心难题
- 适合关注无人店技术落地的研究者与从业者
无人商店利用先进传感技术实现无收银购物、实时库存追踪和无缝客户交互。然而，这些系统面临显著挑战，包括基于视觉的追踪遮挡问题、传感器部署的可扩展性、防盗难题以及实时数据处理。为应对这些问题，研究者探索了多模态传感方法，整合计算机视觉、射频识别（RFID）、称重感应、基于振动的检测和激光雷达（LiDAR），以提升准确性和效率。本文全面回顾了无人零售环境中使用的传感技术，突出其优势、局限性及集成策略。我们将现有解决方案按库存追踪、环境监测、人员追踪和盗窃检测分类，讨论关键挑战与新兴趋势。最后，我们展望了未来可扩展、低成本且注重隐私保护的无人商店系统发展方向。
原文摘要 · Abstract (English)
Autonomous stores leverage advanced sensing technologies to enable cashier-less shopping, real-time inventory tracking, and seamless customer interactions. However, these systems face significant challenges, including occlusion in vision-based tracking, scalability of sensor deployment, theft prevention, and real-time data processing. To address these issues, researchers have explored multi-modal sensing approaches, integrating computer vision, RFID, weight sensing, vibration-based detection, and LiDAR to enhance accuracy and efficiency. This survey provides a comprehensive review of sensing technologies used in autonomous retail environments, highlighting their strengths, limitations, and integration strategies. We categorize existing solutions across inventory tracking, environmental monitoring, people-tracking, and theft detection, discussing key challenges and emerging trends. Finally, we outline future directions for scalable, cost-efficient, and privacy-conscious autonomous store systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。