Tracking of inventory and rearrangement of misplaced items are some of the
most labor-intensive tasks in a retail environment. While there have been
attempts at using vision-based techniques for these tasks, they mostly use
planogram compliance for detection of any anomalies, a technique that has been
found lacking in robustness and scalability. Moreover, existing systems rely on
human intervention to perform corrective actions after detection. In this
paper, we present Co-AD, a Concept-based Anomaly Detection approach using a
Vision Transformer (ViT) that is able to flag misplaced objects without using a
prior knowledge base such as a planogram. It uses an auto-encoder architecture
followed by outlier detection in the latent space. Co-AD has a peak success
rate of 89.90% on anomaly detection image sets of retail objects drawn from the
RP2K dataset, compared to 80.81% on the best-performing baseline of a standard
ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile
manipulation pipeline to autonomously correct the anomalies flagged by Co-AD.
This work is ultimately aimed towards developing autonomous mobile robot
solutions that reduce the need for human intervention in retail store
management.

使用基于概念的异常检测方法和视觉转换器（ViT），Co-AD 能够检测出零售物品中的放错物品，达到 89.90% 的峰值成功率，比基准的标准 ViT 自编码器的 80.81% 高。此研究旨在开发减少零售店铺管理中人为干预需求的自主移动机器人解决方案。

基于概念的零售商店异常检测与移动机器人自动纠正

Concept-based Anomaly Detection in Retail Stores for Automatic  Correction using Mobile Robots

This paper presents a study on the development of an obstacle-avoidance
navigation system for autonomous navigation in home environments. The system
utilizes vision-based techniques and advanced path-planning algorithms to
enable the robot to navigate toward the destination while avoiding obstacles.
The performance of the system is evaluated through qualitative and quantitative
metrics, highlighting its strengths and limitations. The findings contribute to
the advancement of indoor robot navigation, showcasing the potential of
vision-based techniques for real-time, autonomous navigation.

本文介绍了一种基于视觉技术和先进路径规划算法的自主机器人导航系统，该系统可以避开障碍物并朝着目的地导航，其性能通过定性和定量指标进行评估，研究结果有助于室内机器人导航的发展。