In the past few years, the technology of automated guided vehicles (AGVs) has notably advanced. In particular, in the context of factory and warehouse automation, different approaches have been presented for detecting and localizing pallets inside warehouses and shop-floor environments. In a related research paper [1], we show that an AGVs can detect, localize, and track pallets using machine learning techniques based only on the data of an on-board 2D laser rangefinder. Such sensor is very common in industrial scenarios due to its simplicity and robustness, but it can only provide a limited amount of data. Therefore, it has been neglected in the past in favor of more complex solutions. In this paper, we release to the community the data we collected in [1] for further research activities in the field of pallet localization and tracking. The dataset comprises a collection of 565 2D scans from real-world environments, which are divided into 340 samples where pallets are present, and 225 samples where they are not. The data have been manually labelled and are provided in different formats.
翻译:过去几年来,自动制导车(AGVs)技术取得了显著的进步,特别是在工厂和仓库自动化方面,对仓库和商店楼层环境中的货盘的探测和本地化提出了不同方法;在一份相关研究论文[1]中,我们表明,AGVs只能根据2D号激光测距仪的数据,利用机器学习技术探测、本地化和跟踪货盘;这种传感器在工业情景中非常常见,因为其简单和稳健,但只能提供数量有限的数据;因此,在过去,它被忽略了,偏好了更为复杂的解决方案;在本文件中,我们向社区发放了我们在[1]号文件中收集的数据,用于在托盘定位和跟踪领域进一步开展研究活动;数据集包括从现实环境中收集的565 2D扫描,这些扫描分为有托盘的340个样本,而没有收集的样本有225个。这些数据是手工标注的,并以不同格式提供。