A 2D laser rangefinder scans dataset of standard EUR pallets
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In the past few years, the technology of automated guided vehicles (AGVs) has notably advanced. In particular, in the field of factory and warehouse automation, different approaches have been presented for detecting and localizing pallets inside warehouses and shop-floor environments based on the data acquired from 2D laser rangefinders. In [1], we present a robust approach allowing AGVs to detect, localize, and track multiple pallets using machine learning techniques based on an on-board 2D laser rangefinder. In this paper, the data used in [1, 2] for solving the problem of detection, localization and tracking of pallets is described. Furthermore, we present an open repository of dataset and code to the community for further research activities. The dataset comprises a collection of 565 2D scans from real-world environments, which are divided into 340 samples where pallets are present, whereas 225 samples represent the case in which no pallets are present.
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