  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.
