  This paper introduces an innovative dataset specifically crafted for
challenging agricultural settings (a greenhouse), where achieving precise
localization is of paramount importance. The dataset was gathered using a
mobile platform equipped with a set of sensors typically used in mobile robots,
as it was moved through all the corridors of a typical Mediterranean greenhouse
featuring tomato crop. This dataset presents a unique opportunity for
constructing detailed 3D models of plants in such indoor-like space, with
potential applications such as robotized spraying. For the first time to the
best knowledge of authors, a dataset suitable to put at test Simultaneous
Localization and Mapping (SLAM) methods is presented in a greenhouse
environment, which poses unique challenges. The suitability of the dataset for
such goal is assessed by presenting SLAM results with state-of-the-art
algorithms. The dataset is available online in
\url{https://arm.ual.es/arm-group/dataset-greenhouse-2024/}.
