  This paper proposes an approach to build 3D scene graphs in arbitrary indoor
and outdoor environments. Such extension is challenging; the hierarchy of
concepts that describe an outdoor environment is more complex than for indoors,
and manually defining such hierarchy is time-consuming and does not scale.
Furthermore, the lack of training data prevents the straightforward application
of learning-based tools used in indoor settings. To address these challenges,
we propose two novel extensions. First, we develop methods to build a spatial
ontology defining concepts and relations relevant for indoor and outdoor robot
operation. In particular, we use a Large Language Model (LLM) to build such an
ontology, thus largely reducing the amount of manual effort required. Second,
we leverage the spatial ontology for 3D scene graph construction using Logic
Tensor Networks (LTN) to add logical rules, or axioms (e.g., "a beach contains
sand"), which provide additional supervisory signals at training time thus
reducing the need for labelled data, providing better predictions, and even
allowing predicting concepts unseen at training time. We test our approach in a
variety of datasets, including indoor, rural, and coastal environments, and
show that it leads to a significant increase in the quality of the 3D scene
graph generation with sparsely annotated data.
