VDB-based Spatially Grounded Semantics for Interactive Robots

This paper presents a new approach for representing spatially-grounded semantics in interactive robots. The method combines spatial and symbolic data to improve robot interactions in human-occupied environments. A key feature is a voxel-based data structure optimized for dynamic and sparse information, along with a global lookup table to manage and track spatially-grounded entities and their relationships. The implementation, which is integrated into a ROS 2-based framework, allows for seamless querying through semantic web APIs such as SPARQL. Initial tests demonstrate the efficiency of this system in supporting advanced scenarios in human-robot interaction. All the repositories developed as part of this contribution can be found at github.com/RepresentationMaps.