  This article introduces a novel approach to constructing a topometric map
that allows for efficient navigation and decision-making in mobile robotics
applications. The method generates the topometric map from a 2D grid-based map.
The topometric map segments areas of the input map into different
structural-semantic classes: intersections, pathways, dead ends, and pathways
leading to unexplored areas. This method is grounded in a new technique for
intersection detection that identifies the area and the openings of
intersections in a semantically meaningful way. The framework introduces two
levels of pre-filtering with minimal computational cost to eliminate small
openings and objects from the map which are unimportant in the context of
high-level map segmentation and decision making. The topological map generated
by GRID-FAST enables fast navigation in large-scale environments, and the
structural semantics can aid in mission planning, autonomous exploration, and
human-to-robot cooperation. The efficacy of the proposed method is demonstrated
through validation on real maps gathered from robotic experiments: 1) a
structured indoor environment, 2) an unstructured cave-like subterranean
environment, and 3) a large-scale outdoor environment, which comprises
pathways, buildings, and scattered objects. Additionally, the proposed
framework has been compared with state-of-the-art topological mapping solutions
and is able to produce a topometric and topological map with up to \blue92%
fewer nodes than the next best solution.
