  This article presents a novel approach to identifying and classifying
intersections for semantic and topological mapping. More specifically, the
proposed novel approach has the merit of generating a semantically meaningful
map containing intersections, pathways, dead ends, and pathways leading to
unexplored frontiers. Furthermore, the resulting semantic map can be used to
generate a sparse topological map representation, that can be utilized by
robots for global navigation. The proposed solution also introduces a built-in
filtering to handle noises in the environment, to remove openings in the map
that the robot cannot pass, and to remove small objects to optimize and
simplify the overall mapping results. The efficacy of the proposed semantic and
topological mapping method is demonstrated over a map of an indoor structured
environment that is built from experimental data. The proposed framework, when
compared with similar state-of-the-art topological mapping solutions, is able
to produce a map with up to 89% fewer nodes than the next best solution.
