Semantic and Topological Mapping using Intersection Identification

TL;DR

This paper introduces a novel semantic and topological mapping approach that identifies and classifies intersections, producing simplified, noise-filtered maps for robotic navigation, demonstrated in indoor environments with significant node reduction.

Contribution

The paper presents a new method for semantic and topological mapping that effectively filters noise and reduces map complexity compared to existing solutions.

Findings

Achieves up to 89% fewer nodes than state-of-the-art methods.

Successfully maps indoor environments with semantic and topological features.

Demonstrates robustness to environmental noise and map simplification.

Abstract

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.