GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

TL;DR

This paper presents GRID-FAST, a grid-based intersection detection method that efficiently constructs semantic topometric maps for mobile robotics, enabling fast navigation and decision-making in diverse environments.

Contribution

The paper introduces a novel intersection detection technique integrated into a framework for generating semantic topometric maps from 2D grid maps, reducing node count and improving efficiency.

Findings

Achieved up to 92% fewer nodes compared to state-of-the-art methods.

Validated on real maps from indoor, subterranean, and outdoor environments.

Enhanced map segmentation with minimal computational cost.

Abstract

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.