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.

翻译：本文提出了一种构建拓扑度量地图的新方法，该方法能够在移动机器人应用中实现高效导航与决策。该方法从二维栅格地图生成拓扑度量地图，将输入地图的区域分割为不同的结构-语义类别：交叉口、通道、死胡同以及通往未探索区域的通道。该方法基于一种新颖的交叉口检测技术，能以语义上有意义的方式识别交叉口的区域及其开口。该框架引入了两层预滤波，以极小的计算成本消除地图中对高层级地图分割与决策不重要的细小开口和物体。GRID-FAST生成的拓扑地图支持在大规模环境中进行快速导航，其结构语义信息可辅助任务规划、自主探索以及人机协作。通过在机器人实验采集的真实地图上进行验证，证明了所提方法的有效性：1）结构化室内环境，2）非结构化洞穴类地下环境，以及3）包含通道、建筑物和分散物体的大规模室外环境。此外，所提框架与当前最先进的拓扑建图方案进行了比较，能够生成节点数比次优方案减少高达 \blue92% 的拓扑度量地图与拓扑地图。