This paper addresses the problem of building global topological maps from 3D LiDAR point clouds for autonomous mobile robots operating in large-scale, dynamic, and unknown environments. Adaptive Resonance Theory-based Topological Clustering with Different Topologies (ATC-DT) builds global topological maps represented as graphs while mitigating catastrophic forgetting during sequential processing. However, its winner selection mechanism relies on an exhaustive nearest-neighbor search over all existing nodes, leading to scalability limitations as the map grows. To address this challenge, we propose a hierarchical extension called Multi-Layer ATC (MLATC). MLATC organizes nodes into a hierarchy, enabling the nearest-neighbor search to proceed from coarse to fine resolutions, thereby drastically reducing the number of distance evaluations per query. The number of layers is not fixed in advance. MLATC employs an adaptive layer addition mechanism that automatically deepens the hierarchy when lower layers become saturated, keeping the number of user-defined hyperparameters low. Simulation experiments on synthetic large-scale environments show that MLATC accelerates topological map building compared to the original ATC-DT and exhibits a sublinear, approximately logarithmic scaling of search time with respect to the number of nodes. Experiments on campus-scale real-world LiDAR datasets confirm that MLATC maintains a millisecond-level per-frame runtime and enables real-time global topological map building in large-scale environments, significantly outperforming the original ATC-DT in terms of computational efficiency.

翻译：本文针对在大型、动态、未知环境中运行的自主移动机器人，研究从3D LiDAR点云构建全局拓扑地图的问题。基于自适应共振理论的多拓扑结构拓扑聚类（ATC-DT）能够构建以图表示的全局拓扑地图，并在顺序处理过程中缓解灾难性遗忘。然而，其胜者选择机制依赖于对所有现有节点进行穷举式最近邻搜索，导致随着地图规模扩大而出现可扩展性限制。为解决这一挑战，我们提出了一种分层扩展方法，称为多层ATC（MLATC）。MLATC将节点组织为层次结构，使最近邻搜索能够从粗到细的分辨率逐层进行，从而显著减少每次查询的距离计算次数。层数并非预先固定，MLATC采用自适应层添加机制，当较低层趋于饱和时自动加深层次结构，从而保持用户定义超参数的数量较少。在合成大规模环境中的仿真实验表明，与原始ATC-DT相比，MLATC加速了拓扑地图构建，并且搜索时间随节点数量呈亚线性、近似对数的增长趋势。在校园尺度真实世界LiDAR数据集上的实验证实，MLATC能够维持毫秒级的每帧运行时间，实现大规模环境中的实时全局拓扑地图构建，在计算效率方面显著优于原始ATC-DT。