Hierarchical topological representations can significantly reduce search times within mapping and localization algorithms. Although recent research has shown the potential for such approaches, limited consideration has been given to the suitability and comparative performance of different global feature representations within this context. In this work, we evaluate state-of-the-art hand-crafted and learned global descriptors using a hierarchical topological mapping technique on benchmark datasets and present results of a comprehensive evaluation of the impact of the global descriptor used. Although learned descriptors have been incorporated into place recognition methods to improve retrieval accuracy and enhance overall recall, the problem of scalability and efficiency when applied to longer trajectories has not been adequately addressed in a majority of research studies. Based on our empirical analysis of multiple runs, we identify that continuity and distinctiveness are crucial characteristics for an optimal global descriptor that enable efficient and scalable hierarchical mapping, and present a methodology for quantifying and contrasting these characteristics across different global descriptors. Our study demonstrates that the use of global descriptors based on an unsupervised learned Variational Autoencoder (VAE) excels in these characteristics and achieves significantly lower runtime. It runs on a consumer grade desktop, up to 2.3x faster than the second best global descriptor, NetVLAD, and up to 9.5x faster than the hand-crafted descriptor, PHOG, on the longest track evaluated (St Lucia, 17.6 km), without sacrificing overall recall performance.

翻译：分层拓扑表示能显著降低地图构建与定位算法中的搜索时间。尽管近期研究已表明此类方法的潜力，但关于该背景下不同全局特征表示的适用性与比较性能的考量仍较为有限。本文利用分层拓扑地图构建技术，在基准数据集上评估了最先进的手工设计与学习型全局描述符，并呈现了关于全局描述符影响的综合评估结果。尽管学习型描述符已被纳入地点识别方法以提升检索精度和整体召回率，但多数研究并未充分解决其在较长轨迹中应用时的可扩展性与效率问题。基于多次运行的实证分析，我们发现连续性与区分性是实现高效可扩展分层地图构建的最优全局描述符的关键特性，并提出了一种量化与对比不同全局描述符这些特性的方法论。研究表明，基于无监督学习变分自编码器（VAE）的全局描述符在这些特性上表现优异，且实现了显著更低的运行时开销。在消费级台式机上，针对最长评估轨迹（St Lucia，17.6公里），其运行速度比次优全局描述符NetVLAD快2.3倍，比手工设计描述符PHOG快9.5倍，且未牺牲整体召回率性能。