While pole-like structures are widely recognized as stable geometric anchors for long-term robot localization, their identification reliability degrades significantly under Pole-at-Distance (Pad) observations typical of large-scale urban environments. This paper shifts the focus from descriptor design to a systematic investigation of descriptor robustness. Our primary contribution is the establishment of a specialized evaluation framework centered on the Small Pole Landmark (SPL) dataset. This dataset is constructed via an automated tracking-based association pipeline that captures multi-view, multi-distance observations of the same physical landmarks without manual annotation. Using this framework, we present a comparative analysis of Contrastive Learning (CL) and Supervised Learning (SL) paradigms. Our findings reveal that CL induces a more robust feature space for sparse geometry, achieving superior retrieval performance particularly in the 5--10m range. This work provides an empirical foundation and a scalable methodology for evaluating landmark distinctiveness in challenging real-world scenarios.

翻译：尽管杆状结构作为长期机器人定位的稳定几何锚点已被广泛认可，但在大规模城市环境中典型的远距离杆状观测条件下，其识别可靠性显著下降。本文的研究重点从描述符设计转向对描述符鲁棒性的系统性探究。我们的主要贡献是建立了一个以小型杆状地标数据集为核心的专业化评估框架。该数据集通过基于自动跟踪的关联流程构建，能够捕获同一物理地标的多视角、多距离观测数据，且无需人工标注。利用该框架，我们对对比学习与监督学习两种范式进行了比较分析。研究发现，对比学习能为稀疏几何结构诱导出更具鲁棒性的特征空间，尤其在5-10米距离范围内实现了更优的检索性能。本研究为评估具有挑战性的真实场景中地标独特性提供了实证基础和可扩展的方法论。