针对远距离电杆定位难题，提出新评估框架并验证对比学习更优
Pole-centric Descriptors for Robust Robot Localization: Evaluation under Pole-at-Distance (PaD) Observations using the Small Pole Landmark (SPL) Dataset
- 构建自动标注的多视角远距离电杆数据集，系统评估特征鲁棒性
- 对比学习在5-10米距离下检索性能更优，对稀疏几何更具适应性
- 为真实城市环境中的地标识别提供可复现的评估方法，适合定位研究者
尽管电杆类结构被视为长期机器人定位的稳定几何锚点，但在典型的大规模城市环境中，远距离观测（Pole-at-Distance, PaD）下其识别可靠性显著下降。本文不再聚焦描述符设计，而是系统研究描述符的鲁棒性。主要贡献是建立以Small Pole Landmark (SPL) 数据集为核心的专用评估框架。该数据集通过自动化跟踪关联流程构建，无需人工标注即可获取同一物理地标在多视角、多距离下的观测数据。基于此框架，我们对对比学习（CL）与监督学习（SL）范式进行对比分析。结果表明，对比学习在稀疏几何条件下能生成更鲁棒的特征空间，在5–10米范围内实现更优的检索性能。本工作为复杂真实场景下地标独特性评估提供了实证基础和可扩展的方法论。
原文摘要 · Abstract (English)
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.
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