Pole-centric Descriptors for Robust Robot Localization: Evaluation under Pole-at-Distance (PaD) Observations using the Small Pole Landmark (SPL) Dataset

TL;DR

This paper evaluates the robustness of pole-like structure descriptors for robot localization under Pole-at-Distance observations, introducing a new dataset and comparing contrastive and supervised learning methods to improve landmark recognition in urban environments.

Contribution

It establishes a novel evaluation framework with the SPL dataset and provides a comparative analysis of learning paradigms for robust pole landmark descriptors.

Findings

Contrastive Learning yields more robust features for sparse geometry.

CL outperforms SL in retrieval accuracy at 5--10m range.

The framework enables scalable evaluation of landmark distinctiveness.

Abstract

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.