Long-term autonomy for mobile robots requires both robust self-localization and reliable map maintenance. Conventional landmark-based methods face a fundamental trade-off between landmarks with high detectability but low distinctiveness (e.g., poles) and those with high distinctiveness but difficult stable detection (e.g., local point cloud structures). This work addresses the challenge of descriptively identifying a unique "signature" (local point cloud) by leveraging a detectable, high-precision "anchor" (like a pole). To solve this, we propose a novel canonical representation, "Pole-Image," as a hybrid method that uses poles as anchors to generate signatures from the surrounding 3D structure. Pole-Image represents a pole-like landmark and its surrounding environment, detected from a LiDAR point cloud, as a 2D polar coordinate image with the pole itself as the origin. This representation leverages the pole's nature as a high-precision reference point, explicitly encoding the "relative geometry" between the stable pole and the variable surrounding point cloud. The key advantage of pole landmarks is that "detection" is extremely easy. This ease of detection allows the robot to easily track the same pole, enabling the automatic and large-scale collection of diverse observational data (positive pairs). This data acquisition feasibility makes "Contrastive Learning (CL)" applicable. By applying CL, the model learns a viewpoint-invariant and highly discriminative descriptor. The contributions are twofold: 1) The descriptor overcomes perceptual aliasing, enabling robust self-localization. 2) The high-precision encoding enables high-sensitivity change detection, contributing to map maintenance.

翻译：移动机器人的长期自主性需要鲁棒的自身定位与可靠的地图维护。传统的基于地标的方法面临一个根本性的权衡：要么选择可检测性高但区分度低的地标（如杆柱），要么选择区分度高但难以稳定检测的地标（如局部点云结构）。本研究旨在解决一个挑战：如何利用一个可检测的、高精度的“锚点”（如杆柱）来对唯一的“特征签名”（局部点云）进行描述性识别。为此，我们提出了一种新颖的规范表示方法——“Pole-Image”，作为一种混合方法，它使用杆柱作为锚点，从周围的三维结构中生成特征签名。Pole-Image 将从 LiDAR 点云中检测到的杆状地标及其周围环境，表示为一幅以杆柱本身为原点的二维极坐标图像。这种表示利用了杆柱作为高精度参考点的特性，显式地编码了稳定的杆柱与变化的周围点云之间的“相对几何关系”。杆柱地标的关键优势在于其“检测”极其容易。这种易于检测的特性使得机器人能够轻松跟踪同一根杆柱，从而实现多样化观测数据（正样本对）的自动、大规模采集。这种数据获取的可行性使得“对比学习”得以应用。通过应用对比学习，模型学习到一个视点不变且具有高区分度的描述符。本工作的贡献有两点：1) 该描述符克服了感知混叠，实现了鲁棒的自身定位。2) 高精度的编码实现了高灵敏度的变化检测，有助于地图维护。