BEV-SLD：基于激光雷达鸟瞰图图像的自监督场景地标检测全局定位
BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird's-Eye View Images
把 SLD 的『场景地标』思路从相机图像迁移到激光雷达 BEV 图像上，用可学习全局地标坐标与逐帧热力图的一致性损失实现自监督地标发现，可作为无需三维点云地图的轻量级全局重定位方案复用。
原文依据 our self-supervised approach leverages bird's-eye-view (BEV) images to discover scene-specific patterns at a prescribed spatial density and treat them as landmarks
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文提出 BEV-SLD，一种建立在场景地标检测（SLD）概念之上的激光雷达全局定位方法。与场景无关的流程不同，本文的自监督方法利用鸟瞰图（BEV）图像，在预设的空间密度下发现场景特有的模式，并将其作为地标。一致性损失将可学习的全局地标坐标与逐帧热力图对齐，从而在整个场景中获得一致的地标检测结果。在校园、工业与森林环境中，BEV-SLD 均能实现稳健的定位，并与最优方法相比取得强劲的性能。
英文原文摘要 614 字符
We present BEV-SLD, a LiDAR global localization method building on the Scene Landmark Detection (SLD) concept. Unlike scene-agnostic pipelines, our self-supervised approach leverages bird's-eye-view (BEV) images to discover scene-specific patterns at a prescribed spatial density and treat them as landmarks. A consistency loss aligns learnable global landmark coordinates with per-frame heatmaps, yielding consistent landmark detections across the scene. Across campus, industrial, and forest environments, BEV-SLD delivers robust localization and achieves strong performance compared to state-of-the-art methods.