用自监督方法在激光雷达俯视图中定位场景地标，实现精准全局定位。
BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird's-Eye View Images
- 通过俯视图自监督学习发现场景特有模式作为地标
- 在校园、工业区和森林环境均实现稳定定位性能
- 适合需要高精度定位的自动驾驶与机器人导航
我们提出BEV-SLD，一种基于场景地标检测（SLD）概念的激光雷达全局定位方法。与通用场景流程不同，该自监督方法利用鸟瞰图（BEV）图像在指定空间密度下发现场景特有的模式并将其作为地标。通过一致性损失将可学习的全局地标坐标与每帧热图对齐，实现场景内一致的地标检测。在校园、工业区及森林等多种环境中，BEV-SLD均表现出鲁棒的定位能力，性能优于现有最先进方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。