BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird’s-Eye View Images

We present BEV-SLD, a LiDAR global localization method building on the Scene Landmark Detection (SLD) concept. Unlike scene-agnostic pipelines, our new 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 a learnable set of global landmark coordinates with per-frame heatmaps, yielding consistent detection and reliable occurrence across the scene. Across campus, industrial, and forest environments, BEV-SLD delivers robust localization and outperforms state-of-the-art methods. Code and trained models will be released after publication.