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arXiv · 2603.17159

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

David Skuddis·Vincent Ress·Wei Zhang·Vincent Ofosu Nyako·Norbert Haala
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Abstract

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.

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David Skuddis, Vincent Ress, Wei Zhang, Vincent Ofosu Nyako, Norbert Haala. 2026-03-17. BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird's-Eye View Images. https://arxiv.org/abs/2603.17159

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