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

• URL: http://arxiv.org/abs/2603.17159v1
• Date: Tue, 17 Mar 2026 21:44:16 GMT
• Title: BEV-SLD: Self-Supervised Scene Landmark Detection for Global Localization with LiDAR Bird's-Eye View Images
• Abstract summary: We present BEV-SLD, a LiDAR global localization method building on the Scene Landmark Detection (SLD) concept.<n>Unlike scene-agnostic pipelines, our self-supervised approach leverages bird's-eye-view (BEV) images to discover scene-specific patterns.<n>A consistency loss aligns learnable global landmark coordinates with per-frame heatmaps, yielding consistent landmark detections.
• Score: 6.6441836770192815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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