TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning

Phu Pham; Damon Conover; Aniket Bera

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arXiv · 2512.10419

TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning

Phu Pham·Damon Conover·Aniket Bera

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Abstract

Aerial-ground localization is difficult due to large viewpoint and modality gaps between ground-level LiDAR and overhead imagery. We propose TransLocNet, a cross-modal attention framework that fuses LiDAR geometry with aerial semantic context. LiDAR scans are projected into a bird's-eye-view representation and aligned with aerial features through bidirectional attention, followed by a likelihood map decoder that outputs spatial probability distributions over position and orientation. A contrastive learning module enforces a shared embedding space to improve cross-modal alignment. Experiments on CARLA and KITTI show that TransLocNet outperforms state-of-the-art baselines, reducing localization error by up to 63% and achieving sub-meter, sub-degree accuracy. These results demonstrate that TransLocNet provides robust and generalizable aerial-ground localization in both synthetic and real-world settings.

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BibTeX RIS

Phu Pham, Damon Conover, Aniket Bera. 2025-12-11. TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning. https://arxiv.org/abs/2512.10419

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