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

• URL: http://arxiv.org/abs/2512.10419v1
• Date: Thu, 11 Dec 2025 08:34:26 GMT
• Title: TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning
• Authors: Phu Pham, Damon Conover, Aniket Bera,
• Abstract summary: TransLocNet is a cross-modal attention framework that fuses LiDAR geometry with aerial semantic context.<n>Experiments on CARLA and KITTI show that TransLocNet outperforms state-of-the-art baselines.
• Score: 14.74396995978237
• License: http://creativecommons.org/licenses/by/4.0/
• 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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