TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning
This work solves a domain-specific problem for autonomous vehicles and robotics by improving localization accuracy in both synthetic and real-world settings, though it is incremental as it builds on existing cross-modal attention and contrastive learning methods.
The paper tackles the problem of aerial-ground vehicle localization by addressing viewpoint and modality gaps between ground-level LiDAR and overhead imagery, resulting in a reduction of localization error by up to 63% and achieving sub-meter, sub-degree accuracy.
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.