用跨模态注意力融合俯视图与地面激光，实现精准车辆定位
TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning
- 通过双向注意力对齐俯视影像与地面激光数据
- 在CARLA和KITTI上定位误差降低63%，达亚米级精度
- 适合自动驾驶中复杂视角下的定位任务
地表与航拍定位困难源于视角和模态差异。我们提出TransLocNet，一种融合激光几何与航拍语义的跨模态注意力框架。将激光扫描投影为鸟瞰表示，并通过双向注意力与航拍特征对齐，再经似然图解码器输出位置与朝向的概率分布。对比学习模块强制共享嵌入空间以增强跨模态对齐。在CARLA和KITTI上的实验表明，TransLocNet优于现有基线，定位误差最高降低63%，实现亚米级、亚度级精度。结果证明该方法在合成与真实场景下均具鲁棒性和泛化能力。
原文摘要 · Abstract (English)
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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