LoD-Loc v2：基于显式轮廓对齐的低细节层级城市模型航空视觉定位

我们提出了一种用于在低细节层级（LoD）城市模型上的航空视觉定位的新方法。以前的基于轮廓线对齐的方法 LoD-Loc 已显示出利用 LoD 模型获得良好定位结果的潜力。然而，LoD-Loc 主要依赖高细节层级（LoD3 或 LoD2）的城市模型，而大多数可用模型以及许多国家计划构建的全国模型都是低细节层级（LoD1）。因此，使定位在低细节层级城市模型上成为可能， could unlock drones' potential for global urban localization. 为了解决这些问题，我们引入 LoD-Loc v2, which employs a coarse-to-fine strategy using explicit silhouette alignment to achieve accurate localization over low-LoD city models in the air. Specifically, given a query image, LoD-Loc v2 first applies a building segmentation network to shape building silhouettes. Then, in the coarse pose selection stage, we construct a pose cost volume by uniformly sampling pose hypotheses around a prior pose to represent the pose probability distribution. Each cost of the volume measures the degree of alignment between the projected and predicted silhouettes. We select the pose with maximum value as the coarse pose. In the fine pose estimation stage, a particle filtering method incorporating a multi-beam tracking approach is used to efficiently explore the hypothesis space and obtain the final pose estimation. To further facilitate research in this field, we release two datasets with LoD1 city models covering 10.7 km , along with real RGB queries and ground-truth pose annotations. Experimental results show that LoD-Loc v2 improves estimation accuracy with high-LoD models and enables localization with low-LoD models for the first time. Moreover, it outperforms state-of-the-art baselines by large margins, even surpassing texture-model-based methods, and broadens the convergence basin to accommodate larger prior errors.