提出月面跨视角定位新数据集，实现地表与航拍图像精准匹配。
Learning Cross-view Correspondences for Geo-localization on Planetary Surfaces
- 构建基于真实月面模型的全景图与正上方影像配对数据集。
- 在10438组数据上训练后，定位准确率显著优于传统方法。
- 适合研究行星探测视觉定位、遥感与导航融合的学者。
在缺乏卫星定位系统的行星表面探索中，保持全局位置感知是一项根本挑战，因机载里程计会随时间漂移。尽管轨道映射产品（如俯视图像和地形图）提供全局上下文，但将它们与地表观测对齐仍面临视角差异大、纹理低、地形重复及光照与地形变化导致的外观剧烈改变等问题。本文构建了一个新的跨视角地理定位基准数据集，该数据集由物理渲染的地表全景图与基于高分辨率月面地形模型生成的航拍图组成。数据集包含10438个地表全景图（360°），每张均对应精确位于同一位置的正上方影像；此外还提供重叠瓦片，用于研究非中心定位场景下存在多个可能候选的情况。我们在该数据集上训练了当前最先进的基于Transformer的地理定位方法，并报告检索准确率。结果表明，基于学习的跨视角定位方法可成功应用于行星表面领域，为全球导航卫星系统提供一种基于视觉的替代方案。
原文摘要 · Abstract (English)
Maintaining global position awareness is a fundamental challenge for planetary surface exploration, since satellite-based positioning systems are unavailable and onboard odometry drifts over time. Although orbital mapping products, such as overhead imagery and terrain-derived maps, provide global context, aligning them with surface observations is challenging due to large viewpoint differences, low texture, repetitive terrain, and drastic changes in appearance caused by varying illumination and topography. We introduce a new cross-view geo-localization benchmark built from physically rendered surface panoramas and overhead tiles derived from a high-resolution lunar terrain model. Our dataset contains 10438 ground views rendered as 360$^\circ$ surface panoramas with matching overhead images precisely centered at the same location. Additionally, a set of overlapping tiles is provided to study off-center localization with multiple plausible candidates per panorama. We study the performance of a state-of-the-art transformer-based geo-localization method on our data, by training it from scratch and reporting retrieval accuracy. Our results demonstrate that learning-based cross-view localization methods can be successfully applied to the domain of planetary surfaces, providing a vision-based alternative to global navigation satellite systems.
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