DECO：用于低空无人机视觉定位的深度引导共可见性推理

中文摘要 AI 辅助

无人机（UAV）在GNSS信号不可用的环境中越来越需要可靠的视觉定位能力。一种常见的解决方案是通过匹配无人机图像与从卫星或航空图像生成的带地理标签的正射参考图之间的关键点来估计无人机位姿，随后使用 Perspective-n-Point（PnP）进行位姿求解。然而，这类参考图主要记录屋顶、地面平面等自上而下的表面，而立面、墙壁等垂直结构往往被压缩或缺失。因此，低空无人机图像中许多视觉上具有辨识度的关键点在参考图中没有有效的对应点，导致匹配冗余和位姿估计不准确。为解决该问题，我们提出DECO——一种用于低空无人机视觉定位的深度引导共可见性推理框架。DECO利用单目深度先验推断局部表面几何结构，并估计无人机图像与参考图之间的共可见区域。基于该先验，我们引入几何-显著性耦合共可见性得分，以联合考虑几何共可见性和检测器显著性来进行关键点排序。通过这种方式，DECO保留了既具有视觉辨识度又具备几何共可见性的关键点，从而改进特征匹配和基于PnP的位姿估计。大量实验表明，DECO实现了更优的定位性能，且可与不同的深度模型、特征检测器及匹配器集成。源代码将在该httpsURL处提供。

英文摘要

Unmanned aerial vehicles (UAVs) increasingly require robust visual localization in GNSS-denied environments. A common solution estimates UAV poses by matching keypoints between UAV images and geo-tagged orthographic reference maps derived from satellite or aerial imagery, followed by Perspective-\(n\)-Point (PnP) pose solving. However, such reference maps mainly record top-down surfaces such as roofs and ground planes, while vertical structures such as facades and walls are often compressed or missing. Consequently, many visually distinctive keypoints in low-altitude UAV images have no valid counterparts in the reference map, leading to redundant matches and inaccurate pose estimation. To address this issue, we propose DECO, a DEpth-guided CO-visibility reasoning framework for low-altitude UAV visual localization. DECO uses monocular depth priors to infer local surface geometry and estimate co-visible regions between UAV images and the reference map. Based on this prior, a Geometry-Saliency Coupled Co-visibility Score is introduced to jointly consider geometric co-visibility and detector saliency for keypoint ranking. In this way, DECO retains keypoints that are both visually distinctive and geometrically co-visible, improving feature matching and PnP-based pose estimation. Extensive experiments demonstrate that DECO achieves superior localization performance and can be integrated with different depth models, feature detectors, and matchers. The source code will be available at https://github.com/UAV-AVL/DECO.

发表机构

• College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航天科学与工程学院）

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