DECO: Depth-Guided Co-Visibility Reasoning for Low-Altitude UAV Visual Localization

2026-08-23

AI Summary

为解决低空无人机在GNSS缺失环境下的视觉定位问题，DECO通过深度引导的共可见性推理框架，改进特征匹配和姿态估计。

Abstract

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.

Problem

• UAV
• visual localization
• GNSS-denied
• orthographic reference maps
• co-visibility

Innovation

• Depth-guided
• Co-visibility Reasoning
• Geometry-Saliency Coupled Co-visibility Score
• Low-altitude UAV Visual Localization