AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation

• URL: http://arxiv.org/abs/2608.21926v1
• Date: Sat, 22 Aug 2026 11:32:41 GMT
• Title: AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation
• Abstract summary: AirAlign is a framework for RGB-only image-pair relative pose alignment for UAVs.<n>AirAlign uses a pretrained visual geometry reconstruction model as the backbone.<n>Experiments on the PairUAV challenge demonstrate the effectiveness and robustness of our method.
• Score: 20.21278599561095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned aerial vehicle (UAV) navigation in modern low-altitude environments requires more accurate pose alignment in the final approach stage for target information acquisition or manipulation, making "last-meter" navigation increasingly important. However, severe viewpoint and appearance variations make this task challenging. To tackle this problem, we propose AirAlign, a framework for RGB-only image-pair relative pose alignment for UAVs. AirAlign uses a pretrained visual geometry reconstruction model as the backbone to extract geometry-aware features from source-target image pairs. In addition, to better utilize the limited training data, we split the training set into multiple scene-disjoint folds for unseen cross-validation and model selection. During inference, the predictions of the selected models are averaged to form the ensemble output of the overall framework. Experiments on the PairUAV challenge at the ACMMM 2026 Workshop on UAVs in Multimedia demonstrate the effectiveness and robustness of our method, while comprehensive ablation studies validate the contribution of each component.

Related papers

• Last-Meter Precision Navigation for UAVs: A Diffusion-Refined Aerial Visual Servoing Approach [18.42619895752036]
  Last-meter precision navigation for UAVs is challenging due to scale ambiguity, rotation discontinuities, and the need for fine-grained spatial reasoning.<n>We propose DreamNav, a coarse-to-fine diffusion-refined aerial visual servoing framework.<n>Experiments show DreamNav outperforms strong visual servoing and foundation model baselines in accuracy and generalization, with zero-shot transfer to unseen scenes.
  arXiv  Detail & Related papers  (2026-07-05T15:16:44Z)
• AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World [75.54693105865003]
  We introduce AerialMetric, a benchmark dataset to evaluate and facilitate the adaptation of monocular metric depth estimation under UAV aerial viewpoints.<n>AerialMetric provides 52K real-world and 16K synthetic image-depth pairs with reliable metric ground truth.
  arXiv  Detail & Related papers  (2026-06-29T02:48:47Z)
• SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion [76.8430169655641]
  spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation.<n>We introduce SpatialUAV, a real low-altitude UAV benchmark covering semantic discrimination, spatial relation, aerial-aerial collaboration, aerial-ground collaboration, and motion understanding.<n>We show that current models remain far from human-level performance, with pronounced bottlenecks in cross-view association, structured grounding, geometric reasoning, and temporal viewpoint understanding.
  arXiv  Detail & Related papers  (2026-06-26T09:16:55Z)
• Unifying UAV Cross-View Geo-Localization via 3D Geometric Perception [51.687842983240564]
  Cross-view geo-localization for Unmanned Aerial Vehicles (UAVs) remains challenging due to the severe geometric discrepancy between oblique UAV imagery and satellite maps.<n>We propose a geometry-aware UAV geo-localization framework that explicitly models the 3D scene geometry to unify coarse place recognition and fine-grained pose estimation.<n>Our method significantly outperforms state-of-the-art baselines, achieving robust meter-level localization accuracy and improved generalization in complex urban environments.
  arXiv  Detail & Related papers  (2026-04-02T08:08:41Z)
• Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation [51.286599397552756]
  We present Bearing-UAV, a vision-driven cross-view navigation method that jointly predicts UAV absolute location and heading from neighboring features.<n>We also present Bearing-UAV-90k, a benchmark for evaluating cross-view localization and navigation.
  arXiv  Detail & Related papers  (2026-03-23T16:17:39Z)
• Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning [5.517595398768408]
  We present a unified aerial VLN framework that operates solely on ego monocular RGB observations and natural language instructions.<n>This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery.
  arXiv  Detail & Related papers  (2025-12-09T14:25:24Z)
• UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery [14.599037804047724]
  Unmanned aerial vehicle object detection (UAV-OD) has been widely used in various scenarios.<n>Most existing UAV-OD algorithms rely on manually designed components, which require extensive tuning.<n>This paper proposes an efficient detection transformer (DETR) framework tailored for UAV imagery.
  arXiv  Detail & Related papers  (2025-01-03T15:11:14Z)
• SOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection [68.18620488664187]
  We propose a simple yet effective Semi-supervised Oriented Object Detection method termed SOOD++.<n> Specifically, we observe that objects from aerial images usually have arbitrary orientations, small scales, and dense distribution.<n>Extensive experiments conducted on various oriented object under various labeled settings demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-07-01T07:03:51Z)
• CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation [20.476683921252867]
  We propose a novel Cross-View Adaptation (CROVIA) approach to adapt the knowledge learned from on-road vehicle views to UAV views. First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views. Second, cross-view correlations from image space are effectively transferred to segmentation space without any requirement of paired on-road and UAV view data.
  arXiv  Detail & Related papers  (2023-04-14T15:20:40Z)
• Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
  We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
  arXiv  Detail & Related papers  (2023-04-06T14:33:05Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.