AirAlign: ジオメトリを考慮したUAV最終メートル航法のための相対ポーズ整列
AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation
UAVの最終接近段階での正確なポーズ整列を実現するため、事前学習済みの視覚幾何再構成モデルを用いてRGB画像ペアから相対ポーズを推定するフレームワークを提案した。
著者: Jinyi Zhou, Shuo Feng, Yufei Wu, Piji Li
分類: cs.CV
原文アブストラクト
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.