To address the scale mismatch caused by large altitude variations in UAV visual place recognition, we propose a monocular vision-only altitude-adaptive geo-localization framework. The method first estimates relative altitude from a single downward-looking image by transforming the input into the frequency domain and formulating altitude estimation as a regression-as-classification (RAC) problem. The estimated altitude is then used to crop the query image to a canonical scale, after which a classification-then-retrieval visual place recognition module performs coarse localization. To improve retrieval robustness under varying image quality, we further introduce a quality-adaptive margin classifier (QAMC) and refine the final location by weighted coordinate estimation over the top retrieved candidates. Experiments on two synthetic datasets and two real-flight datasets show that the relative altitude estimation (RAE) module yields clear overall improvements in downstream retrieval performance under significant altitude changes. With our visual place recognition module, altitude adaptation improves average R@1 and R@5 by 41.50 and 56.83 percentage points, respectively, compared with using the same retrieval pipeline without altitude normalization, and the full system runs at 13.3 frames/s on the reported workstation hardware. These results indicate that relative altitude estimation provides an effective scale prior for cross-altitude UAV geo-localization and supports GPS-denied coarse initialization without auxiliary range sensors or temporal inputs.

翻译：针对无人机视觉地点识别中因高度剧烈变化导致的尺度不匹配问题，本文提出一种仅依赖单目视觉的自适应高度地理定位框架。该方法首先将单张下视图像变换至频域，将高度估计建模为回归-分类联合问题（RAC），从而从该图像中估计相对高度。随后利用估计高度将查询图像裁剪至规范尺度，再通过分类-检索联合的视觉地点识别模块实现粗定位。为提升不同图像质量下的检索鲁棒性，我们进一步引入质量自适应间隔分类器（QAMC），并通过加权坐标估计从排名靠前的候选结果中精化最终位置。在两组合成数据集与两组真实飞行数据集上的实验表明：在显著高度变化条件下，相对高度估计模块（RAE）能有效提升下游检索性能。采用所提视觉地点识别模块后，相较于未进行高度归一化的相同检索流程，高度自适应使平均R@1与R@5分别提升41.50和56.83个百分点，整套系统在报告的工作站硬件上运行速度达13.3帧/秒。结果表明，相对高度估计可为跨高度无人机地理定位提供有效的尺度先验，且无需辅助测距传感器或时序输入即可支持GPS拒止环境下的粗初始化。