UAV distance estimation plays an important role for path planning of swarm UAVs and collision avoidance. However, the lack of annotated data seriously hinder the related studies. In this paper, we build and present a UAVDE dataset for UAV distance estimation, in which distance between two UAVs is obtained by UWB sensors. During experiments, we surprisingly observe that the commonly used stereo triangulation can not stand for UAV scenes. The core reason is the position deviation issue of UAVs due to long shooting distance and camera vibration, which is common in UAV scenes. To tackle this issue, we propose a novel position correction module (PCM), which can directly predict the offset between the image positions and the actual ones of UAVs and perform calculation compensation in stereo triangulation. Besides, to further boost performance on hard samples, we propose a dynamic iterative correction mechanism, which is composed of multiple stacked PCMs and a gating mechanism to adaptively determine whether further correction is required according to the difficulty of data samples. Consequently, the position deviation issue can be effectively alleviated. We conduct extensive experiments on UAVDE, and our proposed method can achieve a 38.84% performance improvement, which demonstrates its effectiveness and superiority. The code and dataset would be released.

翻译：无人机距离估计在集群无人机路径规划和碰撞避免中扮演着重要角色。然而，标注数据的匮乏严重阻碍了相关研究。本文构建并展示了一个用于无人机距离估计的UAVDE数据集，其中无人机间的距离由UWB传感器获取。实验过程中，我们惊讶地发现常用的立体三角测量方法不适用于无人机场景。其核心原因是由于远距离拍摄和相机振动导致的无人机位置偏差问题，这在无人机场景中十分常见。为解决此问题，我们提出了一种新颖的位置校正模块（PCM），该模块可直接预测无人机图像位置与实际位置之间的偏移量，并在立体三角测量中进行计算补偿。此外，为进一步提升难样本上的性能，我们提出了一种动态迭代校正机制，该机制由多个堆叠的PCM和一个门控机制组成，可根据数据样本的难度自适应决定是否需要进一步校正。由此，位置偏差问题得以有效缓解。我们在UAVDE数据集上进行了大量实验，所提方法实现了38.84%的性能提升，证明了其有效性和优越性。代码和数据集将公开发布。