With the advancement of collaborative perception, the role of aerial-ground
collaborative perception, a crucial component, is becoming increasingly
important. The demand for collaborative perception across different
perspectives to construct more comprehensive perceptual information is growing.
However, challenges arise due to the disparities in the field of view between
cross-domain agents and their varying sensitivity to information in images.
Additionally, when we transform image features into Bird's Eye View (BEV)
features for collaboration, we need accurate depth information. To address
these issues, we propose a framework specifically designed for aerial-ground
collaboration. First, to mitigate the lack of datasets for aerial-ground
collaboration, we develop a virtual dataset named V2U-COO for our research.
Second, we design a Cross-Domain Cross-Adaptation (CDCA) module to align the
target information obtained from different domains, thereby achieving more
accurate perception results. Finally, we introduce a Collaborative Depth
Optimization (CDO) module to obtain more precise depth estimation results,
leading to more accurate perception outcomes. We conduct extensive experiments
on both our virtual dataset and a public dataset to validate the effectiveness
of our framework. Our experiments on the V2U-COO dataset and the DAIR-V2X
dataset demonstrate that our method improves detection accuracy by 6.1% and
2.7%, respectively.

通过开发虚拟数据集 V2U-COO，设计跨领域自适应模块 CDCA 以及协同深度优化模块 CDO，本文提出了一种专门用于空中 - 地面协同感知的框架，并在虚拟数据集和公共数据集上进行了广泛实验证明了其有效性，使检测准确率分别提高了 6.1% 和 2.7%。