Multi-unmanned aerial vehicle (UAV) collaborative 3-D object detection can comprehend complex environments by integrating complementary information, with applications encompassing traffic monitoring, delivery services, and agricultural management. However, the extremely broad observations in aerial remote sensing and significant perspective differences across multiple UAVs make it challenging to achieve precise and consistent feature mapping from 2-D images to 3-D space in multi-UAV collaborative 3-D object detection paradigm. To address the problem, we propose an unparalleled camera-based multi-UAV collaborative 3-D object detection paradigm called UCDNet. Specifically, the depth information from the UAVs to the ground is explicitly utilized as a strong prior to provide a reference for more accurate and generalizable feature mapping. Additionally, we design a homologous point geometric consistency loss as an auxiliary self-supervision, which directly influences the feature mapping module, thereby strengthening the global consistency of multiview perception. Experiments on AeroCollab3D and CoPerception-UAVs datasets show that our method increases 4.7% and 10% mean Average Precision (mAP) respectively compared to the baseline, which demonstrates the superiority of UCDNet.

With the advancement of collaborative perception, the role of autonomous aerial vehicle (AAV)–vehicle collaborative perception has become increasingly significant. The demand for collaborative perception from various perspectives to construct comprehensive perceptual information is rising. However, challenges emerge due to differences in the field of view (FOV) between cross-domain agents and their varying sensitivities to image information. Furthermore, accurate depth information is essential for collaboration to transform image features into bird’s eye view (BEV) features. To address these challenges, we propose a framework specifically designed for aerial-ground collaboration. First, to address the deficiency of datasets for aerial-ground collaboration, we have developed 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 results. We conduct extensive experiments on both our virtual dataset and a public dataset to validate the effectiveness of our framework. Our method resolves the feature fusion issue under significant height differences, a challenge that previous BEV generation methods struggled to address effectively. Our experiments on the V2U-COO and DAIR-V2X datasets demonstrate improvements in detection accuracy of 6.1% and 2.7%, respectively. Our code will be released athttps://github.com/wyccoo/uvcp.

Remote sensing lightweight foundation models have successfully achieved online perception, providing real-time intelligent interpretation. However, their capabilities are restricted to inferences solely based on their respective observations and models, thus lacking a comprehensive understanding of large-scale remote sensing scenarios. To address this limitation, we propose RingMo-Galaxy, a remote sensing distributed foundation model based on generalized information mapping and interaction. RingMo-Galaxy can realize online collaborative perception across multiple platforms and diverse downstream tasks by mapping observations into a unified space and implementing a task-agnostic information interaction strategy. Specifically, we leverage the ground-based geometric prior of remote sensing oblique observations to change feature mapping from absolute to relative depth estimation, thereby enhancing the model’s ability to extract generalized features across diverse heights and perspectives. In addition, we present a dual-branch information compression module to decouple high-frequency and low-frequency features, achieving feature-level compression while preserving critical task-agnostic details. To support our research, we collect a multitask simulation dataset named AirCo-MultiTasks, specifically designed for multi-unmanned aerial vehicle (UAV) collaborative observation. We also conduct extensive experiments, including 3-D object detection, instance segmentation, and trajectory prediction. The numerous results demonstrate that our proposed RingMo-Galaxy achieves state-of-the-art performance across various downstream tasks.

Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observations make it difficult to generate precise Bird's Eye View (BEV) representations. Besides, excessive interactions can not meet real-time prediction requirements within the constrained drone-based communication bandwidth. To address these problems, we propose a novel framework named "Drones Help Drones" (DHD). Firstly, we incorporate the ground priors provided by the drone's inclined observation to estimate the distance between objects and drones, leading to more precise BEV generation. Secondly, we design a selective mechanism based on the local feature discrepancy to prioritize the critical information contributing to prediction tasks during inter-drone interactions. Additionally, we create the first dataset for multi-drone collaborative prediction, named "Air-Co-Pred", and conduct quantitative and qualitative experiments to validate the effectiveness of our DHD framework. The results demonstrate that compared to state-of-the-art approaches, DHD reduces position deviation in BEV representations by over 20\% and requires only a quarter of the transmission ratio for interactions while achieving comparable prediction performance. Moreover, DHD also shows promising generalization to the collaborative 3D object detection in CoPerception-UAVs.