MCOP: Multi-UAV Collaborative Occupancy Prediction

• URL: http://arxiv.org/abs/2510.12679v2
• Date: Wed, 15 Oct 2025 01:11:34 GMT
• Title: MCOP: Multi-UAV Collaborative Occupancy Prediction
• Authors: Zefu Lin, Wenbo Chen, Xiaojuan Jin, Yuran Yang, Lue Fan, Yixin Zhang, Yufeng Zhang, Zhaoxiang Zhang,
• Abstract summary: Current Bird's Eye View (BEV)-based approaches exhibit two main limitations.<n>We propose a novel multi-UAV collaborative occupancy prediction framework.<n>Our method achieves state-of-the-art accuracy, significantly outperforming existing collaborative methods.
• Score: 40.58729551462363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned Aerial Vehicle (UAV) swarm systems necessitate efficient collaborative perception mechanisms for diverse operational scenarios. Current Bird's Eye View (BEV)-based approaches exhibit two main limitations: bounding-box representations fail to capture complete semantic and geometric information of the scene, and their performance significantly degrades when encountering undefined or occluded objects. To address these limitations, we propose a novel multi-UAV collaborative occupancy prediction framework. Our framework effectively preserves 3D spatial structures and semantics through integrating a Spatial-Aware Feature Encoder and Cross-Agent Feature Integration. To enhance efficiency, we further introduce Altitude-Aware Feature Reduction to compactly represent scene information, along with a Dual-Mask Perceptual Guidance mechanism to adaptively select features and reduce communication overhead. Due to the absence of suitable benchmark datasets, we extend three datasets for evaluation: two virtual datasets (Air-to-Pred-Occ and UAV3D-Occ) and one real-world dataset (GauUScene-Occ). Experiments results demonstrate that our method achieves state-of-the-art accuracy, significantly outperforming existing collaborative methods while reducing communication overhead to only a fraction of previous approaches.

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