Problem
Research questions and friction points this paper is trying to address.
3D UAV Localization
Sparse LiDAR Geometry
Modality-Imbalanced Fusion
Redundant Feature Transmission
PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
🤖 AI Summary
为解决无人机3D定位中稀疏LiDAR几何、模态不平衡融合及冗余特征传输问题，提出PRI-Net，采用点云渲染、残差注意力融合和信息瓶颈方法。
📝 Abstract
Accurate 3D localization of unmanned aerial vehicles (UAVs) remains challenging for existing multimodal approaches due to sparse LiDAR geometry, modality-imbalanced fusion, and redundant feature transmission over constrained edge-to-server links. To address these limitations, we propose PRI-Net, an efficient and lightweight multimodal fusion framework for UAV localization that integrates point cloud splatting, residual attention fusion, and an information bottleneck. Specifically, a 3D point cloud splatting (3DPCS) strategy is introduced to transform sparse LiDAR observations into geometrically consistent dense depth maps. A residual attention fusion (RAF) module is then designed to alleviate modal bias by using an image branch for coarse estimation and a gated fusion branch for refinement. In addition, a multimodal information bottleneck (MIB) module compacts features by filtering task-irrelevant redundancy. Experiments show that PRI-Net achieves high localization accuracy with lightweight architectures, while reducing feature dimensionality and improving edge-to-server UAV sensing efficiency and robustness.
Innovation
Methods, ideas, or system contributions that make the work stand out.
3DPCS
RAF
MIB
Multimodal Fusion
Lightweight Architecture
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