PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization

• URL: http://arxiv.org/abs/2609.14469v1
• Date: Sun, 13 Sep 2026 12:18:51 GMT
• Title: PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
• Abstract summary: PRI-Net is an efficient and lightweight multimodal fusion framework for UAV localization.<n>A 3D point cloud splatting (3DPCS) strategy is introduced to transform sparse LiDAR observations into geometrically consistent dense depth maps.<n>A residual attention fusion (RAF) module is then designed to alleviate modal bias.<n>A multimodal information bottleneck (MIB) module compacts features by filtering task-irrelevant redundancy.
• Score: 18.199437709938177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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