A Multimodal Scale Normalization Framework for Vision-Radar Small UAV Positioning

Abstract

Uncrewed aerial vehicles (UAVs) positioning is of crucial importance in diverse applications. However, it is extremely challenging to realize the precise UAVs positioning over long distances due to the small size and dramatic scale variations associated with the high mobility in the wide area. To tackle this issue, a multimodal scale normalization framework is proposed for the scale-robust precise pixel-level UAV positioning. The framework exploits our proposed distance-aware image slicing and distance-aware scale normalization module. Moreover, a modal fusion-based scale normalization network is proposed that can accept arbitrary low-resolution UAV patches and produce the consistent high-resolution images at a uniform UAV instance scale with a single learnable model. The proposed framework is generic and can be directly used in the existing pixel-level positioning pipelines to improve the positioning performance and scale robustness. To verify the proposed framework in the real application, a practical vision-radar UAV positioning system is developed. Experimental results on the real-world dataset demonstrate the generality and effectiveness of our framework. Moreover, the ablation experiments also confirm the contribution of each module in the framework.