用语义加权粒子滤波实现低内存4-自由度无人机定位，秒级响应。
SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments
- 融合无人机与卫星图像语义特征，动态加权优化粒子滤波
- 计算效率提升10倍，定位误差低于10米，3秒内完成4-DoF估计
- 适用于无卫星信号环境，适合低分辨率地图部署
基于视觉的无人机定位系统在无全球导航卫星系统（GNSS）环境下被广泛研究。然而，现有检索类方法受限于数据集规模，普遍存在实时性能不佳、环境敏感及泛化能力弱等问题，尤其在动态或时变环境中表现更差。为此，本文构建了大规模多高度飞行片段数据集（MAFS），用于变高度场景，并提出一种新型语义加权自适应粒子滤波（SWA-PF）方法。该方法通过两项创新：语义加权机制与优化的粒子滤波架构，融合无人机拍摄图像与卫星影像中的鲁棒语义特征。在自建数据集上评估表明，所提方法相较特征提取方法实现10倍计算效率提升，保持全局定位误差低于10米，并仅需可获取的低分辨率卫星地图，在数秒内完成4自由度（4-DoF）姿态估计。代码与数据集将公开于https://github.com/YuanJiayuuu/SWA-PF。
原文摘要 · Abstract (English)
Vision-based Unmanned Aerial Vehicle (UAV) localization systems have been extensively investigated for Global Navigation Satellite System (GNSS)-denied environments. However, existing retrieval-based approaches face limitations in dataset availability and persistent challenges including suboptimal real-time performance, environmental sensitivity, and limited generalization capability, particularly in dynamic or temporally varying environments. To overcome these limitations, we present a large-scale Multi-Altitude Flight Segments dataset (MAFS) for variable altitude scenarios and propose a novel Semantic-Weighted Adaptive Particle Filter (SWA-PF) method. This approach integrates robust semantic features from both UAV-captured images and satellite imagery through two key innovations: a semantic weighting mechanism and an optimized particle filtering architecture. Evaluated using our dataset, the proposed method achieves 10x computational efficiency gain over feature extraction methods, maintains global positioning errors below 10 meters, and enables rapid 4 degree of freedom (4-DoF) pose estimation within seconds using accessible low-resolution satellite maps. Code and dataset will be available at https://github.com/YuanJiayuuu/SWA-PF.
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