SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments

• URL: http://arxiv.org/abs/2509.13795v2
• Date: Sat, 20 Sep 2025 08:43:46 GMT
• Title: SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments
• Authors: Jiayu Yuan, Ming Dai, Enhui Zheng, Chao Su, Nanxing Chen, Qiming Hu, Shibo Zhu, Yibin Cao,
• Abstract summary: Vision-based Unmanned Aerial Vehicle (UAV) localization systems have been extensively investigated for Global Navigation Satellite System (GNSS)-denied environments.<n>We present a large-scale Multi-Altitude Flight Segments dataset (MAFS) for variable altitude scenarios.<n>We propose a novel Semantic-Weighted Adaptive Particle Filter (SWA-PF) method to overcome these limitations.
• Score: 8.46731803518948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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