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<p>Autonomous localization methods for Unmanned Aerial Vehicles (UAVs) have significant application potential in complex environments. This paper presents a comprehensive survey of UAV localization techniques, focusing on both pure vision-based and sensor-assisted approaches. For pure vision-based localization, the survey emphasizes key technologies for feature descriptor generation, advancements in similarity measurement criteria, and optimized computational strategies. The impact of these technologies on improving computational efficiency and localization accuracy. In the context of sensor-assisted multi-source UAV localization, the applications of filtering-based fusion, optimization-based fusion, and deep learning-based fusion methods are discussed. A detailed analysis demonstrates the advantages of multi-modal data fusion in improving robustness and accuracy. Despite significant progress in localization accuracy and adaptability to complex environments, challenges remain in adapting to low-texture environments, optimizing fusion strategies, and addressing computational resource limitations. Finally, the paper discusses future directions for the research and implementation of UAV autonomous localization methods.</p>

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A survey of sensors based autonomous unmanned aerial vehicle (UAV) localization techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Haiqiao Liu,
  • Qing Long,
  • Bing Yi,
  • Wen Jiang

摘要

Autonomous localization methods for Unmanned Aerial Vehicles (UAVs) have significant application potential in complex environments. This paper presents a comprehensive survey of UAV localization techniques, focusing on both pure vision-based and sensor-assisted approaches. For pure vision-based localization, the survey emphasizes key technologies for feature descriptor generation, advancements in similarity measurement criteria, and optimized computational strategies. The impact of these technologies on improving computational efficiency and localization accuracy. In the context of sensor-assisted multi-source UAV localization, the applications of filtering-based fusion, optimization-based fusion, and deep learning-based fusion methods are discussed. A detailed analysis demonstrates the advantages of multi-modal data fusion in improving robustness and accuracy. Despite significant progress in localization accuracy and adaptability to complex environments, challenges remain in adapting to low-texture environments, optimizing fusion strategies, and addressing computational resource limitations. Finally, the paper discusses future directions for the research and implementation of UAV autonomous localization methods.