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景象匹配无人机视觉定位

袁媛 孙柏 刘赶超

袁媛, 孙柏, 刘赶超. 景象匹配无人机视觉定位. 自动化学报, 2025, 51(1): 1−25 doi: 10.16383/j.aas.c230778
引用本文: 袁媛, 孙柏, 刘赶超. 景象匹配无人机视觉定位. 自动化学报, 2025, 51(1): 1−25 doi: 10.16383/j.aas.c230778
Yuan Yuan, Sun Bo, Liu Gan-Chao. Drone-based scene matcing visual geo-localization. Acta Automatica Sinica, 2025, 51(1): 1−25 doi: 10.16383/j.aas.c230778
Citation: Yuan Yuan, Sun Bo, Liu Gan-Chao. Drone-based scene matcing visual geo-localization. Acta Automatica Sinica, 2025, 51(1): 1−25 doi: 10.16383/j.aas.c230778

景象匹配无人机视觉定位

doi: 10.16383/j.aas.c230778 cstr: 32138.14.j.aas.c230778
基金项目: 国家自然科学基金 (62273282, 62201471) 资助
详细信息
    作者简介:

    袁媛:西北工业大学光电与人工智能研究院教授. 主要研究方向为跨域遥感和群体智能决策. E-mail: y.yuan@nwpu.edu.cn

    孙柏:西北工业大学博士研究生. 主要研究方向为度量学习和视觉定位. E-mail: sunbo_bosun@mail.nwpu.edu.cn

    刘赶超:西北工业大学光电与人工智能研究院副教授. 主要研究方向为跨域遥感和视觉定位. 本文通信作者. E-mail: liuganchao@nwpu.edu.cn

Drone-based Scene Matcing Visual Geo-localization

Funds: Supported by National Natural Science Foundation of China (62273282, 62201471)
More Information
    Author Bio:

    YUAN Yuan Professor at the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University. Her research interest covers cross-domain remote sensing and swarm intelligent decision-making

    SUN Bo Ph.D. candidate at the School of Computer, Northwestern Polytechnical University. His research interest covers metric learning and UAV geo-localization

    LIU Gan-Chao Associate Professor at the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University. His research interest covers cross-domain remote sensing and visual positioning. Corresponding author of this paper

  • 摘要: 无人机因其极高的灵活性, 在临地安防、灾后救援、地质勘测、农业植保等领域发挥着重要作用, 因此受到越来越多的关注. 定位导航作为无人机中的关键技术, 对于无人机能否顺利执行任务至关重要. 当前主要的定位导航算法包括全球卫星定位系统、惯性定位以及景象匹配定位导航等. 其中, 景象匹配定位导航方法利用计算机视觉技术, 对无人机飞行时采集的航空影像进行数字化特征编码. 随后, 通过构建相似性度量与检索模型, 将航空影像特征与预先获取的遥感地图库特征进行相似性度量, 从而完成景象匹配. 最后, 根据无人机航空影像与遥感卫星地图的匹配结果, 获取相应的地理位置信息, 并将其更新为无人机的定位结果. 景象匹配定位导航方法摆脱了定位系统对定位信号的依赖, 实现了无人机飞行定位的自主化. 鉴于此, 以景象匹配算法中的特征提取方式为线索, 分别针对基于模板匹配、基于手工特征以及基于度量学习的景象匹配, 梳理其发展过程, 并总结了景象匹配定位导航方法中的关键问题. 最后, 针对景象匹配算法的发展现状, 总结了无人机景象匹配定位方法中亟待解决的问题.
  • 图  1  无人机景象匹配视觉定位算法流程图

    Fig.  1  Flow chart of drone scene matching visual geo-localization algorithm

    图  2  基于模板匹配的定位算法示意图

    Fig.  2  Schematic diagram of location algorithm based on template matching

    图  3  SIFT特征点匹配算法示意图

    Fig.  3  Schematic diagram of feature point matching algorithm

    图  4  多源影像差异

    Fig.  4  Multi-source image differences

    图  5  多源多视角无人机影像差异

    Fig.  5  Multi-view drone image differences

    表  1  定位算法对比结果

    Table  1  Comparison results of localization algorithms

    分类 方法 精度 抗干扰性 实时性 发展现状
    相对定位 INS 短时精度高 较为成熟
    绝对定位 GPS 较高 成熟
    绝对定位 SMS 较低 亟待研究
    下载: 导出CSV

    表  2  代表性方法汇总

    Table  2  Summary of representative methods

    方法 算法分类 实现方式 地图数据来源 无人机数据来源 航拍影像尺寸(像素) 定位/匹配精度
    Dalen[16] 模板匹配 NCC 谷歌地图 真实拍摄 12.5 m
    Yol[18] 模板匹配 MI 谷歌地图 真实拍摄 10.36 m
    Fan[19] 模板匹配 NCC 谷歌地图 谷歌地球
    Levin[20] 模板匹配 CC DEM数据 DEM 数据
    Lin[55] 模板匹配 MI 谷歌地图 谷歌地球 720$ \times $480 1.91 m
    Huang[56] 模板匹配 MI 谷歌地图 真实拍摄 640$ \times $480
    Wan[57] 模板匹配 PC 卫星数据 真实拍摄 3 648$ \times $2 736 3 m
    Patel[58] 模板匹配 NID 谷歌地图 真实拍摄 560$ \times $315
    Shan[23] 特征点法 HOG 谷歌地图 真实拍摄 850$ \times $500 3 m
    Masselli[27] 特征点法 ORB 谷歌地图 真实拍摄 640$ \times $480 9.5 m
    Chiu[30] 特征点法 2D-3D点 DARPA 真实拍摄 13.98 m
    Mantelli[31] 特征点法 abBREIF 谷歌地图 真实拍摄 17.78 m
    Shan[33] 特征点法 MSD+ LSS 谷歌地图 真实拍摄
    Woo[34] 特征点法 角点 谷歌地图 真实拍摄 96%
    Pluckter[59] 特征点法 ORB 谷歌地图 真实拍摄
    Pan[61] 特征点法 SIFT 谷歌地图 真实拍摄 586$ \times $452 5.2 pix
    Couturier[81] 特征点法 ORB 真实拍摄
    Couturier[82] 特征点法 SURF 真实拍摄 1 920$ \times $1 080 5.2 m
    Goforth[42] 深度学习 VGG16 谷歌地图 真实拍摄 4 608$ \times $2 592 25 m
    Amer[44] 深度学习 VGG16 谷歌地图 Bing地图 500$ \times $500 91.2%
    Nassar[47] 深度学习 U-Net 谷歌地图+Bing地图 谷歌地球
    Marcu[60] 深度学习 MSMT OpenStreetMap 1 500$ \times $1 500
    Schleiss[52] 深度学习 cGAN+SSD 真实拍摄
    Zheng[64] 深度学习 ResNet 谷歌地图 谷歌地球 512$ \times $512 70.54%
    Workman[65] 深度学习 谷歌地图 谷歌街景/Flickr
    Hays[66] 深度学习 网络爬取 Flickr
    Weyand[62] 深度学习 LSTM 谷歌地图 谷歌地球
    Wu[63] 深度学习 Lucas-Kanade 真实拍摄 仿真数据 5 632$ \times $5 376 9.8 m
    Li[96] 深度学习 channel attention 真实拍摄 真实拍摄 44.7%
    Jouko[97] 深度学习 正交投影 谷歌地图 真实拍摄 4 800$ \times $2 987 11.2 m
    Wen[98] 深度学习 SiamRPN 谷歌地图 真实拍摄
    Wang[78] 深度学习 LPN 谷歌地图 谷歌地球 512$ \times $512 79.14%
    Dai[99] 深度学习 FSRA 谷歌地图 谷歌地球 512$ \times $512 87.32%
    Tian[100] 深度学习 PCL 谷歌地图 谷歌地球 512$ \times $512 87.53%
    Zhu[101] 深度学习 SUES-200 谷歌地图 真实拍摄 512$ \times $512 80.67%
    下载: 导出CSV
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  • 收稿日期:  2023-12-19
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