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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.Abstract: Drones play an important role in vicinagearth security, post-disaster rescue, geological survey, agricultural plant protection, and other fields due to their high flexibility, and they receive increasing attention. As a key technology in drones, positioning and navigation are crucial for whether the drone can successfully perform tasks. Currently, the main positioning and navigation algorithms include the global navigation satellite system, inertial positioning, and scene matching positioning and navigation. Among them, the scene matching positioning and navigation method uses computer vision technology to encode the digital features of aerial images collected during the flight of drones. Then, by constructing a similarity measurement and retrieval model, it measures the similarity between the aerial image features and the pre-obtained remote sensing map library features to complete the scene matching. Finally, based on the matching results of drone aerial images and remote sensing satellite maps, it obtains the corresponding geographic position information and updates it as the positioning result of the drone. The scene matching positioning and navigation method eliminates the dependence of the positioning system on positioning signals and realizes the autonomy of drone flight positioning. This paper follows the feature extraction methods in the scene matching algorithm and outlines the development process of scene matching based on template matching, manual feature-based, and metric learning-based approaches while summarizing the key problems in the positioning and navigation methods of scene matching. Finally, this paper summarizes the urgent problems that need to be solved in drone scene matching localization methods based on the current development status of scene matching algorithms.
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Key words:
- Vicinagearth security /
- drone /
- visual geo-localization /
- scene matching /
- metric learning /
- multi-view changes
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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
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 强 强 较为成熟 绝对定位 GPS 较高 弱 强 成熟 绝对定位 SMS 较低 强 强 亟待研究 
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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%
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