A Review of Vision-Based UAV Localization and Navigation Methods

GU Mei-ying; LI Hang; ZHANG Jia-wei; BAI Xiao; ZHENG Jin

doi:10.12263/DZXB.20240699

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A Review of Vision-Based UAV Localization and Navigation Methods

Intelligent Vision Algorithms for Unmanned Systems | 更新时间：2025-12-08

- A Review of Vision-Based UAV Localization and Navigation Methods
- ACTA ELECTRONICA SINICA Vol. 53, Issue 3, Pages: 651-685(2025)
- 作者机构：
  
  1.北京航空航天大学计算机学院，北京 100191
  2.北京航空航天大学江西研究院，江西南昌 330000
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62276016;62372029)
- DOI：10.12263/DZXB.20240699
  CLC： TP391.4;V279
- Received：26 July 2024，
  
  Revised：2025-01-22，
  
  Published：25 March 2025
- 稿件说明：
移动端阅览
谷美颖, 李航, 张家伟, 等. 基于视觉的无人机定位与导航方法研究综述[J]. 电子学报, 2025, 53(03): 651-685.

GU Mei-ying, LI Hang, ZHANG Jia-wei, et al. A Review of Vision-Based UAV Localization and Navigation Methods[J]. Acta Electronica Sinica, 2025, 53(03): 651-685.
谷美颖, 李航, 张家伟, 等. 基于视觉的无人机定位与导航方法研究综述[J]. 电子学报, 2025, 53(03): 651-685. DOI：10.12263/DZXB.20240699

GU Mei-ying, LI Hang, ZHANG Jia-wei, et al. A Review of Vision-Based UAV Localization and Navigation Methods[J]. Acta Electronica Sinica, 2025, 53(03): 651-685. DOI：10.12263/DZXB.20240699

摘要

随着无人机（Unmanned Aerial Vehicle，UAV）成本的降低，无人机引起了越来越多的研究兴趣.其应用领域广泛，包括农业、消防、测绘、航拍以及娱乐应用.这些应用需要无人机在精准的自我定位下进行自主飞行，通常高度依赖于全球导航卫星系统（Global Navigation Satellite System，GNSS）.然而，GNSS在长距离无线电通信方面存在多种缺陷（如非视距接收、多路径效应、欺骗信号），这推动了补充或取代卫星导航新方法的发展.基于视觉的无人机定位与导航方法利用无人机搭载的视觉传感器，实现自主定位与导航，成为解决这一问题的重要途径.本文的贡献在于系统性地梳理了基于视觉的无人机定位与导航技术，全面总结了该领域的研究现状和发展趋势.首先，介绍了无人机视觉定位的方法，主要分为图像检索和图像匹配两类，并对其技术特点、适用场景以及相关数据集和评价指标进行了分析.其次，详细阐述了无人机视觉导航的方法，根据导航功能的不同分为障碍物检测与规避方法以及路径规划方法，揭示了现有技术的优势和局限.最后，进一步讨论了基于视觉的无人机定位与导航方法在公共可用数据集、硬件加速、环境复杂性、实时性要求、能源限制以及模拟器到真实世界的泛化等方面可能面临的挑战.

Abstract

As the cost of unmanned aerial vehicles (UAVs) decreases

they have attracted increasing research interest. UAVs are now widely applied in various fields

including agriculture

firefighting

surveying

aerial photography

and recreational applications. These applications require UAVs to perform autonomous flights with precise self-localization

typically relying heavily on global navigation satellite systems (GNSS). However

GNSS has multiple shortcomings related to long-distance radio communications

such as non-line-of-sight reception

multi-path effects

and spoofing. This has driven the development of new methods to supplement or replace satellite navigation. Vision-based UAV localization and navigation methods

utilizing onboard visual sensors for autonomous localization and navigation

have become crucial in addressing this issue. This review contributes to the field by systematically reviewing vision-based UAV localization and navigation technologies

providing a comprehensive summary of the current research landscape and developmental trends. First

it introduces vision-based UAV localization methods

which are categorized into image retrieval and feature matching approaches. The technical characteristics

applicable scenarios

relevant datasets

and evaluation metrics of these methods are analyzed in detail. Second

this review elaborates on vision-based UAV navigation methods

distinguishing between obstacle detection and avoidance techniques and path planning methods based on their functional objectives

while highlighting the strengths and limitations of existing technologies. Finally

this review further discusses the potential challenges faced by vision-based UAV localization and navigation methods

including the lack of publicly available datasets

the need for hardware acceleration

the complexity of operating environments

real-time processing requirements

energy constraints

and the gap between simulated and real-world environments.

关键词

Keywords

references

朱得糠 , 李东泽 , 郭鸿博 , 等 . 无人机视觉地理定位研究综述 [J ] . 导航与控制 , 2023 , 22 ( 3 ): 21 - 33, 20 .

ZHU D K , LI D Z , GUO H B , et al . A review of vision-based geolocation using UAVs [J ] . Navigation and Control , 2023 , 22 ( 3 ): 21 - 33, 20 . (in Chinese)

冷佳旭 , 莫梦竟成 , 周应华 , 等 . 无人机视角下的目标检测研究进展 [J ] . 中国图象图形学报 , 2023 , 28 ( 9 ): 2563 - 2586 .

LENG J X , MO M J C , ZHOU Y H , et al . Recent advances in drone-view object detection [J ] . Journal of Image and Graphics , 2023 , 28 ( 9 ): 2563 - 2586 . (in Chinese)

张智 , 易华挥 , 郑锦 . 聚焦小目标的航拍图像目标检测算法 [J ] . 电子学报 , 2023 , 51 ( 4 ): 944 - 955 .

ZHANG Z , YI H H , ZHENG J . Focusing on small objects detector in aerial images [J ] . Acta Electronica Sinica , 2023 , 51 ( 4 ): 944 - 955 . (in Chinese)

郑锦 , 蒋博韬 , 彭微 , 等 . LiDar点云指导下特征分布趋同与语义关联的3D目标检测 [J ] . 电子学报 , 2024 , 52 ( 5 ): 1700 - 1715 .

ZHENG J , JIANG B T , PENG W , et al . 3D object detection based on feature distribution convergence guided by LiDar point cloud and semantic association [J ] . Acta Electronica Sinica , 2024 , 52 ( 5 ): 1700 - 1715 . (in Chinese)

马宁 , 曹云峰 . 面向无人机自主着陆的视觉感知与位姿估计方法综述 [J ] . 自动化学报 , 2024 , 50 ( 7 ): 1284 - 1304 .

MA N , CAO Y F . A survey on vision-based sensing and pose estimation methods for UAV autonomous landing [J ] . Acta Automatica Sinica , 2024 , 50 ( 7 ): 1284 - 1304 . (in Chinese)

刘玄冰 , 周绍磊 , 肖支才 , 等 . 无人机避障方法研究综述 [J ] . 兵器装备工程学报 , 2022 , 43 ( 5 ): 40 - 47 .

LIU X B , ZHOU S L , XIAO Z C , et al . Review on UAV obstacle avoidance methods [J ] . Journal of Ordnance Equipment Engineering , 2022 , 43 ( 5 ): 40 - 47 . (in Chinese)

王从宝 , 张安思 , 杨磊 , 等 . 基于深度视觉的四旋翼无人机自主飞行感知和避障综述 [J ] . 无线电工程 , 2023 , 53 ( 10 ): 2233 - 2243 .

WANG C B , ZHANG A S , YANG L , et al . A review of deep vision-based autonomous flight perception and obstacle avoidance for quadrotor UAVs [J ] . Radio Engineering , 2023 , 53 ( 10 ): 2233 - 2243 . (in Chinese)

KEANE J F , CARR S S . A brief history of early unmanned aircraft [J ] . Johns Hopkins APL Technical Digest , 2013 , 32 ( 3 ): 558 - 571 .

CANDIAGO S , REMONDINO F , DE GIGLIO M , et al . Evaluating multispectral images and vegetation indices for precision farming applications from UAV images [J ] . Remote Sensing , 2015 , 7 ( 4 ): 4026 - 4047 .

QU Y Y , JIANG L , GUO X P . Moving vehicle detection with convolutional networks in UAV videos [C ] // 2016 2nd International Conference on Control, Automation and Robotics (ICCAR) . Piscataway : IEEE , 2016 : 225 - 229 .

TURNER I L , HARLEY M D , DRUMMOND C D . UAVs for coastal surveying [J ] . Coastal Engineering , 2016 , 114 : 19 - 24 .

FRASER R H , OLTHOF I , LANTZ T C , et al . UAV photogrammetry for mapping vegetation in the low-Arctic [J ] . Arctic Science , 2016 , 2 ( 3 ): 79 - 102 .

AKHLOUFI M A , COUTURIER A , CASTRO N A . Unmanned aerial vehicles for wildland fires: Sensing, perception, cooperation and assistance [J ] . Drones , 2021 , 5 ( 1 ): 15 .

SCHERER J , YAHYANEJAD S , HAYAT S , et al . An autonomous multi-UAV system for search and rescue [C ] // Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian Use . New York : ACM , 2015 : 33 - 38 .

COUTURIER A , AKHLOUFI M A . A review on absolute visual localization for UAV [J ] . Robotics and Autonomous Systems , 2021 , 135 : 103666 .

BALAMURUGAN G , VALARMATHI J , NAIDU V P S . Survey on UAV navigation in GPS denied environments [C ] // 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) . Piscataway : IEEE , 2016 : 198 - 204 .

LU Y C , XUE Z C , XIA G S , et al . A survey on vision-based UAV navigation [J ] . Geo-spatial Information Science , 2018 , 21 ( 1 ): 21 - 32 .

GYAGENDA N , HATILIMA J V , ROTH H , et al . A review of GNSS-independent UAV navigation techniques [J ] . Robotics and Autonomous Systems , 2022 , 152 : 104069 .

JUN M , ROUMELIOTIS S I , SUKHATME G S . State estimation of an autonomous helicopter using Kalman filtering [C ] // Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems . Human and Environment Friendly Robots with High Intelligence and Emotional Quotients . Piscataway : IEEE , 1999 : 1346 - 1353 .

OSHMAN Y , SHAVIV I . Optimal tuning of a Kalman filter using genetic algorithms [C ] // AIAA Guidance, Navigation, and Control Conference and Exhibit . Reston : AIAA , 2000 : 4558 .

SASIADEK J , WANG Q , JOHNSON R , et al . UAV navigation based on parallel extended Kalman filter [C ] // AIAA Guidance, Navigation, and Control Conference and Exhibit . Reston : AIAA , 2000 : 4165 .

HOFMANN-WELLENHOF B , LICHTENEGGER H , WASLE E . GNSS--Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and More [M ] . Wien : Springer , 2008 .

VAN DALEN G J , MAGREE D P , JOHNSON E N . Absolute localization using image alignment and particle filtering [C ] // AIAA Guidance, Navigation, and Control Conference . Reston : AIAA , 2016 : 0647 .

MAGREE D P , JOHNSON E N . A monocular vision-aided inertial navigation system with improved numerical stability [C ] // AIAA Guidance, Navigation, and Control Conference . Reston : AIAA , 2015 : 0097 .

YOL A , DELABARRE B , DAME A , et al . Vision-based absolute localization for unmanned aerial vehicles [C ] // 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway : IEEE , 2014 : 3429 - 3434 .

WAN X , LIU J G , YAN H S , et al . Illumination-invariant image matching for autonomous UAV localisation based on optical sensing [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2016 , 119 : 198 - 213 .

ALURI R C . Localization of UAVs Using Computer Vision in a GPS-Denied Environment [D ] . Denton : University of North Texas Libraries , 2022 .

ZHENG Z D , WEI Y C , YANG Y . University-1652: A multi-view multi-source benchmark for drone-based geo-localization [C ] // Proceedings of the 28th ACM International Conference on Multimedia . New York : ACM , 2020 : 1395 - 1403 .

WANG T Y , ZHENG Z D , YAN C G , et al . Each part matters: Local patterns facilitate cross-view geo-localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2022 , 32 ( 2 ): 867 - 879 .

DAI M , HU J H , ZHUANG J D , et al . A transformer-based feature segmentation and region alignment method for UAV-view geo-localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2022 , 32 ( 7 ): 4376 - 4389 .

LIN J L , ZHENG Z D , ZHONG Z , et al . Joint representation learning and keypoint detection for cross-view geo-localization [J ] . IEEE Transactions on Image Processing , 2022 , 31 : 3780 - 3792 .

ZHU R Z , YIN L , YANG M Z , et al . SUES-200: A multi-height multi-scene cross-view image benchmark across drone and satellite [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2023 , 33 ( 9 ): 4825 - 4839 .

SHEN T R , WEI Y M , KANG L , et al . MCCG: A ConvNeXt-based multiple-classifier method for cross-view geo-localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 ( 3 ): 1456 - 1468 .

LI S L , HU M , XIAO X W , et al . Patch similarity self-knowledge distillation for cross-view geo-localization [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2024 , 34 ( 6 ): 5091 - 5103 .

CHEN Q , WANG T Y , YANG Z H , et al . SDPL: Shifting-dense partition learning for UAV-view geo-localization [EB/OL ] . ( 2024-07-06 )[ 2025-03-31 ] . https://arxiv.org/abs/2403.04172v2 https://arxiv.org/abs/2403.04172v2 .

ZHAO H , REN K Y , YUE T Y , et al . TransFG: A cross-view geo-localization of satellite and UAVs imagery pipeline using transformer-based feature aggregation and gradient guidance [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 4700912 .

WANG T Y , ZHENG Z D , SUN Y Q , et al . Multiple-environment self-adaptive network for aerial-view geo-localization [J ] . Pattern Recognition , 2024 , 152 : 110363 .

XIAO J H , TORTEI D , ROURA E , et al . Long-range UAV thermal geo-localization with satellite imagery [C ] // 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2023 : 5820 - 5827 .

HE M F , LIU J C , GU P F , et al . Leveraging map retrieval and alignment for robust UAV visual geo-localization [J ] . IEEE Transactions on Instrumentation and Measurement , 2024 , 73 : 2523113 .

SEEMA B , KUMAR H , NAIDU V . Geo-registration of aerial images using RANSAC algorithm [J ] . NCTAESD - 2014 , 2014, 25 ( 6 ): 234 - 238 .

SARANYA K C , NAIDU V P S , SINGHAL V , et al . Application of vision based techniques for UAV position estimation [C ] // 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS) . Piscataway : IEEE , 2016 : 1 - 5 .

SHAN M , WANG F , LIN F , et al . Google map aided visual navigation for UAVs in GPS-denied environment [C ] // 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) . Piscataway : IEEE , 2015 : 114 - 119 .

CHIU H P , DAS A , MILLER P , et al . Precise vision-aided aerial navigation [C ] // 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway : IEEE , 2014 : 688 - 695 .

MANTELLI M , PITTOL D , NEULAND R , et al . A novel measurement model based on abBRIEF for global localization of a UAV over satellite images [J ] . Robotics and Autonomous Systems , 2019 , 112 : 304 - 319 .

MASSELLI A , HANTEN R , ZELL A . Localization of unmanned aerial vehicles using terrain classification from aerial images [M ] // Intelligent Autonomous Systems 13 . Cham : Springer International Publishing , 2015 : 831 - 842 .

AKHLOUFI M A , COUTURIER A . Relative visual localization (RVL) for UAV navigation [C ] // Degraded Environments: Sensing, Processing, and Display 2018 . Orlando : SPIE , 2018 : 213 - 226 .

AMER K , SAMY M , ELHAKIM R , et al . Convolutional neural network-based deep urban signatures with application to drone localization [C ] // 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) . Piscataway : IEEE , 2017 : 2138 - 2145 .

NASSAR A , AMER K , ELHAKIM R , et al . A deep CNN-based framework for enhanced aerial imagery registration with applications to UAV geolocalization [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway : IEEE , 2018 : 1 - 11 .

NASSAR A , ELHELW M . Aerial imagery registration using deep learning for UAV geolocalization [M ] // Deep Learning in Computer Vision . First edition. Boca Raton, FL : CRC Press/Taylor and Francis . 2020 : 183 - 210 .

MARCU A , COSTEA D , SLUSANSCHI E , et al . A multi-stage multi-task neural network for aerial scene interpretation and geolocalization [EB/OL ] . ( 2018-08-04 )[ 2025-03-31 ] . https://arxiv.org/abs/1804.01322v1 https://arxiv.org/abs/1804.01322v1 .

SCHLEISS M . Translating aerial images into street-map-like representations for visual self-localization of uavs [J ] . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 2019 , 42 : 575 - 580 .

GURGU M M , QUERALTA J P , WESTERLUND T . Vision-based GNSS-free localization for UAVs in the wild [C ] // 2022 7th International Conference on Mechanical Engineering and Robotics Research (ICMERR) . Piscataway : IEEE , 2022 : 7 - 12 .

LI H L , WANG J N , WEI Z W , et al . Jointly optimized global-local visual localization of UAVs [EB/OL ] . ( 2023-10-12 )[ 2025-03-31 ] . https://arxiv.org/abs/2310.08082v1 https://arxiv.org/abs/2310.08082v1 .

LUO X B , WAN X , GAO Y X , et al . JointLoc: A real-time visual localization framework for planetary UAVs based on joint relative and absolute pose estimation [EB/OL ] . ( 2024-05-13 )[ 2025-03-31 ] . https://arxiv.org/abs/2405.07429v1 https://arxiv.org/abs/2405.07429v1 .

WARREN M , GREEFF M , PATEL B , et al . There’s No place like home: Visual teach and repeat for emergency return of multirotor UAVs during GPS failure [J ] . IEEE Robotics and Automation Letters , 2019 , 4 ( 1 ): 161 - 168 .

GOFORTH H , LUCEY S . GPS-denied UAV localization using pre-existing satellite imagery [C ] // 2019 International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE , 2019 : 2974 - 2980 .

HARRIS C , STEPHENS M . A combined corner and edge detector [C ] // Proceedings ofthe Alvey Vision Conference 1988 . Plessey Research Roke Manor, U K : Alvey Vision Club , 1988 : 10 - 5244 .

ROSTEN E , DRUMMOND T . Machine Learning for High-Speed Corner Detection [M ] // Computer Vision-ECCV 2006 . Berlin : Springer Berlin Heidelberg , 2006 : 430 - 443 .

LOWE D G . Distinctive image features from scale-invariant keypoints [J ] . International Journal of Computer Vision , 2004 , 60 ( 2 ): 91 - 110 .

CALONDER M , LEPETIT V , STRECHA C , et al . BRIEF: Binary robust independent elementary features [M ] // Computer Vision-ECCV 2010 . Berlin : Springer Berlin Heidelberg , 2010 : 778 - 792 .

SARLIN P E , DETONE D , MALISIEWICZ T , et al . SuperGlue: Learning feature matching with graph neural networks [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 4938 - 4947 .

DAI M , ZHENG E H , FENG Z H , et al . Vision-based UAV self-positioning in low-altitude urban environments [J ] . IEEE Transactions on Image Processing , 2023 , 33 : 493 - 508 .

AMELI Z , AREMANDA Y , FRIESS W A , et al . Impact of UAV hardware options on bridge inspection mission capabilities [J ] . Drones , 2022 , 6 ( 3 ): 64 .

STRÜBBE S , STÜRZL W , EGELHAAF M . Insect-inspired self-motion estimation with dense flow fields: An adaptive matched filter approach [J ] . PLoS One , 2015 , 10 ( 8 ): e0128413 .

RUFFIER F , VIOLLET S , AMIC S , et al . Bio-inspired optical flow circuits for the visual guidance of micro air vehicles [C ] // Proceedings of the 2003 International Symposium on Circuits and Systems , 2003 . ISCAS' 03. Piscataway : IEEE , 2003: 3 .

HE Z H , IYER R V , CHANDLER P R . Vision-based UAV flight control and obstacle avoidance [C ] // 2006 American Control Conference . Piscataway : IEEE , 2006 : 5 .

LIN H Y , PENG X Z . Autonomous quadrotor navigation with vision based obstacle avoidance and path planning [J ] . IEEE Access , 2021 , 9 : 102450 - 102459 .

FARNEBÄCK G . Two-Frame Motion Estimation Based on Polynomial Expansion [M ] // Image Analysis . Berlin : Springer Berlin Heidelberg , 2003 : 363 - 370 .

HRABAR S . 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs [C ] // 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway : IEEE , 2008 : 807 - 814 .

MATTHIES L , BROCKERS R , KUWATA Y , et al . Stereo vision-based obstacle avoidance for micro air vehicles using disparity space [C ] // 2014 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway : IEEE , 2014 : 3242 - 3249 .

TIJMONS S , DE CROON G C H E , REMES B D W , et al . Obstacle avoidance strategy using onboard stereo vision on a flapping wing MAV [J ] . IEEE Transactions on Robotics , 2017 , 33 ( 4 ): 858 - 874 .

GRINBERG M , RUF B . UAV use case: Real-time obstacle avoidance system for unmanned aerial vehicles based on stereo vision [M ] // Towards Ubiquitous Low-power Image Processing Platforms . Cham : Springer International Publishing , 2020 : 139 - 149 .

BAI G H , XIANG X J , ZHU H Y , et al . Research on obstacles avoidance technology For UAV based on improved PTAM algorithm [C ] // 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) . Piscataway : IEEE , 2015 : 543 - 550 .

ESRAFILIAN O , TAGHIRAD H D . Autonomous flight and obstacle avoidance of a quadrotor by monocular SLAM [C ] // 2016 4th International Conference on Robotics and Mechatronics (ICROM) . Piscataway : IEEE , 2016 : 240 - 245 .

POTENA C , NARDI D , PRETTO A . Joint vision-based navigation, control and obstacle avoidance for UAVs in dynamic environments [C ] // 2019 European Conference on Mobile Robots (ECMR) . Piscataway : IEEE , 2019 : 1 - 7 .

YANG L Y , XIAO B , ZHOU Y , et al . A robust real-time vision based GPS-denied navigation system of UAV [C ] // 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) . Piscataway : IEEE , 2016 : 321 - 326 .

GOSIEWSKI Z , CIESLUK J , AMBROZIAK L . Vision-based obstacle avoidance for unmanned aerial vehicles [C ] // 2011 4th International Congress on Image and Signal Processing . Piscataway : IEEE , 2011 : 2020 - 2025 .

HAAG J , DENK W , BORST A . Fly motion vision is based on Reichardt detectors regardless of the signal-to-noise ratio [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2004 , 101 ( 46 ): 16333 - 16338 .

BERTRAND O J N , LINDEMANN J P , EGELHAAF M . A bio-inspired collision avoidance model based on spatial information derived from motion detectors leads to common routes [J ] . PLoS Computational Biology , 2015 , 11 ( 11 ): e1004339 .

MORENO-ARMENDÁRIZ M A , CALVO H . Visual SLAM and obstacle avoidance in real time for mobile robots navigation [C ] // 2014 International Conference on Mechatronics, Electronics and Automotive Engineering . Piscataway : IEEE , 2014 : 44 - 49 .

PENG X Z , LIN H Y , DAI J M . Path planning and obstacle avoidance for vision guided quadrotor UAV navigation [C ] // 2016 12th IEEE International Conference on Control and Automation (ICCA) . Piscataway : IEEE , 2016 : 984 - 989 .

SANCHEZ-RODRIGUEZ J P , ACEVES-LOPEZ A . A survey on stereo vision-based autonomous navigation for multi-rotor MUAVs [J ] . Robotica , 2018 , 36 ( 8 ): 1225 - 1243 .

SHARMA P S , CHITALIYA D N G . Obstacle avoidance using stereo vision: A survey [J ] . International Journal of Innovative Research in Computer and Communication Engineering , 2015 , 3 ( 1 ): 24 - 29 .

ZHANG J W , WANG X , BAI X , et al . Revisiting domain generalized stereo matching networks from a feature consistency perspective [C ] // 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2022 : 12991 - 13001 .

ZHANG J W , LI J H , HUANG L , et al . Robust synthetic-to-real transfer for stereo matching [C ] // 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2024 : 20247 - 20257 .

ZHANG J W , HUANG L , BAI X , et al . Exploring the usage of pre-trained features for stereo matching [J ] . International Journal of Computer Vision , 2024 , 132 ( 10 ): 4305 - 4326 .

周晓清 , 王翔 , 郑锦 , 等 . 基于自适应空间稀疏化的高效多视图立体匹配 [J ] . 电子学报 , 2023 , 51 ( 11 ): 3079 - 3091 .

ZHOU X Q , WANG X , ZHENG J , et al . Adaptive spatial sparsification for efficient multi-view stereo matching [J ] . Acta Electronica Sinica , 2023 , 51 ( 11 ): 3079 - 3091 . (in Chinese)

VACHTSEVANOS G , KIM W , AL-HASAN S , et al . Autonomous vehicles: From flight control to mission planning using fuzzy logic techniques [C ] // Proceedings of 13th International Conference on Digital Signal Processing . Piscataway : IEEE , 1997 : 977 - 981 .

ROUSE D M . Route planning using pattern classification and search techniques [C ] // Proceedings of the IEEE National Aerospace and Electronics Conference . Piscataway : IEEE , 1989 : 2015 - 2020 .

SZCZERBA R J , GALKOWSKI P , GLICKTEIN I S , et al . Robust algorithm for real-time route planning [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2000 , 36 ( 3 ): 869 - 878 .

STENTZ A . Optimal and efficient path planning for partially-known environments [C ] // Proceedings of the 1994 IEEE International Conference on Robotics and Automation . Piscataway : IEEE , 1994 : 3310 - 3317 .

BELGE E , ALTAN A , HACIOĞLU R . Metaheuristic optimization-based path planning and tracking of quadcopter for payload hold-release mission [J ] . Electronics , 2022 , 11 ( 8 ): 1208 .

ZHANG Q , MA J C , LIU Q . Path planning based quadtree representation for mobile robot using hybrid-simulated annealing and ant colony optimization algorithm [C ] // Proceedings of the 10th World Congress on Intelligent Control and Automation . Piscataway : IEEE , 2012 : 2537 - 2542 .

ANDERT F , ADOLF F . Online world modeling and path planning for an unmanned helicopter [J ] . Autonomous Robots , 2009 , 27 ( 3 ): 147 - 164 .

YAN M , AUN CHAN C , GYGAX A F , et al . Efficient generation of optimal UAV trajectories with uncertain obstacle avoidance in MEC networks [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 23 ): 38380 - 38392 .

WANG X , TAN G Z , LU F L , et al . A molecular force field-based optimal deployment algorithm for UAV swarm coverage maximization in mobile wireless sensor network [J ] . Processes , 2020 , 8 ( 3 ): 369 .

SOUZA R M J A , LIMA G V , MORAIS A S , et al . Modified artificial potential field for the path planning of aircraft swarms in three-dimensional environments [J ] . Sensors , 2022 , 22 ( 4 ): 1558 .

SHEN Y , ZHU Y L , KANG H W , et al . UAV path planning based on multi-stage constraint optimization [J ] . Drones , 2021 , 5 ( 4 ): 144 .

YUE L W , CHEN H N . Unmanned vehicle path planning using a novel ant colony algorithm [J ] . EURASIP Journal on Wireless Communications and Networking , 2019 , 2019 ( 1 ): 136 .

LIU Y X , XU W . Application of improved hopfield neural network in path planning [J ] . Journal of Physics: Conference Series , 2020 , 1544 ( 1 ): 012154 .

YANG L K , FAN S R , YU B G , et al . A coverage sampling path planning method suitable for UAV 3D space atmospheric environment detection [J ] . Atmosphere , 2022 , 13 ( 8 ): 1321 .

MITTAL M , MOHAN R , BURGARD W , et al . Vision-based autonomous UAV navigation and landing for urban search and rescue [M ] // Robotics Research . Cham : Springer International Publishing , 2022 : 575 - 592 .

BASHIR N , BOUDJIT S , DAUPHIN G , et al . An obstacle avoidance approach for UAV path planning [J ] . Simulation Modelling Practice and Theory , 2023 , 129 : 102815 .

MACIEL-PEARSON B G , MARCHEGIANI L , AKCAY S , et al . Online deep reinforcement learning for autonomous UAV navigation and exploration of outdoor environments [EB/OL ] . ( 2019-11-11 )[ 2025-03-31 ] . https://arxiv.org/abs/1912.05684v1 https://arxiv.org/abs/1912.05684v1 .

HE L , AOUF N , WHIDBORNE J F , et al . Deep reinforcement learning based local planner for UAV obstacle avoidance using demonstration data [EB/OL ] . ( 2020-08-06 )[ 2025-03-31 ] . https://arxiv.org/abs/2008.02521v1 https://arxiv.org/abs/2008.02521v1 .

THEILE M , BAYERLEIN H , NAI R , et al . UAV coverage path planning under varying power constraints using deep reinforcement learning [C ] // 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway : IEEE , 2020 : 1444 - 1449 .

THEILE M , BAYERLEIN H , NAI R , et al . UAV path planning using global and local map information with deep reinforcement learning [C ] // 2021 20th International Conference on Advanced Robotics (ICAR) . Piscataway : IEEE , 2021 : 539 - 546 .

YIN Y F , WANG Z T , ZHENG L L , et al . Autonomous UAV navigation with adaptive control based on deep reinforcement learning [J ] . Electronics , 2024 , 13 ( 13 ): 2432 .

CHHIKARA P , TEKCHANDANI R , KUMAR N , et al . DCNN-GA: A deep neural net architecture for navigation of UAV in indoor environment [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 6 ): 4448 - 4460 .

MENFOUKH K , TOUBA M M , KHENFRI F , et al . Optimized Convolutional Neural Network architecture for UAV navigation within unstructured trail [C ] // 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP) . Piscataway : IEEE , 2020 : 211 - 214 .

SILVESTRINI S , LAVAGNA M . Deep learning and artificial neural networks for spacecraft dynamics, navigation and control [J ] . Drones , 2022 , 6 ( 10 ): 270 .

TULLU A , ENDALE B , WONDOSEN A , et al . Machine learning approach to real-time 3D path planning for autonomous navigation of unmanned aerial vehicle [J ] . Applied Sciences , 2021 , 11 ( 10 ): 4706 .

ZHAO Y M , ZHANG J L , ZHANG C Y . Deep-learning based autonomous-exploration for UAV navigation [J ] . Knowledge-Based Systems , 2024 , 297 : 111925 .

LIANG H J , BAI H Y , SUN R , et al . Three-dimensional path planning based on DEM [C ] // 2017 36th Chinese Control Conference (CCC) . Piscataway : IEEE , 2017 : 5980 - 5987 .

LI Z T , ZHAO J N , ZHOU X , et al . RTSDM: A real-time semantic dense mapping system for UAVs [J ] . Machines , 2022 , 10 ( 4 ): 285 .

CHEN S Y , ZHOU W F , YANG A S , et al . An end-to-end UAV simulation platform for visual SLAM and navigation [J ] . Aerospace , 2022 , 9 ( 2 ): 48 .

LU S , DING B X , LI Y M . Minimum-jerk trajectory planning pertaining to a translational 3-degree-of-freedom parallel manipulator through piecewise quintic polynomials interpolation [J ] . Advances in Mechanical Engineering , 2020 , 12 ( 3 ): 168781402091366 .

YU J M , SUN H , SUN J Q . Improved twin delayed deep deterministic policy gradient algorithm based real-time trajectory planning for parafoil under complicated constraints [J ] . Applied Sciences , 2022 , 12 ( 16 ): 8189 .

XU W J , YAO Y X , CAO J Q , et al . UAV-VisLoc: A large-scale dataset for UAV visual localization [EB/OL ] . ( 2024-05-20 )[ 2025-03-31 ] . https://arxiv.org/abs/2405.11936v1 https://arxiv.org/abs/2405.11936v1 .

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