电子学报2025，Vol.53Issue(11)：4171-4198,28.DOI:10.12263/DZXB.20250775
基于视觉与深度学习的无人机自主着陆场景感知方法研究进展
Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning
摘要
Abstract
With the vigorous development of unmanned aerial vehicle(UAV)technology,its applications in various fields such as military defense,intelligent transportation,facility inspection,disaster relief,and agricultural management have become increasingly widespread,becoming the core driving force for the development of the low-altitude economy.Autonomous landing,as one of the core and key technologies of UAVs,directly determines the safety and reliability of UAV operations.Especially in emergency scenarios such as low battery power,deteriorating weather conditions,or commu-nication disruptions,it can effectively prevent equipment damage and accidents,and is a crucial step towards achieving full automation of UAVs.Scene perception technology based on vision and deep learning,with its powerful feature learning and pattern recognition capabilities,has broken through the limitations of traditional technologies such as GPS(Global Position-ing System)and LiDAR(Light Detection And Ranging)in complex environments,bringing a brand-new solution to the field of UAV autonomous landing.This paper systematically reviews the scene perception methods for UAV autonomous landing based on vision and deep learning.Firstly,it elaborates on the application background and significance of deep learning in UAV autonomous landing,and sorts out the technological evolution from traditional sensor-driven to intelligent perception.Then,it analyzes in detail the features and technical challenges of different scenarios:static platform landing fo-cuses on three types of scenarios-landing marks,runway detection,and ground guidance,with the core demand being to improve landing accuracy and reliability;dynamic platform landing covers land-based vehicles,ships at sea,and other mo-bile platforms,and needs to focus on solving problems of motion tracking and interference suppression;special scenario landing faces multiple challenges such as obstacle occlusion,signal interference,and extreme weather in complex environ-ments like mountains,forests,and urban canyons.This paper deeply explores the core technical system,including the princi-ples and applications of key technologies such as object detection,semantic segmentation,pose estimation,optical flow pre-diction,and 3D reconstruction.At the same time,it analyzes the application effects and performance of feature extraction optimization,semantic understanding enhancement,and scene adaptation strategies.Finally,it summarizes the challenges faced in this field,such as insufficient adaptability to complex environments,computational resource constraints,data de-pendence and annotation difficulties,and looks forward to future research directions.It points out that multi-source sensor data fusion can enhance the perception ability in complex environments,developing lightweight models can adapt to the re-source limitations of UAVs,and strengthening the combination of simulation and real scenarios can improve the generaliza-tion ability of models.Through systematic summary and analysis,this paper comprehensively presents the current technical status and development trends in this field,providing valuable reference and guidance for further research and engineering applications of UAV autonomous landing technology.关键词
自主着陆/无人机/深度学习/计算机视觉/目标检测/语义分割Key words
autonomous landing/UAV/deep learning/computer vision/object detection/semantic segmentation分类
信息技术与安全科学引用本文复制引用
王中天,吴一全..基于视觉与深度学习的无人机自主着陆场景感知方法研究进展[J].电子学报,2025,53(11):4171-4198,28.基金项目
国家自然科学基金(No.61573183) National Natural Science Foundation of China(No.61573183) （No.61573183）
