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

王中天; 吴一全

doi:10.12263/DZXB.20250775

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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

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- 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
- 电子学报 2025年53卷第11期页码：4171-4198
- 作者机构：
  
  南京航空航天大学电子信息工程学院，江苏南京 211106
- 作者简介：
  
  [ "王中天男，2002年生，安徽蚌埠人.现为南京航空航天大学电子信息学院博士研究生.主要研究方向为模式识别、计算机视觉、图像处理等.E-mail: zt_wang01@163.com" ]
  [ "吴一全男，1963年生，江苏启东人.现为南京航空航天大学教授、博士生导师.主要研究方向为视觉检测与图像测量、遥感图像处理与理解、红外目标检测与识别、视频处理与智能分析等.E-mail: nuaaimage@163.com" ]
- 基金信息：
  
  国家自然科学基金(61573183)
- DOI：10.12263/DZXB.20250775
  中图分类号： TP391.41;V279
- 收稿：2025-09-05，
  
  录用：2025-11-18，
  
  纸质出版：2025-11-25
- 稿件说明：
移动端阅览
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.

WANG Zhong-tian, WU Yi-quan. Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning[J]. Acta Electronica Sinica, 2025, 53(11): 4171-4198.
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. DOI：10.12263/DZXB.20250775

WANG Zhong-tian, WU Yi-quan. Research Progress of UAV Autonomous Landing Scene Perception Methods Based on Vision and Deep Learning[J]. Acta Electronica Sinica, 2025, 53(11): 4171-4198. DOI：10.12263/DZXB.20250775

摘要

随着无人机（Unmanned Aerial Vehicle，UAV）技术的蓬勃发展，其在军事国防、智能交通、设施巡检、灾害救援、农业管理等众多领域的应用日益广泛，成为低空经济发展的核心驱动力.自主着陆作为无人机关键核心技术之一，直接决定了无人机作业的安全性与可靠性，尤其在电池电量不足、气象条件恶化或通信中断等紧急场景下，能有效避免设备损坏与事故发生，是实现无人机完全自动化的关键环节.基于视觉与深度学习的场景感知技术，凭借强大的特征学习和模式识别能力，突破了传统依赖GPS（Global Positioning System）、激光雷达（Light Detection And Ranging，LiDAR）等技术在复杂环境中的局限性，为无人机自主着陆领域带来了全新的解决方案.本文系统综述了基于视觉与深度学习的无人机自主着陆场景感知方法.首先阐述了深度学习在无人机自主着陆中的应用背景和重要性，梳理了从传统传感器驱动到智能感知的技术演进历程.随后详细剖析了不同场景的特征与技术挑战：静态平台着陆聚焦降落标识、跑道检测、地基引导三类场景，核心需求是提升着陆精度与准确率；动态平台着陆涵盖车载陆地、舰艇海上及其他移动平台，需重点解决运动跟踪与干扰抑制问题；特殊场景着陆则面临山区、森林、城市峡谷等复杂环境中的障碍物遮挡、信号干扰、极端气象等多重挑战.本文深入探讨了核心技术体系，包括目标检测、语义分割、姿态估计、光流预测、三维重建等关键技术的原理与应用.同时分析了特征提取优化、语义理解增强及场景适配策略的应用效果与性能表现.最后总结了该领域面临的复杂环境适应性不足、计算资源约束、数据依赖与标注难题等挑战，并对未来研究方向进行了展望.指出通过多源传感器数据融合可提升复杂环境感知能力，开发轻量化模型能适配无人机资源限制，加强仿真与真实场景结合可提高模型泛化能力.本文通过系统地总结与分析，全面呈现了该领域的技术现状与发展脉络，为无人机自主着陆技术的进一步研究与工程应用提供了宝贵的参考和指导.

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 communication 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 Positioning 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 focuses 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 mobile 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 environments like mountains

forests

and urban canyons. This paper deeply explores the core technical system

including the principles and applications of key technologies such as object detection

semantic segmentation

pose estimation

optical flow prediction

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 dependence 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 resource limitations of UAVs

and strengthening the combination of simulation and real scenarios can improve the generalization 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

Keywords

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