计算机工程与应用2025，Vol.61Issue(10)：66-78,13.DOI:10.3778/j.issn.1002-8331.2409-0215
基于深度强化学习的视觉导航方法综述
Review on Visual Navigation Methods Based on Deep Reinforcement Learning
摘要
Abstract
Traditional visual navigation methods are highly dependent on high-precision maps,and suffer from inevitable issues of error accumulation.These issues result in suboptimal performance when the traditional navigation methods are used in complex dynamic environments.Visual navigation methods based on deep reinforcement learning,by simulating human's innate navigation abilities,can achieve safe navigation to specified targets in an end-to-end manner by directly using visual information.This kind of methods has emerged as a promising research hotspot in the field of visual navi-gation.In order to explore the latest research issues in the direction of deep reinforcement learning navigation,and intui-tively analyze the latest methods,this paper introduces the background and theories of deep reinforcement learning.Then,it summarizes and analyzes crucial methods from three aspects:data utilization,policy optimization,and scene generaliza-tion,focusing on the major research issues in this direction over the past five years.Finally,this paper offers reflections on the current research status and future research directions of such methods,aiming to provide a reference for future investigations in related areas while summarizing the latest research trends.关键词
视觉导航/深度强化学习/样本效率/泛化/计算机视觉Key words
visual navigation/deep reinforcement learning/sample efficiency/generalization/computer vision分类
天文与地球科学引用本文复制引用
高宇宁,王安成,赵华凯,罗豪龙,杨子迪,李建胜..基于深度强化学习的视觉导航方法综述[J].计算机工程与应用,2025,61(10):66-78,13.基金项目
智慧中原地理信息技术河南省协同创新中心和时空感知与智能处理自然资源部重点实验室基金(232104). （232104）
