摘要
针对自主定位与环境构建问题,基于视觉传感器的同时定位与地图构建(SLAM)成为现阶段研究的热点,为深入分析视觉SLAM的现状,综述其相关算法与成果。首先简要概述了视觉SLAM的概念、特点与研究意义;然后深入分析帧间估计算法,详细描述经典的帧间估计方法,其中包含基于特征点的方法、基于光流的方法和直接法,并介绍了经典视觉SLAM算法的标志性成果;之后按照有监督学习与无监督学习两种方式介绍深度学习在视觉SLAM中的研究进展,并对算法进行了归纳总结;此外分析了视觉SLAM和惯性导航的融合;最后展望了视觉SLAM的未来发展趋势。
Aiming at the problem of autonomous localization and environmental reconstruction,SLAM based on visual sensor has become a hot research topic at this stage.In order to deeply analyze the current situation of visual SLAM,this paper summarized the related algorithms and results.Firstly,it briefly summarized the concept,characteristics and research significance of visual SLAM.Then,it analyzed the visual odometry algorithm in depth,and described the classical visual odometry algorithms in detail,which included feature-based method,optical flow-based method and direct method,and introduced the landmark achievements of classical visual SLAM algorithm.Secondly,this paper introduced the research progress visual SLAM based on deep learning according to supervised learning and unsupervised learning,and summarized the algorithms.In addition,it analyzed the integration of visual SLAM and inertial navigation.Finally,it proposed the future development trend of visual SLAM.
作者
吴凡
宗艳桃
汤霞清
Wu Fan;Zong Yantao;Tang Xiaqing(Dept.of Weapons&Control,Army Academy of Armored Forces,Beijing 100072,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第8期2248-2254,共7页
Application Research of Computers
基金
武器装备军内重点科研资助项目。
关键词
SLAM
帧间估计
深度学习
神经网络
simultaneous localization and mapping(SLAM)
inter-frame estimation
deep learning
neural network
