Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (11): 47-59.DOI: 10.3778/j.issn.1002-8331.2111-0045

• Research Hotspots and Reviews • Previous Articles     Next Articles

Research Progress and Prospect of V-SLAM Deep Learning Loop Closure Detection

GAO Gui, WU Xuanheng, WANG Zhongmei, ZHENG Liang   

  1. 1.School of Railway Transportation, Hunan University of Technology, Zhuzhou, Hunan 412000, China
    2.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610000, China
    3.The 15th Research Institute of China Electronics Technology Corporation, Beijing 100083, China
  • Online:2022-06-01 Published:2022-06-01



  1. 1.湖南工业大学 轨道交通学院,湖南 株洲 412000
    2.西南交通大学 地球科学与环境工程学院,成都 610000
    3.中国电子科技集团公司 第十五研究所,北京 100083

Abstract: Loop closure detection is an important part of simultaneous localization and mapping(SLAM), which can reduce the cumulative error caused by mobile robots in position estimation and construction of environmental maps. The traditional method adopts the characteristics of artificial design, but it is easy to be affected by factors such as illumination, weather and viewpoint change in the external environment. With the development of deep learning technology, extensive exploration has been carried out in loop closure detection, and the loop closure detection based on deep learning has strong robustness in complex environment. Through combing the background and development status of loop closure detection, this paper compares and analyzes the basic principles and algorithm characteristics of the current visual SLAM loop closure detection methods from three aspects based on deep convolution neural network, automatic encoder and semantic information, summarizes the applicable scenarios of the three methods from the visual application level, and finally, the challenges and research prospects of loop closure detection in three aspects:natural environment change, multiple moving targets and real-time dynamics are discussed.

Key words: simultaneous localization and mapping, loop closure detection, deep learning, convolutional neural network, automatic encoder, semantic information

摘要: 闭环检测是同步定位与建图(simultaneous localization and mapping,SLAM)中的一个重要组成部分,用于减少移动机器人在位置估计和构建环境地图时产生的累计误差。传统方法采用人工设计的特征,但在外界环境中容易受到光照、天气和视点变化等因素所带来的影响。随着深度学习技术的发展,闭环检测得到广泛的探索,且在复杂环境中基于深度学习的闭环检测具有较强的鲁棒性。通过梳理闭环检测的背景和发展现状,从基于深度卷积神经网络、自动编码器和语义信息三个方面,对目前视觉SLAM(visual-SLAM,V-SLAM)闭环检测方法的基本原理、算法特点进行了对比分析,并从视觉应用层面上总结了三类方法所适用的场景,最后讨论了闭环检测未来在自然环境变化、多移动目标和实时动态三个方面所存在的挑战和研究展望。

关键词: 同步定位与建图, 闭环检测, 深度学习, 卷积神经网络, 自动编码器, 语义信息