视觉同步定位与建图系统中回环检测研究进展  

Research Progress of Loop-closure Detection in Visual SLAM System

作　　者：苏子旸 张策[1] 张茹 张展[3] 张婧 吕为工 SU Zi-yang;ZHANG Ce;ZHANG Ru;ZHANG Zhan;ZHANG Jing;LYU Wei-gong(School of Computer Science and Technology,Harbin Institute of Technology(Weihai),Weihai 264209,China;School of Management,Harbin University of Commerce,Harbin 150076,China;School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China)

机构地区：[1]哈尔滨工业大学(威海)计算机科学与技术学院,山东威海264209 [2]哈尔滨商业大学管理学院,黑龙江哈尔滨150076 [3]哈尔滨工业大学计算机科学与技术学院,黑龙江哈尔滨150001

出　　处：《计算机技术与发展》2023年第4期1-8,共8页Computer Technology and Development

基　　金：国家自然科学基金(61473097);山东省重点研发计划项目(GG201703130116,GG201703040002);2021年度山东省自然科学基金面上项目(ZR2021MF067)。

摘　　要：回环检测又被称为位置识别,是“同步定位与建图”(Simultaneous Localization And Mapping,SLAM)系统中根据图像间的相似度判断运动轨迹是否经过重复地点(即存在回环)的功能,起到阶段性消除累积误差的作用。聚焦于视觉SLAM系统这一特定主题下的回环检测主题进行研究,概述了SLAM系统的基本功能与基本组成,分析了视觉SLAM系统中回环检测的原理与工作流程、前置问题、评测指标。剖析了回环检测发展过程中产生的系列方法,归类了视觉SLAM系统中回环检测存在的两类算法——基于词袋模型的回环检测算法和基于深度学习的回环检测算法,并对这两类算法的原理及优缺点进行了深入分析与总结。分析表明,基于词袋模型的回环检测算法因其在实时性上的优势仍处于主流,基于深度学习的回环检测算法具有较好的准确率和鲁棒性,但受限于设备对计算资源的分配,这一类做法如何应用于注重实时性的视觉SLAM系统仍是亟待解决的问题。最后,对回环检测面临的挑战和存在的问题进行了分析与展望。Loop-closure detection,also known as position recognition,is a function in the"Simultaneous Localization And Mapping(SLAM)system to judge whether the motion track passes through repeated places(i.e.there is loop-closure)according to the similarity between images,and plays a role in eliminating cumulative errors in stages.Focused on the research of loop-closure detection under the specific theme of visual SLAM system,we summarize the basic functions and basic components of SLAM system,analyze the principle and workflow of loop-closure detection,pre-problems and evaluation indicators.We analyze a series of methods generated in the development of loop-closure detection,classify two types of loop-closure detection algorithms in SLAM system,which are loop-closure detection algorithm based on bag-of-words model and loop-closure detection algorithm based on deep learning,and focus on the principle,advantages and disadvantages of these two types of algorithms.The analysis shows that the loop-closure detection algorithm based on bag-of-words model is still in the mainstream because of its real-time advantage.The loop-closure detection algorithm based on deep learning has excellent accuracy and robustness,but limited by the allocation of computing resources by devices,how to apply this kind of method to the visual SLAM system that pays attention to real-time is still an urgent problem to be solved.Finally,the challenges and problems of loop-closure detection are analyzed and prospected.

关 键 词：同步定位与建图 回环检测 位置识别 词袋模型 深度学习 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]