电子测量与仪器学报

杨雪梅,李帅永.移动机器人视觉 SLAM 回环检测原理、现状及趋势[J].电子测量与仪器学报,2022,36(8):1-12

移动机器人视觉 SLAM 回环检测原理、现状及趋势

Principle, current situation and trend of visual SLAMloop

DOI：

英文关键词:visual SLAM loop closure detection bag-of-words deep learning semantic segmentation performance evaluation

基金项目:国家重点研发计划项目（2019YFB2005900，2017YFB1303704）、国家自然科学基金(617033066)、重庆市基础研究与前沿探索项目(cstc2018jcyjAX0536)、重庆市技术创新与应用发展专项项目(cstc2019jscx fxydX0042，cstc2019jscx zdztzxX0053)资助

作者	单位
杨雪梅	1.重庆邮电大学工业物联网与网络化控制教育部重点实验室
李帅永	1.重庆邮电大学工业物联网与网络化控制教育部重点实验室

Author	Institution
Yang Xuemei	1.Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing University of Posts and Telecommunications
Li Shuaiyong	1.Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education,Chongqing University of Posts and Telecommunications

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中文摘要:

近年来,视觉 SLAM 以结构简单、成本低、可结合语义信息等优势得到广泛关注。回环检测在其中发挥着重要的作用。根据获得的回环信息,视觉 SLAM 后端优化算法便可以根据回环约束对位姿进行优化,消除移动机器人在长时间的工作下产生的累积误差,实现精确的长期定位,从而构建全局一致的运动轨迹和地图。首先介绍视觉 SLAM 中回环检测原理及作用,从特征提取、相似度判断、实验评估几个方面对传统词袋模型进行深入分析,并概述目前基于词袋模型和概率的改进算法,对比总结基于深度学习的回环检测方法,简单概述结合语义信息的回环检测方法,最后对回环检测技术目前存在的问题以及未来的发展趋势进行总结与展望。

英文摘要:

In recent years, visual SLAM has attracted wide attention due to its advantages of simple structure, low cost and ability to integrate semantic information. Loop closure detection plays an important role on it. According to the loop information obtained, the visual SLAM back-end optimization algorithm can optimize the pose according to the loop constraint, eliminate the cumulative error generated after long-term work, and achieve accurate long-term positioning, in order to build a globally consistent motion track and map. First introduce the principle and function of loop closure detection in visual SLAM, then conduct an in-depth analysis of the traditional bag-of-words model from feature extraction, similarity judgment, and experimental evaluation, and outlines several improved algorithms based on the bag-of-words model and probability, and summarizes several loop closure detection methods based on deep learning, briefly summarize the loop closure detection methods combined with semantic information, and finally summarize and prospect the current problems and future development of loop closure detection.

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