测绘学报 ›› 2018, Vol. 47 ›› Issue (6): 770-779.doi: 10.11947/j.AGCS.2018.20170652

• 高精度高效率数字摄影测量 • 上一篇    下一篇

视觉SLAM技术的进展与应用

邸凯昌1, 万文辉1, 赵红颖2, 刘召芹1, 王润之1, 张飞舟2   

  1. 1. 中国科学院遥感与数字地球研究所遥感科学国家重点实验室, 北京 100101;
    2. 北京大学遥感与地理信息系统研究所, 北京 100871
  • 收稿日期:2017-11-20 修回日期:2018-03-28 出版日期:2018-06-20 发布日期:2018-06-21
  • 通讯作者: 张飞舟 E-mail:zhangfz@pku.edu.cn
  • 作者简介:邸凯昌(1967-),男,博士,研究员,博士生导师,主要研究方向为行星遥感制图与导航定位。E-mail:dikc@radi.ac.cn
  • 基金资助:
    国家重点研发计划(2016YFB0502102);国家自然科学基金面上项目(41471388)

Progress and Applications of Visual SLAM

DI Kaichang1, WAN Wenhui1, ZHAO Hongying2, LIU Zhaoqin1, WANG Runzhi1, ZHANG Feizhou2   

  1. 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
    2. Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China
  • Received:2017-11-20 Revised:2018-03-28 Online:2018-06-20 Published:2018-06-21
  • Supported by:
    The National Key Research and Development Program of China (No.2016YFB0502102);The National Natural Science Foundation of China (No.41471388)

摘要: 视觉SLAM技术依靠体积小、功耗低、信息获取丰富的视觉传感器,为未知环境下的机器人提供环境地图及自身在地图中的定位结果,对机器人自动化、智能化应用有着重要意义。本文介绍了视觉SLAM方法的关键技术,总结了目前视觉SLAM的研究现状,分析了当前视觉SLAM研究的主要趋势,最后讨论了视觉SLAM技术在深空、室内等受限环境下的应用现状与前景。

关键词: 视觉SLAM, 特征提取, 卡尔曼滤波, 图优化, 闭环检测

Abstract: Visual SLAM provides mapping and self-localization results of a robot in an unknown environment based on visual sensor,which has the advantages of small volume,low power consumption,and richness of information acquisition.Visual SLAM is critical and significant in supporting of robots’ automated and intelligent applications.This paper presents the key techniques of visual SLAM,summarizes the current status of visual SLAM research,and analyzes the new trends of visual SLAM research and development.Finally,status and prospect of visual SLAM application in restricted environments,such as deep space,indoor scene and so on,are discussed.

Key words: visual SLAM, feature extraction, Kalman filter, graph based optimization, loop closure detection

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