Review of vision-based Simultaneous Localization and Mapping

被引：0
|
作者：
Li, Ang [1 ,2 ]
Ruan, Xiaogang [1 ,2 ]
Huang, Jing [1 ,2 ]
Zhu, Xiaoqing [1 ,2 ]
Wang, Fei [1 ,2 ]
机构：
[1] Beijing Univ Technol, Coll Elect & Control Engn, Beijing, Peoples R China
[2] Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2019 IEEE 3RD INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2019) | 2019年
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
visual simultaneous localization and mapping; robot; visual odometry; graph optimization; loop closure detection; MONOCULAR SLAM; WORDS;
D O I：
10.1109/itnec.2019.8729285
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Vision-based simultaneous localization and mapping (VSLAM) which uses visual sensor to make a robot locate itself in an unknown environment while simultaneously construct a map of the environment. With the continuous development of computer vision and robotics, VSLAM has become a supporting technology for popular fields such as unmanned aerial vehicle, virtual reality and unmanned driving. In this paper, the classical framework of visual SLAM is introduced briefly. On this basis, the key technologies and latest research progress of VSLAM from indirect and direct methods are surveyed. Then the research progress of deep learning techniques applied to VSLAM is reviewed. Finally, the development tendency of VSLAM is discussed.
引用
收藏
页码：117 / 123
页数：7
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