Comparison of methods to efficient graph SLAM under general optimization framework

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	Comparison of methods to efficient graph SLAM under general optimization framework
	Haoran Li1,2 ; Qichao Zhang1,2 ; Dongbin Zhao1,2
	2017
会议录名称	YAC 2017
期号	*
页码	*
摘要	Simultaneous Localization and Mapping(SLAM) algorithms can infer the robot's trajectory as well as the map under unknown environment. Robust and time-efficient optimization methods are important requirements for SLAM. There are many algorithms designed for the graph optimization. However, it is hard to select an appropriate algorithm and corresponding software library, due to the difficulty of evaluating algorithms' adaptabilities under various situations. In this paper, we summarize these algorithms under general optimization framework, conduct several sets of experiments to compare these algorithms in three software libraries, and give some suggestions to choose algorithms.
关键词	Optimization Slam Pose Graph
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19422
专题	多模态人工智能系统全国重点实验室_深度强化学习
作者单位	1.The state Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2.University of Chinese Academy of Sciences, Beijing, 100049, China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Haoran Li,Qichao Zhang,Dongbin Zhao. Comparison of methods to efficient graph SLAM under general optimization framework[C],2017:*.

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