CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 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
Source PublicationYAC 2017
Issue*
Pages*
Abstract   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.
KeywordOptimization Slam Pose Graph
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19422
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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