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
专题复杂系统管理与控制国家重点实验室_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
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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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