Comparison of Methods to Efficient Graph SLAM Under General Optimization Framework

被引：0

作者：

Li, Haoran ^{[1
,2
]}

Zhang, Qichao ^{[1
,2
]}

Zhao, Dongbin ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

来源：

2017 32ND YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2017年

基金：

中国国家自然科学基金;

关键词：

SLAM; pose graph; optimization; SIMULTANEOUS LOCALIZATION; ALIGNMENT;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：321 / 326

页数：6

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